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"IPU available: False, using: 0 IPUs\n",
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"\u001b[32m2023-07-13 14:02:28.065\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
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"-------------------\n",
"\u001b[0m\n",
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"-------------------\n",
"\u001b[0m\n",
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"\tabout = test set\n",
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"\tnum_nodes_total = 5503\n",
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"\tmean_num_edges_per_graph = 64.83977900552486\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:28.066\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mget_max_num_nodes_datamodule\u001b[0m:\u001b[36m556\u001b[0m - \u001b[1mMax num nodes being calcuated train\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:28.067\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mget_max_num_nodes_datamodule\u001b[0m:\u001b[36m565\u001b[0m - \u001b[1mMax num nodes being calcuated val\u001b[0m\n",
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"\u001b[32m2023-07-13 14:02:28.069\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = training set\n",
"\tnum_graphs_total = 634\n",
"\tnum_nodes_total = 18351\n",
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"\tmean_num_edges_per_graph = 62.24921135646688\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:28.069\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1169\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = validation set\n",
"\tnum_graphs_total = 91\n",
"\tnum_nodes_total = 2791\n",
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"\tmin_num_nodes_per_graph = 13\n",
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"\tmean_num_edges_per_graph = 66.3076923076923\n",
"-------------------\n",
"\u001b[0m\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"----------------------------------------------------\n",
"0 | model | FullGraphMultiTaskNetwork | 111 K \n",
"----------------------------------------------------\n",
"111 K Trainable params\n",
"0 Non-trainable params\n",
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"`Trainer.fit` stopped: `max_epochs=10` reached.\n",
"\u001b[32m2023-07-13 14:02:49.408\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
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"-------------------\n",
"\u001b[0m\n",
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"\tmean_num_edges_per_graph = 66.3076923076923\n",
"-------------------\n",
"\u001b[0m\n",
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"-------------------\n",
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"-------------------\n",
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"\u001b[32m2023-07-13 14:02:50.421\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = training set\n",
"\tnum_graphs_total = 403\n",
"\tnum_nodes_total = 9065\n",
"\tmax_num_nodes_per_graph = 101\n",
"\tmin_num_nodes_per_graph = 7\n",
"\tstd_num_nodes_per_graph = 8.379156239363269\n",
"\tmean_num_nodes_per_graph = 22.49379652605459\n",
"\tnum_edges_total = 19420\n",
"\tmax_num_edges_per_graph = 220\n",
"\tmin_num_edges_per_graph = 14\n",
"\tstd_num_edges_per_graph = 18.678530787820403\n",
"\tmean_num_edges_per_graph = 48.188585607940446\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.422\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1169\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = validation set\n",
"\tnum_graphs_total = 58\n",
"\tnum_nodes_total = 1431\n",
"\tmax_num_nodes_per_graph = 67\n",
"\tmin_num_nodes_per_graph = 9\n",
"\tstd_num_nodes_per_graph = 9.610317607573146\n",
"\tmean_num_nodes_per_graph = 24.67241379310345\n",
"\tnum_edges_total = 3102\n",
"\tmax_num_edges_per_graph = 144\n",
"\tmin_num_edges_per_graph = 18\n",
"\tstd_num_edges_per_graph = 21.938976007372833\n",
"\tmean_num_edges_per_graph = 53.48275862068966\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.423\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1186\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = test set\n",
"\tnum_graphs_total = 117\n",
"\tnum_nodes_total = 2716\n",
"\tmax_num_nodes_per_graph = 66\n",
"\tmin_num_nodes_per_graph = 3\n",
"\tstd_num_nodes_per_graph = 9.966464331904731\n",
"\tmean_num_nodes_per_graph = 23.213675213675213\n",
"\tnum_edges_total = 5790\n",
"\tmax_num_edges_per_graph = 136\n",
"\tmin_num_edges_per_graph = 4\n",
"\tstd_num_edges_per_graph = 22.25440142180863\n",
"\tmean_num_edges_per_graph = 49.48717948717949\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.423\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mget_max_num_nodes_datamodule\u001b[0m:\u001b[36m556\u001b[0m - \u001b[1mMax num nodes being calcuated train\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.423\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mget_max_num_nodes_datamodule\u001b[0m:\u001b[36m565\u001b[0m - \u001b[1mMax num nodes being calcuated val\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.424\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mprepare_data\u001b[0m:\u001b[36m943\u001b[0m - \u001b[1mData is already prepared. Skipping the preparation\u001b[0m\n",
"You are using a CUDA device ('NVIDIA GeForce RTX 3070') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
"\u001b[32m2023-07-13 14:02:50.426\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = training set\n",
"\tnum_graphs_total = 403\n",
"\tnum_nodes_total = 9065\n",
"\tmax_num_nodes_per_graph = 101\n",
"\tmin_num_nodes_per_graph = 7\n",
"\tstd_num_nodes_per_graph = 8.379156239363269\n",
"\tmean_num_nodes_per_graph = 22.49379652605459\n",
"\tnum_edges_total = 19420\n",
"\tmax_num_edges_per_graph = 220\n",
"\tmin_num_edges_per_graph = 14\n",
"\tstd_num_edges_per_graph = 18.678530787820403\n",
"\tmean_num_edges_per_graph = 48.188585607940446\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:02:50.426\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1169\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = validation set\n",
"\tnum_graphs_total = 58\n",
"\tnum_nodes_total = 1431\n",
"\tmax_num_nodes_per_graph = 67\n",
"\tmin_num_nodes_per_graph = 9\n",
"\tstd_num_nodes_per_graph = 9.610317607573146\n",
"\tmean_num_nodes_per_graph = 24.67241379310345\n",
"\tnum_edges_total = 3102\n",
"\tmax_num_edges_per_graph = 144\n",
"\tmin_num_edges_per_graph = 18\n",
"\tstd_num_edges_per_graph = 21.938976007372833\n",
"\tmean_num_edges_per_graph = 53.48275862068966\n",
"-------------------\n",
"\u001b[0m\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"----------------------------------------------------\n",
"0 | model | FullGraphMultiTaskNetwork | 111 K \n",
"----------------------------------------------------\n",
"111 K Trainable params\n",
"0 Non-trainable params\n",
"111 K Total params\n",
"0.448 Total estimated model params size (MB)\n"
]
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"\u001b[32m2023-07-13 14:02:51.236\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgraphium.trainer.predictor_summaries\u001b[0m:\u001b[36mget_metrics_logs\u001b[0m:\u001b[36m273\u001b[0m - \u001b[33m\u001b[1mError for metric graph_hia_hou/mcc/val. NaN is returned. Exception: stack expects a non-empty TensorList\u001b[0m\n"
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{
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"Training: 0it [00:00, ?it/s]"
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"\u001b[32m2023-07-13 14:02:52.078\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgraphium.trainer.predictor_summaries\u001b[0m:\u001b[36mget_metrics_logs\u001b[0m:\u001b[36m273\u001b[0m - \u001b[33m\u001b[1mError for metric graph_hia_hou/mcc/train. NaN is returned. Exception: stack expects a non-empty TensorList\u001b[0m\n",
"`Trainer.fit` stopped: `max_epochs=10` reached.\n",
"\u001b[32m2023-07-13 14:03:06.289\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1168\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = training set\n",
"\tnum_graphs_total = 403\n",
"\tnum_nodes_total = 9065\n",
"\tmax_num_nodes_per_graph = 101\n",
"\tmin_num_nodes_per_graph = 7\n",
"\tstd_num_nodes_per_graph = 8.379156239363269\n",
"\tmean_num_nodes_per_graph = 22.49379652605459\n",
"\tnum_edges_total = 19420\n",
"\tmax_num_edges_per_graph = 220\n",
"\tmin_num_edges_per_graph = 14\n",
"\tstd_num_edges_per_graph = 18.678530787820403\n",
"\tmean_num_edges_per_graph = 48.188585607940446\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:03:06.290\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1169\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = validation set\n",
"\tnum_graphs_total = 58\n",
"\tnum_nodes_total = 1431\n",
"\tmax_num_nodes_per_graph = 67\n",
"\tmin_num_nodes_per_graph = 9\n",
"\tstd_num_nodes_per_graph = 9.610317607573146\n",
"\tmean_num_nodes_per_graph = 24.67241379310345\n",
"\tnum_edges_total = 3102\n",
"\tmax_num_edges_per_graph = 144\n",
"\tmin_num_edges_per_graph = 18\n",
"\tstd_num_edges_per_graph = 21.938976007372833\n",
"\tmean_num_edges_per_graph = 53.48275862068966\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:03:06.290\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1186\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = test set\n",
"\tnum_graphs_total = 117\n",
"\tnum_nodes_total = 2716\n",
"\tmax_num_nodes_per_graph = 66\n",
"\tmin_num_nodes_per_graph = 3\n",
"\tstd_num_nodes_per_graph = 9.966464331904731\n",
"\tmean_num_nodes_per_graph = 23.213675213675213\n",
"\tnum_edges_total = 5790\n",
"\tmax_num_edges_per_graph = 136\n",
"\tmin_num_edges_per_graph = 4\n",
"\tstd_num_edges_per_graph = 22.25440142180863\n",
"\tmean_num_edges_per_graph = 49.48717948717949\n",
"-------------------\n",
"\u001b[0m\n",
"\u001b[32m2023-07-13 14:03:06.291\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mget_max_num_nodes_datamodule\u001b[0m:\u001b[36m574\u001b[0m - \u001b[1mMax num nodes being calcuated test\u001b[0m\n",
"\u001b[32m2023-07-13 14:03:06.291\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36mprepare_data\u001b[0m:\u001b[36m943\u001b[0m - \u001b[1mData is already prepared. Skipping the preparation\u001b[0m\n",
"You are using a CUDA device ('NVIDIA GeForce RTX 3070') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
"\u001b[32m2023-07-13 14:03:06.292\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgraphium.data.datamodule\u001b[0m:\u001b[36msetup\u001b[0m:\u001b[36m1186\u001b[0m - \u001b[1m-------------------\n",
"MultitaskDataset\n",
"\tabout = test set\n",
"\tnum_graphs_total = 117\n",
"\tnum_nodes_total = 2716\n",
"\tmax_num_nodes_per_graph = 66\n",
"\tmin_num_nodes_per_graph = 3\n",
"\tstd_num_nodes_per_graph = 9.966464331904731\n",
"\tmean_num_nodes_per_graph = 23.213675213675213\n",
"\tnum_edges_total = 5790\n",
"\tmax_num_edges_per_graph = 136\n",
"\tmin_num_edges_per_graph = 4\n",
"\tstd_num_edges_per_graph = 22.25440142180863\n",
"\tmean_num_edges_per_graph = 49.48717948717949\n",
"-------------------\n",
"\u001b[0m\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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"\u001b[32m2023-07-13 14:03:07.087\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgraphium.trainer.predictor_summaries\u001b[0m:\u001b[36mget_metrics_logs\u001b[0m:\u001b[36m273\u001b[0m - \u001b[33m\u001b[1mError for metric graph_hia_hou/mcc/test. NaN is returned. Exception: stack expects a non-empty TensorList\u001b[0m\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Test metric ┃ DataLoader 0 ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ gpu_allocated_GB │ 0.0018811225891113281 │\n",
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"│ graph_hia_hou/accuracy/test │ 0.7692307829856873 │\n",
"│ graph_hia_hou/auprc/test │ 0.8519759774208069 │\n",
"│ graph_hia_hou/auroc/test │ 0.7008230686187744 │\n",
"│ graph_hia_hou/loss/test │ 0.5804430842399597 │\n",
"│ graph_hia_hou/mcc/test │ nan │\n",
"│ graph_hia_hou/mean_pred/test │ 0.8799146413803101 │\n",
"│ graph_hia_hou/mean_target/test │ 0.7692307829856873 │\n",
"│ graph_hia_hou/median_pred/test │ 0.8844738006591797 │\n",
"│ graph_hia_hou/median_target/test │ 1.0 │\n",
"│ graph_hia_hou/std_pred/test │ 0.010796700604259968 │\n",
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"│\u001b[36m \u001b[0m\u001b[36m graph_hia_hou/std_pred/test \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.010796700604259968 \u001b[0m\u001b[35m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36m graph_hia_hou/std_target/test \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.42313718795776367 \u001b[0m\u001b[35m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36m loss/test \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.5804430842399597 \u001b[0m\u001b[35m \u001b[0m│\n",
"└──────────────────────────────────┴──────────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results = {}\n",
"\n",
"for name in benchmarks: \n",
" \n",
" # Run the training-testing loop\n",
" cfg = filter_cfg_based_on_benchmark_name(config, name)\n",
" benchmark_results = training_testing_loop(cfg)\n",
" \n",
" # Extract the main metric from the config\n",
" metric = cfg[\"predictor\"][\"metrics_on_progress_bar\"][name][0]\n",
" key = f\"graph_{name}/{metric}/test\"\n",
" results[f\"{name}/{metric}\"] = benchmark_results[0][key]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c2cea93b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"caco2_wang/mae: 2.312683582305908\n",
"hia_hou/auroc: 0.7008230686187744\n",
"\n"
]
}
],
"source": [
"print(omegaconf.OmegaConf.to_yaml(results))"
]
},
{
"cell_type": "markdown",
"id": "2150be90",
"metadata": {},
"source": [
"The End. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: notebooks/running-fingerprints-from-pretrained-model.ipynb
================================================
{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"orig_nbformat": 4,
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.8 64-bit ('graphium': conda)"
},
"interpreter": {
"hash": "f4a99d018a205fcbcc0480c84566beaebcb91b08d0414b39a842df533e2a1d25"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"source": [
"# General imports\r\n",
"import yaml\r\n",
"import numpy as np\r\n",
"import torch\r\n",
"import fsspec\r\n",
"\r\n",
"# Current project imports\r\n",
"import graphium\r\n",
"from graphium.config._loader import load_datamodule, load_trainer\r\n",
"from graphium.trainer.predictor import PredictorModule\r\n"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Using backend: pytorch\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"# Path containing the model and its configurations\r\n",
"MODEL_PATH = \"https://storage.valencelabs.com/graphium/pretrained-models/graphium-zinc-micro-dummy-test\"\r\n",
"MODEL_FILE = f\"{MODEL_PATH}/model.ckpt\"\r\n",
"CONFIG_FILE = f\"{MODEL_PATH}/configs.yaml\"\r\n",
"\r\n",
"# Path containing the SMILES data to infer\r\n",
"SMILES_DF_PATH = f\"https://storage.valencelabs.com/graphium/datasets/graphium-zinc-bench-gnn/smiles_score.csv.gz\"\r\n",
"SMILES_COL = \"SMILES\"\r\n",
"\r\n",
"# Number of layers to drop when inferring the fingerprints\r\n",
"NUM_LAYERS_TO_DROP = 1"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"# Load the configuration file of the trained model\r\n",
"with fsspec.open(CONFIG_FILE, \"rb\") as f:\r\n",
" cfg = yaml.safe_load(f)\r\n",
"\r\n",
"# Overwrite configurations of the datamodule\r\n",
"cfg[\"datamodule\"][\"module_type\"] = \"DGLFromSmilesDataModule\"\r\n",
"args = cfg[\"datamodule\"][\"args\"]\r\n",
"cfg[\"datamodule\"][\"args\"] = {\r\n",
" \"df_path\": SMILES_DF_PATH,\r\n",
" \"smiles_col\": SMILES_COL,\r\n",
" \"label_cols\": [],\r\n",
" \"featurization\": args[\"featurization\"],\r\n",
" }\r\n",
"\r\n",
"# Load and initialize the dataset\r\n",
"datamodule = load_datamodule(cfg)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"# Load the model, drop the layers, and load the trainer\r\n",
"predictor = PredictorModule.load_from_checkpoint(MODEL_FILE)\r\n",
"predictor.model.drop_graph_output_nn_layers(num_layers_to_drop=NUM_LAYERS_TO_DROP)\r\n",
"trainer = load_trainer(cfg)\r\n",
"\r\n",
"predictor"
],
"outputs": [
{
"output_type": "error",
"ename": "AssertionError",
"evalue": "",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Load the model, drop the layers, and load the trainer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mpredictor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPredictorModule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_from_checkpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMODEL_FILE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpredictor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_graph_output_nn_layers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_layers_to_drop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNUM_LAYERS_TO_DROP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_trainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\domin\\documents\\gits\\graphium_windows\\graphium\\nn\\architectures.py\u001b[0m in \u001b[0;36mdrop_graph_output_nn_layers\u001b[1;34m(self, num_layers_to_drop)\u001b[0m\n\u001b[0;32m 867\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mnum_layers_to_drop\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 869\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mnum_layers_to_drop\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_output_nn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnum_layers_to_drop\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Run the model prediction, and concatenate the batched results\r\n",
"preds = trainer.predict(model=predictor, datamodule=datamodule)\r\n",
"if isinstance(preds[0], torch.Tensor):\r\n",
" preds = [p.detach().cpu().numpy() for p in preds]\r\n",
"preds = np.concatenate(preds, axis=0)\r\n",
"\r\n",
"preds"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"preds.shape"
],
"outputs": [],
"metadata": {}
}
]
}
================================================
FILE: notebooks/running-model-from-config.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using backend: pytorch\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"# General imports\n",
"import os\n",
"from os.path import dirname, abspath\n",
"import yaml\n",
"from copy import deepcopy\n",
"from omegaconf import DictConfig, OmegaConf\n",
"\n",
"\n",
"# Current project imports\n",
"import graphium\n",
"from graphium.utils.config_loader import (\n",
" config_load_constants,\n",
" config_load_dataset,\n",
" config_load_architecture,\n",
" config_load_metrics,\n",
" config_load_predictor,\n",
" config_load_training,\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the config file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dominique/anaconda3/envs/graphium_env/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /opt/conda/conda-bld/pytorch_1607370156314/work/c10/cuda/CUDAFunctions.cpp:100.)\n",
" return torch._C._cuda_getDeviceCount() > 0\n"
]
}
],
"source": [
"# Set up the working directory\n",
"MAIN_DIR = dirname(dirname(abspath(graphium.__file__)))\n",
"os.chdir(MAIN_DIR)\n",
"\n",
"with open(os.path.join(MAIN_DIR, \"expts/config_micro_ZINC.yaml\"), \"r\") as f:\n",
" cfg = yaml.safe_load(f)\n",
"\n",
"cfg = dict(deepcopy(cfg))\n",
"\n",
"# Get the general parameters and generate the train/val/test datasets\n",
"data_device, model_device, dtype, exp_name, seed, raise_train_error = config_load_constants(\n",
" **cfg[\"constants\"], main_dir=MAIN_DIR\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"datamodule:\n",
" name: DGLFromSmilesDataModule\n",
"len: 1000\n",
"batch_size_training: 128\n",
"batch_size_inference: 256\n",
"num_node_feats: 55\n",
"num_edge_feats: 13\n",
"collate_fn: graphium_collate_fn\n",
"featurization:\n",
" atom_property_list_onehot:\n",
" - atomic-number\n",
" - valence\n",
" atom_property_list_float:\n",
" - mass\n",
" - electronegativity\n",
" - in-ring\n",
" edge_property_list:\n",
" - bond-type-onehot\n",
" - stereo\n",
" - in-ring\n",
" add_self_loop: false\n",
" explicit_H: false\n",
" use_bonds_weights: false\n",
" \n",
"\n"
]
}
],
"source": [
"\n",
"# Load and initialize the dataset\n",
"datamodule = config_load_dataset(**cfg[\"datasets\"], main_dir=MAIN_DIR,)\n",
"print(\"\\ndatamodule:\\n\", datamodule, \"\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"model:\n",
" DGL_GNN\n",
"---------\n",
" pre-trans-NN(depth=1, ResidualConnectionNone)\n",
" [FCLayer[55 -> 32] -> Linear(32)\n",
" \n",
" main-GNN(depth=4, ResidualConnectionSimple(skip_steps=1))\n",
" PNAMessagePassingLayer[32 -> 32 -> 32 -> 32 -> 32]\n",
" -> Pooling(sum) -> FCLayer(32 -> 32, activation=None)\n",
" \n",
" post-trans-NN(depth=2, ResidualConnectionNone)\n",
" [FCLayer[32 -> 32 -> 32] -> Linear(32) \n",
"\n"
]
}
],
"source": [
"# Initialize the network\n",
"model = config_load_architecture(\n",
" **cfg[\"architecture\"],\n",
" in_dim_nodes=datamodule.num_node_feats_with_positional_encoding,\n",
" in_dim_edges=datamodule.num_edge_feats\n",
")\n",
"\n",
"print(\"\\nmodel:\\n\", model, \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mae': mean_absolute_error, 'pearsonr': pearsonr, 'f1 > 5': f1(>5), 'precision > 5': precision(>5), 'auroc > 5': auroc(>5)}\n"
]
}
],
"source": [
"metrics = config_load_metrics(cfg[\"metrics\"])\n",
"print(metrics)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: profiling/configs_profiling.yaml
================================================
constants:
seed: &seed 42
raise_train_error: true # Whether the code should raise an error if it crashes during training
datamodule:
module_type: "DGLFromSmilesDataModule"
args:
df_path: https://storage.valencelabs.com/graphium/datasets/graphium-zinc-bench-gnn/smiles_score.csv.gz
processed_graph_data_path: null
label_cols: ['score']
smiles_col: SMILES
# Featurization
featurization_n_jobs: -1
featurization_progress: True
featurization:
atom_property_list_onehot: [atomic-number, valence]
atom_property_list_float: [mass, electronegativity]
edge_property_list: [bond-type-onehot, stereo]
add_self_loop: False
explicit_H: False
use_bonds_weights: False
pos_encoding_as_features: &pos_enc
pos_type: laplacian_eigvec
num_pos: 3
normalization: "none"
disconnected_comp: True
pos_encoding_as_directions: *pos_enc
on_error: warn
# Train, val, test parameters
split_val: null
split_test: null
split_seed: *seed
splits_path: https://storage.valencelabs.com/graphium/datasets/graphium-zinc-bench-gnn/indexes_train_val_test.csv.gz
batch_size_training: 128
batch_size_inference: 256
# Data loading
num_workers: 0
pin_memory: False
persistent_workers: False # Keep True on Windows if running multiple workers, false for single worker
architecture:
model_type: fulldglnetwork
pre_nn: # Set as null to avoid a pre-nn network
out_dim: &middle_dim 200
hidden_dims: *middle_dim
depth: 0
activation: relu
last_activation: none
dropout: &dropout 0.
normalization: &normalization "batch_norm"
last_normalization: *normalization
residual_type: none
pre_nn_edges: # Set as null to avoid a pre-nn network
out_dim: 16
hidden_dims: 16
depth: 2
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: none
gnn: # Set as null to avoid a post-nn network
out_dim: *middle_dim
hidden_dims: *middle_dim
depth: 6
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: simple
pooling: 'sum'
virtual_node: none
layer_type: 'dgl:dgn-msgpass'
layer_kwargs:
# num_heads: 3
aggregators: [mean, max, dir1/dx_abs, dir1/smooth]
scalers: [identity, amplification, attenuation]
graph_output_nn:
out_dim: 1
hidden_dims: *middle_dim
depth: 0
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
predictor:
metrics_on_progress_bar: ["mae", "pearsonr", "f1 < 0", "precision < 0"]
loss_fun: mse
random_seed: *seed
optim_kwargs:
lr: 1.e-3
weight_decay: 3.e-6
lr_reduce_on_plateau_kwargs:
factor: 0.5
patience: 50
min_lr: 1.e-5
scheduler_kwargs:
monitor: &monitor loss/val
frequency: 1
target_nan_mask: 0 # null: no mask, 0: 0 mask, ignore: ignore nan values from loss
metrics:
- name: mae
metric: mae
threshold_kwargs: null
- name: pearsonr
metric: pearsonr
threshold_kwargs: null
- name: f1 < 0
metric: f1
target_to_int: True
num_classes: 2
average: micro
threshold_kwargs: &threshold_0
operator: lower
threshold: 0
th_on_preds: True
th_on_target: True
- name: f1 < -1
metric: f1
target_to_int: True
num_classes: 2
average: micro
threshold_kwargs:
operator: lower
threshold: -1
th_on_preds: True
th_on_target: True
- name: precision < 0
metric: precision
average: micro
threshold_kwargs: *threshold_0
trainer:
logger:
save_dir: logs/ZINC_bench_gnn
early_stopping:
monitor: *monitor
min_delta: 0
patience: 200
mode: &mode min
model_checkpoint:
dirpath: models_checkpoints/ZINC_bench_gnn/
filename: "model"
monitor: *monitor
mode: *mode
save_top_k: 1
every_n_val_epochs: 1
trainer:
max_epochs: 2000
min_epochs: 100
gpus: 1
================================================
FILE: profiling/profile_mol_to_graph.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
from tqdm import tqdm
import datamol as dm
import pickle
from graphium.data.utils import load_micro_zinc
from graphium.features.featurizer import mol_to_pyggraph, mol_to_adj_and_features, mol_to_graph_dict
# Check out this profiling tool: https://kirillstrelkov.medium.com/python-profiling-with-vscode-3a17c0407833
def main():
df = load_micro_zinc()
smiles = df["SMILES"].values.tolist()
smiles = smiles * 10
print("Num smiles: ", len(smiles))
pos_enc = {
"pos_type": "laplacian_eigvec",
"num_pos": 3,
"normalization": "none",
"disconnected_comp": True,
}
atom_property_list_float1 = [
"mass",
"in-ring",
"hybridization",
"chirality",
"aromatic",
"degree",
"formal-charge",
"single-bond",
"double-bond",
"radical-electron",
]
atom_property_list_float2 = ["electronegativity", "vdw-radius", "covalent-radius", "metal"]
featurizer = {
"atom_property_list_onehot": ["atomic-number", "valence"],
"atom_property_list_float": atom_property_list_float1 + atom_property_list_float2,
"edge_property_list": [
"bond-type-onehot",
"bond-type-float",
"stereo",
"in-ring",
"conjugated",
"estimated-bond-length",
],
"add_self_loop": False,
"explicit_H": False,
"use_bonds_weights": False,
"pos_encoding_as_features": pos_enc,
# "on_error": "raise",
}
graphs = []
for s in tqdm(smiles):
mol = dm.to_mol(
s
) # Doesn't need `ordered=True` because this is just to test the speed of the featurizer
graphs.append(mol_to_graph_dict(mol, **featurizer))
print(graphs[0])
if __name__ == "__main__":
main()
================================================
FILE: profiling/profile_one_of_k_encoding.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
from tqdm import tqdm
from graphium.utils.tensor import one_of_k_encoding
def main():
CLASSES = ["AA", "BB", "CC", "DD", "EE"]
CHOICES = CLASSES + ["FF"]
for ii in tqdm(range(500000)):
for choice in CHOICES:
one_of_k_encoding(choice, CLASSES)
print("DONE :)")
if __name__ == "__main__":
main()
================================================
FILE: profiling/profile_predictor.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
from tqdm import tqdm
import os
from time import time
import yaml
import fsspec
from graphium.config._loader import (
load_datamodule,
load_metrics,
load_trainer,
load_predictor,
load_architecture,
)
from lightning import Trainer
def main():
CONFIG_PATH = "expts/config_micro-PCBA.yaml"
# DATA_PATH = "https://storage.valencelabs.com/graphium/datasets/graphium-zinc-bench-gnn/smiles_score.csv.gz"
with fsspec.open(CONFIG_PATH, "r") as f:
cfg = yaml.safe_load(f)
cfg["datamodule"]["args"][
"processed_graph_data_path"
] = "graphium/data/cache/profiling/predictor_data.cache"
# cfg["datamodule"]["args"]["df_path"] = DATA_PATH
cfg["trainer"]["trainer"]["max_epochs"] = 5
cfg["trainer"]["trainer"]["min_epochs"] = 5
datamodule = load_datamodule(cfg)
# Initialize the network
model_class, model_kwargs = load_architecture(
cfg,
in_dim_nodes=datamodule.num_node_feats_with_positional_encoding,
in_dim_edges=datamodule.num_edge_feats,
)
metrics = load_metrics(cfg)
print(metrics)
predictor = load_predictor(cfg, model_class, model_kwargs, metrics)
trainer = load_trainer(cfg)
trainer.fit(model=predictor, datamodule=datamodule)
print("Done :)")
if __name__ == "__main__":
main()
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools", "setuptools-scm"]
build-backend = "setuptools.build_meta"
[project]
name = "graphium"
description = "Graphium: Scaling molecular GNNs to infinity."
dynamic = ["version"]
authors = [
{ name = "Dominique Beaini", email = "dominique@valencediscovery.com" },
]
readme = "README.md"
requires-python = ">=3.8"
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audience :: Healthcare Industry",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"Topic :: Scientific/Engineering :: Information Analysis",
"Topic :: Scientific/Engineering :: Medical Science Apps.",
"Natural Language :: English",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
]
dependencies = [
"typer",
"loguru",
"omegaconf >=2.0.0",
"tqdm",
"platformdirs",
# scientific
"numpy",
"scipy >=1.4",
"pandas >=1.0",
"scikit-learn",
"fastparquet",
# viz
"matplotlib >=3.0.1",
"seaborn",
# cloud IO
"fsspec >=2021.6",
"s3fs >=2021.6",
"gcsfs >=2021.6",
# ML packages
"lightning >=2.0",
"torchmetrics >=0.7.0,<0.11",
"ogb",
"torch-geometric >=2.0",
"wandb",
"mup",
"torch_sparse >=0.6",
"torch_cluster >=1.5",
"torch_scatter >=2.0",
# chemistry
"datamol >=0.10",
]
[project.scripts]
graphium = "graphium.cli.main:app"
graphium-train = "graphium.cli.train_finetune_test:cli"
[project.urls]
Website = "https://graphium.datamol.io/"
"Source Code" = "https://github.com/datamol-io/graphium"
"Bug Tracker" = "https://github.com/datamol-io/graphium/issues"
Documentation = "https://graphium-docs.datamol.io/"
[tool.setuptools]
include-package-data = true
[tool.setuptools_scm]
fallback_version = "dev"
[tool.setuptools.packages.find]
where = ["."]
include = ["graphium", "graphium.*", "expts", "expts.*"]
exclude = []
namespaces = true
[tool.setuptools.package-data]
"graphium" = ["**/*.csv", "**/*.parquet", "**/*.yaml"]
[tool.black]
line-length = 110
target-version = ['py310', 'py311']
include = '\.pyi?$'
[tool.pytest.ini_options]
minversion = "6.0"
addopts = "--verbose --durations=10 -n 1 --cov=graphium --cov-fail-under=60 --cov-report xml --cov-report term"
testpaths = ["tests"]
filterwarnings = [
"ignore::DeprecationWarning:ray.*:",
"ignore::DeprecationWarning:numba.*:",
"ignore::DeprecationWarning:lightning_fabric.*:",
"ignore::DeprecationWarning:pytorch_lightning.*:",
"ignore::DeprecationWarning:pkg_resources.*:",
]
markers = [
"ipu: marks tests that are specific to the IPU (deselect with '-m \"not ipu\"')",
]
[tool.coverage.run]
source = ["graphium/"]
disable_warnings = ["no-data-collected"]
data_file = ".coverage/coverage"
[tool.coverage.report]
omit = ["graphium/__init__.py", "graphium/_version.py"]
[tool.coverage.xml]
output = "coverage.xml"
================================================
FILE: scripts/balance_params_and_train.sh
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
#!/bin/bash
set -e
old_dim=0
num_params=10000000
rel_error=0.005
rel_step=0.5
out=$(graphium params balance "${@}" "$num_params" "$rel_error" "$rel_step" "$old_dim")
read -r new_dim new_edge_dim rel_step stop <<< "$out"
while true; do
tmp_dim=$new_dim
out=$(graphium params balance "${@}" constants.gnn_dim="$new_dim" constants.gnn_edge_dim="$new_edge_dim" "$num_params" "$rel_error" "$rel_step" "$old_dim")
read -r new_dim new_edge_dim rel_step stop <<< "$out"
old_dim=$tmp_dim
[[ $stop == "true" ]] && break
done
graphium-train "${@}" constants.gnn_dim="$new_dim" constants.gnn_edge_dim="$new_edge_dim"
================================================
FILE: scripts/convert_yml.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Convert the dependencies from conda's `env.yml` to pip `requirements.txt`
"""
import ruamel.yaml
yaml = ruamel.yaml.YAML()
data = yaml.load(open("env.yml"))
requirements = []
for dep in data["dependencies"]:
if isinstance(dep, str):
outputs = dep.split("=")
if len(outputs) == 1:
package = outputs[0]
requirements.append(package)
elif len(outputs) == 2:
package, package_version = outputs[0], outputs[1]
requirements.append(package + "==" + package_version)
elif len(outputs) == 3:
package, package_version, python_version = outputs[0], outputs[1], outputs[2]
requirements.append(package + "==" + package_version)
elif isinstance(dep, dict):
for preq in dep.get("pip", []):
requirements.append(preq)
with open("requirements.txt", "w") as fp:
for requirement in requirements:
print(requirement, file=fp)
================================================
FILE: scripts/ipu_start.sh
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Start the ipu environment and SDK
"""
source /opt/gc/sdk-3.0.0+1128/poplar-ubuntu_20_04-3.0.0+5468-0379b9a65d/enable.sh
source /opt/gc/sdk-3.0.0+1128/popart-ubuntu_20_04-3.0.0+5468-0379b9a65d/enable.sh
source ~/.venv/graphium_ipu/bin/activate # Change to your path
export VISUAL=vim
export EDITOR="$VISUAL"
================================================
FILE: scripts/ipu_venv.sh
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Create the pip environment for IPU
"""
## Uncomment this to create the folder for the environment
# mkdir ~/.venv # Create the folder for the environment
# python3 -m venv ~/.venv/graphium_ipu # Create the environment
# source ~/.venv/graphium_ipu/bin/activate # Activate the environment
# Installing the dependencies for the IPU environment
pip install torch==1.10+cpu torchvision==0.11+cpu torchaudio==0.10 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
pip install dgl dglgo -f https://data.dgl.ai/wheels/repo.html
pip install /opt/gc/sdk-3.0.0+1128/poptorch-3.0.0+84519_672c9cbc7f_ubuntu_20_04-cp38-cp38-linux_x86_64.whl
pip install -r requirements.txt
pip install -e .
================================================
FILE: scripts/scale_mpnn.sh
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
#!/bin/bash
graphium-train \
--config-path=/home/frederik_valencediscovery_com/projects/graphium_hps/expts/configs/ \
--config-name=config_mpnn_base.yaml \
constants.max_epochs=100 \
trainer.model_checkpoint.dirpath=model_checkpoints/large-dataset/scale_mpnn/ \
+architecture.mup_scale_factor=2 +architecture.mup_base_path=mup/mpnn_base/base_shapes.yaml \
datamodule.args.batch_size_inference=1024 datamodule.args.batch_size_training=1024 +trainer.trainer.accumulate_grad_batches=2 \
================================================
FILE: tests/.gitignore
================================================
temp_cache_*
================================================
FILE: tests/__init__.py
================================================
================================================
FILE: tests/config_test_ipu_dataloader.yaml
================================================
# Testing the multitask pipeline with the QM9 dataset on IPU, by splitting it up into three tasks: homo, alpha and cv.
constants:
name: &name test_ipu #qm9_full
seed: &seed 42
raise_train_error: true # Whether the code should raise an error if it crashes during training
accelerator:
type: ipu # cpu or ipu or gpu
config_override:
datamodule:
args:
ipu_dataloader_training_opts:
mode: async
max_num_nodes_per_graph: 20 # train max nodes: 20, max_edges: 54
max_num_edges_per_graph: 60
ipu_dataloader_inference_opts:
mode: async
max_num_nodes_per_graph: 16 # valid max nodes: 51, max_edges: 118
max_num_edges_per_graph: 120
# Data handling-related
batch_size_training: 6
batch_size_inference: 6
trainer:
trainer:
precision: 16
accumulate_grad_batches: 4
ipu_config:
- deviceIterations(2)
datamodule:
module_type: "MultitaskFromSmilesDataModule"
args: # Matches that in the test_multitask_datamodule.py case.
task_specific_args: # To be replaced by a new class "DatasetParams"
homo:
df: null
df_path: &df_path https://storage.valencelabs.com/datasets-public-research/PCQM4M/cxsmiles/pcqm4mv2-2k-lumo-alpha.csv
smiles_col: "cxsmiles"
label_cols: ["homo_lumo_gap", "lumo"]
split_val: 0.2
split_test: 0.2
seed: *seed
splits_path: null # This may not always be provided
sample_size: null # This may not always be provided
idx_col: null # This may not always be provided
weights_col: null # This may not always be provided
weights_type: null # This may not always be provided
task_level: graph
alpha:
df: null
df_path: *df_path
smiles_col: "cxsmiles"
label_cols: ["alpha"]
split_val: 0.2
split_test: 0.2
seed: *seed
splits_path: null # This may not always be provided
sample_size: null # This may not always be provided
idx_col: null # This may not always be provided
weights_col: null # This may not always be provided
weights_type: null # This may not always be provided
task_level: graph
# Featurization
prepare_dict_or_graph: pyg:graph
featurization_n_jobs: 0
featurization_progress: True
featurization:
atom_property_list_onehot: [atomic-number, valence]
atom_property_list_float: [mass, electronegativity, in-ring]
edge_property_list: [bond-type-onehot, stereo, in-ring]
conformer_property_list: [positions_3d]
add_self_loop: False
explicit_H: False
use_bonds_weights: False
pos_encoding_as_features: # encoder dropout 0.18
pos_types:
node_laplacian_eigvec:
pos_type: laplacian_eigvec
pos_level: node
num_pos: 5
normalization: "none"
disconnected_comp: True
node_laplacian_eigval:
pos_type: laplacian_eigval
pos_level: node
num_pos: 5
normalization: "none"
disconnected_comp: True
rw_return_probs:
pos_type: rw_return_probs
pos_level: node
ksteps: [4, 8]
edge_rw_transition_probs:
pos_type: rw_transition_probs
pos_level: edge
ksteps: [2, 4]
nodepair_rw_return_probs:
pos_type: rw_return_probs
pos_level: nodepair
ksteps: [4]
electrostatic:
pos_type: electrostatic
pos_level: node
edge_commute:
pos_type: commute
pos_level: edge
nodepair_graphormer:
pos_type: graphormer
pos_level: nodepair
num_workers: -1
architecture:
model_type: FullGraphMultiTaskNetwork
mup_base_path: null
pre_nn: # Set as null to avoid a pre-nn network
out_dim: 16
hidden_dims: 16
depth: 1
activation: relu
last_activation: none
dropout: &dropout 0.1
normalization: &normalization batch_norm
last_normalization: *normalization
residual_type: none
pre_nn_edges: # Set as null to avoid a pre-nn network
out_dim: 16
hidden_dims: 16
depth: 1
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: none
pe_encoders:
out_dim: &pe_out_dim 16
edge_out_dim: &edge_pe_out_dim 8
pool: "sum" #"mean" "max"
last_norm: None #"batch_norm", "layer_norm"
max_num_nodes_per_graph: 30
encoders:
emb_la_pos:
encoder_type: "laplacian_pe"
input_keys: ["laplacian_eigvec", "laplacian_eigval"]
output_keys: ["feat"]
hidden_dim: 32
model_type: 'DeepSet' #'Transformer' or 'DeepSet'
num_layers: 2
num_layers_post: 1 # Num. layers to apply after pooling
dropout: 0.1
first_normalization: "none" #"batch_norm" or "layer_norm"
emb_rwse:
encoder_type: "mlp"
input_keys: ["rw_return_probs"]
output_keys: ["feat"]
hidden_dim: 32
num_layers: 2
dropout: 0.1
normalization: "layer_norm" #"batch_norm" or "layer_norm"
first_normalization: "layer_norm" #"batch_norm" or "layer_norm"
emb_electrostatic:
encoder_type: "mlp"
input_keys: ["electrostatic"]
output_keys: ["feat"]
hidden_dim: 32
num_layers: 1
dropout: 0.1
normalization: "layer_norm" #"batch_norm" or "layer_norm"
first_normalization: "layer_norm" #"batch_norm" or "layer_norm"
emb_edge_rwse:
encoder_type: "mlp"
input_keys: ["edge_rw_transition_probs"]
output_keys: ["edge_feat"]
hidden_dim: 32
num_layers: 1
dropout: 0.1
normalization: "layer_norm" #"batch_norm" or "layer_norm"
emb_edge_pes:
encoder_type: "cat_mlp"
input_keys: ["edge_rw_transition_probs", "edge_commute"]
output_keys: ["edge_feat"]
hidden_dim: 32
num_layers: 1
dropout: 0.1
normalization: "layer_norm" #"batch_norm" or "layer_norm"
gaussian_pos:
encoder_type: "gaussian_kernel"
input_keys: ["positions_3d"]
output_keys: ["feat", "nodepair_gaussian_bias_3d"]
num_heads: &num_heads 2
num_layers: 2
embed_dim: *pe_out_dim
use_input_keys_prefix: False
gnn: # Set as null to avoid a post-nn network
out_dim: 8
hidden_dims: 16
depth: 2
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: simple
virtual_node: 'none'
layer_type: 'pyg:gps' #pyg:gine #'pyg:gps' # pyg:gated-gcn, pyg:gine,pyg:gps
layer_kwargs: # Parameters for the model itself. You could define dropout_attn: 0.1
mpnn_type: 'pyg:gine'
mpnn_kwargs: null
#out_dim_edges: 10
attn_type: "none" # "full-attention", "none"
attn_kwargs: null
graph_output_nn:
graph:
pooling: [sum, mean, max]
out_dim: 8
hidden_dims: 8
depth: 1
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
task_heads:
homo:
out_dim: 2
hidden_dims: 8
depth: 1 # Not needed if we have hidden_dims
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
task_level: graph
alpha:
out_dim: 1
hidden_dims: 8
depth: 1 # Not needed if we have hidden_dims
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
task_level: graph
cv:
out_dim: 1
hidden_dims: 8
depth: 2 # Not needed if we have hidden_dims
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
task_level: graph
#Task-specific
predictor:
metrics_on_progress_bar:
homo: ["mae"]
alpha: ["mae"]
loss_fun:
homo: mse_ipu
alpha: mse_ipu
random_seed: *seed
optim_kwargs:
lr: 1.e-3
target_nan_mask: null
# Task-specific
metrics:
homo:
- name: mae
metric: mae
threshold_kwargs: null
target_nan_mask: null
alpha:
- name: mae
metric: mae
threshold_kwargs: null
trainer:
seed: *seed
logger:
save_dir: logs/QM9
name: *name
model_checkpoint:
dirpath: models_checkpoints/QM9/
filename: *name
save_top_k: 1
every_n_epochs: 1
trainer:
max_epochs: 2
min_epochs: 1
================================================
FILE: tests/config_test_ipu_dataloader_multitask.yaml
================================================
# Testing the gcn model with the PCQMv2 dataset on IPU.
constants:
name: &name neurips2023_small_data_gcn
seed: &seed 42
raise_train_error: true # Whether the code should raise an error if it crashes during training
accelerator:
type: ipu # cpu or ipu or gpu
config_override:
datamodule:
args:
ipu_dataloader_training_opts:
mode: async
max_num_nodes_per_graph: 44 # train max nodes: 20, max_edges: 54
max_num_edges_per_graph: 80
ipu_dataloader_inference_opts:
mode: async
max_num_nodes_per_graph: 44 # valid max nodes: 51, max_edges: 118
max_num_edges_per_graph: 80
# Data handling-related
batch_size_training: 50
batch_size_inference: 50
predictor:
optim_kwargs:
loss_scaling: 1024
trainer:
trainer:
precision: 16
accumulate_grad_batches: 4
ipu_config:
- deviceIterations(5) # IPU would require large batches to be ready for the model.
- replicationFactor(1)
# - enableProfiling("graph_analyser") # The folder where the profile will be stored
# - enableExecutableCaching("pop_compiler_cache")
- TensorLocations.numIOTiles(128)
- _Popart.set("defaultBufferingDepth", 128)
- Precision.enableStochasticRounding(True)
- useIpuModel(True)
# accelerator:
# type: cpu # cpu or ipu or gpu
# config_override:
# datamodule:
# batch_size_training: 64
# batch_size_inference: 256
# trainer:
# trainer:
# precision: 32
# accumulate_grad_batches: 1
datamodule:
module_type: "MultitaskFromSmilesDataModule"
# module_type: "FakeDataModule" # Option to use generated data
args: # Matches that in the test_multitask_datamodule.py case.
task_specific_args: # To be replaced by a new class "DatasetParams"
qm9:
df: null
df_path: qm9.csv.gz
# wget https://storage.valencelabs.com/graphium/datasets/neurips_2023/Small-dataset/qm9.csv.gz
# or set path as the URL directly
smiles_col: "smiles"
label_cols: ["A", "B", "C", "mu", "alpha", "homo", "lumo", "gap", "r2", "zpve", "u0", "u298", "h298", "g298", "cv", "u0_atom", "u298_atom", "h298_atom", "g298_atom"]
sample_size: 2000 # use sample_size for test
seed: *seed
task_level: graph
label_normalization:
normalize_val_test: True
method: "normal"
tox21:
df: null
df_path: Tox21-7k-12-labels.csv.gz
# wget https://storage.valencelabs.com/graphium/datasets/neurips_2023/Small-dataset/Tox21-7k-12-labels.csv.gz
# or set path as the URL directly
smiles_col: "smiles"
label_cols: ["NR-AR", "NR-AR-LBD", "NR-AhR", "NR-Aromatase", "NR-ER", "NR-ER-LBD", "NR-PPAR-gamma", "SR-ARE", "SR-ATAD5", "SR-HSE", "SR-MMP", "SR-p53"]
sample_size: 2000 # use sample_size for test
seed: *seed
task_level: graph
zinc:
df: null
df_path: ZINC12k.csv.gz
# df_path: data/neurips2023/small-dataset/ZINC12k.csv.gz
# wget https://storage.valencelabs.com/graphium/datasets/neurips_2023/Small-dataset/ZINC12k.csv.gz
# or set path as the URL directly
smiles_col: "smiles"
label_cols: ["SA", "logp", "score"]
sample_size: 2000 # use sample_size for test
seed: *seed
task_level: graph
label_normalization:
normalize_val_test: True
method: "normal"
# Featurization
prepare_dict_or_graph: pyg:graph
featurization_n_jobs: 30
featurization_progress: True
featurization_backend: "loky"
# processed_graph_data_path: "../datacache/neurips2023-small/"
featurization:
# OGB: ['atomic_num', 'degree', 'possible_formal_charge', 'possible_numH' (total-valence),
# 'possible_number_radical_e', 'possible_is_aromatic', 'possible_is_in_ring',
# 'num_chiral_centers (not included yet)']
atom_property_list_onehot: [atomic-number, group, period, total-valence]
atom_property_list_float: [degree, formal-charge, radical-electron, aromatic, in-ring]
# OGB: ['possible_bond_type', 'possible_bond_stereo', 'possible_is_in_ring']
edge_property_list: [bond-type-onehot, stereo, in-ring]
add_self_loop: False
explicit_H: False # if H is included
use_bonds_weights: False
pos_encoding_as_features: # encoder dropout 0.18
pos_types:
lap_eigvec:
pos_level: node
pos_type: laplacian_eigvec
num_pos: 8
normalization: "none" # nomrlization already applied on the eigen vectors
disconnected_comp: True # if eigen values/vector for disconnected graph are included
lap_eigval:
pos_level: node
pos_type: laplacian_eigval
num_pos: 8
normalization: "none" # nomrlization already applied on the eigen vectors
disconnected_comp: True # if eigen values/vector for disconnected graph are included
rw_pos: # use same name as pe_encoder
pos_level: node
pos_type: rw_return_probs
ksteps: 16
num_workers: -1 # -1 to use all
persistent_workers: False # if use persistent worker at the start of each epoch.
# Using persistent_workers false might make the start of each epoch very long.
featurization_backend: "loky"
architecture:
model_type: FullGraphMultiTaskNetwork
mup_base_path: null
pre_nn: # Set as null to avoid a pre-nn network
out_dim: 16
hidden_dims: 16
depth: 1
activation: relu
last_activation: none
dropout: &dropout 0.1
normalization: &normalization layer_norm
last_normalization: *normalization
residual_type: none
pre_nn_edges: null # Set as null to avoid a pre-nn network
pe_encoders:
out_dim: 32
pool: "sum" #"mean" "max"
last_norm: None #"batch_norm", "layer_norm"
encoders: #la_pos | rw_pos
la_pos: # Set as null to avoid a pre-nn network
encoder_type: "laplacian_pe"
input_keys: ["laplacian_eigvec", "laplacian_eigval"]
output_keys: ["feat"]
hidden_dim: 64
out_dim: 32
model_type: 'DeepSet' #'Transformer' or 'DeepSet'
num_layers: 2
num_layers_post: 1 # Num. layers to apply after pooling
dropout: 0.1
first_normalization: "none" #"batch_norm" or "layer_norm"
rw_pos:
encoder_type: "mlp"
input_keys: ["rw_return_probs"]
output_keys: ["feat"]
hidden_dim: 64
out_dim: 32
num_layers: 2
dropout: 0.1
normalization: "layer_norm" #"batch_norm" or "layer_norm"
first_normalization: "layer_norm" #"batch_norm" or "layer_norm"
gnn: # Set as null to avoid a post-nn network
in_dim: 16 # or otherwise the correct value
out_dim: &gnn_dim 16
hidden_dims: *gnn_dim
depth: 1
activation: gelu
last_activation: none
dropout: 0.1
normalization: "layer_norm"
last_normalization: *normalization
residual_type: simple
virtual_node: 'none'
layer_type: 'pyg:gcn' #pyg:gine #'pyg:gps' # pyg:gated-gcn, pyg:gine,pyg:gps
layer_kwargs: null # Parameters for the model itself. You could define dropout_attn: 0.1
graph_output_nn:
graph:
pooling: [sum]
out_dim: *gnn_dim
hidden_dims: *gnn_dim
depth: 1
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
task_heads:
qm9:
task_level: graph
out_dim: 19
hidden_dims: 16
depth: 1
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
tox21:
task_level: graph
out_dim: 12
hidden_dims: 16
depth: 1
activation: relu
last_activation: sigmoid
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
zinc:
task_level: graph
out_dim: 3
hidden_dims: 16
depth: 2
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
#Task-specific
predictor:
metrics_on_progress_bar:
qm9: ["mae"]
tox21: ["auroc"]
zinc: ["mae"]
loss_fun:
qm9: mae_ipu
tox21: bce_ipu
zinc: mae_ipu
random_seed: *seed
optim_kwargs:
lr: 4.e-5 # warmup can be scheduled using torch_scheduler_kwargs
# weight_decay: 1.e-7
torch_scheduler_kwargs:
module_type: WarmUpLinearLR
max_num_epochs: &max_epochs 1
warmup_epochs: 1
verbose: False
scheduler_kwargs:
# monitor: &monitor qm9/mae/train
# mode: min
# frequency: 1
target_nan_mask: null # null: no mask, 0: 0 mask, ignore-flatten, ignore-mean-per-label
multitask_handling: flatten # flatten, mean-per-label
# Task-specific
metrics:
qm9: &qm9_metrics
- name: mae
metric: mae_ipu
target_nan_mask: null
multitask_handling: flatten
threshold_kwargs: null
- name: pearsonr
metric: pearsonr_ipu
threshold_kwargs: null
target_nan_mask: null
multitask_handling: mean-per-label
- name: r2_score
metric: r2_score_ipu
target_nan_mask: null
multitask_handling: mean-per-label
threshold_kwargs: null
tox21:
- name: auroc
metric: auroc_ipu
task: binary
multitask_handling: mean-per-label
threshold_kwargs: null
- name: avpr
metric: average_precision_ipu
task: binary
multitask_handling: mean-per-label
threshold_kwargs: null
- name: f1 > 0.5
metric: f1
multitask_handling: mean-per-label
target_to_int: True
num_classes: 2
average: micro
threshold_kwargs: &threshold_05
operator: greater
threshold: 0.5
th_on_preds: True
th_on_target: True
- name: precision > 0.5
metric: precision
multitask_handling: mean-per-label
average: micro
threshold_kwargs: *threshold_05
zinc: *qm9_metrics
trainer:
seed: *seed
logger:
save_dir: logs/neurips2023-small/
name: *name
project: *name
#early_stopping:
# monitor: *monitor
# min_delta: 0
# patience: 10
# mode: &mode min
model_checkpoint:
dirpath: models_checkpoints/neurips2023-small-gcn/
filename: *name
# monitor: *monitor
# mode: *mode
# save_top_k: 1
save_last: True
trainer:
max_epochs: *max_epochs
min_epochs: 1
check_val_every_n_epoch: 20
================================================
FILE: tests/conftest.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pathlib
import pytest
TEST_DIR_PATH = pathlib.Path(__file__).parent / "data"
DATA_DIR_PATH = TEST_DIR_PATH.absolute()
@pytest.fixture
def datadir(request):
return DATA_DIR_PATH
================================================
FILE: tests/data/config_micro_ZINC.yaml
================================================
constants:
seed: &seed 42
raise_train_error: true # Whether the code should raise an error if it crashes during training
datamodule:
module_type: "DGLFromSmilesDataModule"
args:
df_path: graphium/data/micro_ZINC/micro_ZINC.csv
processed_graph_data_path: graphium/data/cache/micro_ZINC/
label_cols: ['score']
smiles_col: SMILES
# Featurization
featurization_n_jobs: -1
featurization_progress: True
featurization:
atom_property_list_onehot: [atomic-number, valence]
atom_property_list_float: [mass, electronegativity, in-ring]
edge_property_list: [bond-type-onehot, stereo, in-ring]
add_self_loop: False
explicit_H: False
use_bonds_weights: False
pos_encoding_as_features: &pos_enc
pos_type: laplacian_eigvec
num_pos: 3
normalization: "none"
disconnected_comp: True
pos_encoding_as_directions: *pos_enc
# Train, val, test parameters
split_val: 0.2
split_test: 0.2
split_seed: *seed
splits_path: null
batch_size_training: 128
batch_size_inference: 128
# Data loading
num_workers: 0
pin_memory: False
persistent_workers: False # Keep True on Windows if running multiple workers
architecture:
model_type: fulldglnetwork
pre_nn: # Set as null to avoid a pre-nn network
out_dim: 32
hidden_dims: 32
depth: 1
activation: relu
last_activation: none
dropout: &dropout 0.1
normalization: &normalization "batch_norm"
last_normalization: *normalization
residual_type: none
pre_nn_edges: # Set as null to avoid a pre-nn network
out_dim: 16
hidden_dims: 16
depth: 2
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: none
gnn: # Set as null to avoid a post-nn network
out_dim: 32
hidden_dims: 32
depth: 4
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: *normalization
residual_type: simple
pooling: [sum, max, dir1]
virtual_node: 'sum'
layer_type: 'dgl:dgn-msgpass'
layer_kwargs:
# num_heads: 3
aggregators: [mean, max, dir1/dx_abs, dir1/smooth]
scalers: [identity, amplification, attenuation]
graph_output_nn:
out_dim: 1
hidden_dims: 32
depth: 2
activation: relu
last_activation: none
dropout: *dropout
normalization: *normalization
last_normalization: "none"
residual_type: none
predictor:
metrics_on_progress_bar: ["mae", "pearsonr", "f1 > 3", "precision > 3"]
loss_fun: mse
random_seed: *seed
optim_kwargs:
lr: 1.e-2
weight_decay: 1.e-7
lr_reduce_on_plateau_kwargs:
factor: 0.5
patience: 7
scheduler_kwargs:
monitor: &monitor loss/val
frequency: 1
target_nan_mask: 0 # null: no mask, 0: 0 mask, ignore: ignore nan values from loss
metrics:
- name: mae
metric: mae
threshold_kwargs: null
- name: pearsonr
metric: pearsonr
threshold_kwargs: null
- name: f1 > 3
metric: f1
target_to_int: True
num_classes: 2
average: micro
threshold_kwargs: &threshold_3
operator: greater
threshold: 3
th_on_preds: True
th_on_target: True
- name: f1 > 5
metric: f1
target_to_int: True
num_classes: 2
average: micro
threshold_kwargs:
operator: greater
threshold: 5
th_on_preds: True
th_on_target: True
- name: precision > 3
metric: precision
average: micro
threshold_kwargs: *threshold_3
trainer:
logger:
save_dir: logs/micro_ZINC
early_stopping:
monitor: *monitor
min_delta: 0
patience: 10
mode: &mode min
model_checkpoint:
dirpath: models_checkpoints/micro_ZINC/
filename: "model"
monitor: *monitor
mode: *mode
save_top_k: 1
every_n_epochs: 1
trainer:
max_epochs: 25
min_epochs: 5
gpus: 1
================================================
FILE: tests/data/micro_ZINC.csv
================================================
SMILES,SA,logp,score
CCc1ccc(C(=O)/C(=C(/S)NC2CC2)[n+]2ccc(CC)cc2)cc1,2.775189478,3.7897,1.014510522
C=CCOc1ccc(C(F)(F)F)cc1C(=O)NC[C@H]1CCC[C@H]1O,3.071497898,3.161,0.089502102
COc1cc(OC)cc([C@@H](NC(=O)Cn2ccccc2=O)c2nccn2C)c1,2.84000896,1.5048,-1.33520896
CN1CCC[C@@]2(CCN(C(=O)CN3CCNC(=O)C3)C2)C1=O,3.605168457,-1.1109,-4.716068457
COc1ccc(Cl)cc1NC(=O)c1nn(C)cc1[N+](=O)[O-],2.132664673,2.2426,0.109935327
Cc1nn(-c2ccccc2)c(O)c1/C=[NH+]/Cc1ccncc1,3.117639223,0.98102,-2.136619223
C=CCN1C(=O)C(C)(C)COc2ccc(NC(=O)C3(c4ccc(OC)cc4)CCOCC3)cc21,2.744789746,4.3197,1.574910254
CON(C)C(=O)c1cnn2c(C)cc(C)nc12,2.547847184,0.97954,-1.568307184
NC(=O)[C@@H](NC(=O)CCCc1cccs1)c1ccccc1,2.444743614,2.4136,-0.031143614
COc1cccc(CN2CCC[NH+](CC(=O)Nc3ccc(F)cc3)S2(=O)=O)c1,3.546311784,0.8084,-2.737911784
Cc1ccc(-c2nc3ccc(C)c(C)c3[nH]2)nc1,2.342516783,3.55016,1.207643217
Cc1n[nH]c(SCC(=O)Nc2cccc(C#Cc3ccccn3)c2)n1,2.685016252,2.63872,-0.046296252
COC(=O)c1cncc(C(=O)Nc2cccc(COC(C)C)c2)c1,2.04419285,3.0455,1.00130715
Cc1ccc(C)c(Oc2ccc(CNC(=O)N3CCCSCC3)cn2)c1,2.369895336,4.13924,1.769344664
CC[NH2+][C@@]1(C(=O)[O-])CCC[C@H](Oc2ccc(CC)cc2)C1,4.165551538,0.6424,-3.523151538
CC(=O)N1c2ccc(S(=O)(=O)N3CCCC3)cc2C[C@H]1C(=O)NCC[NH+](C)C1CCCCC1,3.989079408,0.7123,-3.276779408
C=CCN1CC(=O)N2[C@@H](Cc3c([nH]c4ccccc34)[C@@H]2c2ccccc2OC)C1=O,3.287567863,3.0474,-0.240167863
COc1ccc(-c2noc3ncnc(N4CCC[C@H](C(=O)[O-])C4)c23)cc1,3.152874099,1.2597,-1.893174099
N#Cc1cc(NC(=O)Cc2ccccc2Cl)ccc1N1CCC(O)CC1,2.173785623,3.35398,1.180194377
Cc1ncc(-c2ccc(S(=O)(=O)NC3CCCCCC3)s2)o1,2.513723357,3.71262,1.198896643
Cc1ccc(NC(=O)c2ccc(F)cc2F)cc1S(=O)(=O)Nc1ccc(Cl)cc1,1.947448768,4.97972,3.032271232
CNC(=O)[C@@H]1CCC[NH+]1Cc1ccc(C)c(F)c1,4.391338086,0.42742,-3.963918086
CN1C(=O)N[C@@H](c2cccc([N+](=O)[O-])c2)C2=C1CN(c1ccc(F)cc1)C2=O,2.956072154,2.7309,-0.225172154
Cc1cc(CC(=O)N[C@H](c2ccc(F)cc2)C2CCC2)no1,2.665131639,3.32222,0.657088361
Cc1cc(N2CCN(CC(=O)NC3CC3)CC2)nc(-c2ccc([N+](=O)[O-])cc2)n1,2.249569987,1.76082,-0.488749987
Cc1cc(C(=O)N2CCN(C(=O)N[C@H]3CC(=O)N(C4CC4)C3)CC2)c(C)o1,2.921725033,1.12714,-1.794585033
O=c1c2c3nc4ccccc4nc3n(CCC3=CCCCC3)c2ncn1C[C@H]1CCCO1,3.257260117,4.3639,1.106639883
CC[NH+](C/C=C/c1ccc(C#N)cc1)C[C@H](C)C#N,4.36293875,1.63596,-2.72697875
COc1c(Cl)cc(C[NH2+][C@@H](Cc2c[nH]c3ccccc23)C(=O)[O-])cc1Cl,3.725368177,1.9079,-1.817468177
COc1ccccc1[C@H](C)NC(=O)C[C@H]1C[NH2+]CCO1,3.812797047,0.2247,-3.588097047
Cc1ccc(NC(=O)C(=O)NCCc2ccccc2F)c(C)c1,1.826753956,2.73994,0.913186044
CC(C)(C)OC(=O)N[C@H]1CCN(c2cc(-c3cccs3)n[nH]2)C1,3.228025092,3.2416,0.013574908
CC[C@H](C)C[NH+]1CCCC[C@@H]1C(=O)NC(C)(C)C,4.787067722,1.3846,-3.402467722
COC(=O)c1c(S(=O)(=O)NC2CC2)sc2c1CCN(Cc1ccc3ccccc3c1)C2,2.485526606,3.6869,1.201373394
Cc1cc(C)cc(NC(=O)[C@@H](Sc2nnnn2C2CC2)c2ccccc2)c1,2.708478583,4.09694,1.388461417
C=CCn1c([S-])nnc1-c1sc(NC(=O)c2cccc(NC(C)=O)c2)nc1C,2.943459024,3.01252,0.069060976
O=C(CNc1nc(C2CC2)no1)N1CCc2sccc2C1,2.781430253,2.0053,-0.776130253
Cc1ccccc1NC(=O)NCCn1c([N+](=O)[O-])cnc1C,2.258312512,2.22984,-0.028472512
C[C@H](NC=C(C#N)C#N)c1ccc(-n2cncn2)cc1,3.277555708,1.84896,-1.428595708
CN1C[C@H](C(=O)NC[C@H]2CCC(C)(C)c3ccccc32)CC1=O,3.259412472,2.4361,-0.823312472
CC(C)n1cccc1C(=O)N[C@H]1CCc2cc(N)ccc21,2.887282024,3.0685,0.181217976
CCN1C(=O)C(C#N)=C(C)/C(=C\Nc2ccc([N+](=O)[O-])cc2)C1=O,2.501067124,2.11938,-0.381687124
O=C(Nc1ccnn1Cc1cccc(Cl)c1)C1CCN(S(=O)(=O)c2ccccc2F)CC1,2.296421252,3.7633,1.466878748
COc1cc([C@@H]2N[C@H](C(=O)[O-])CS2)ccc1OC(=O)c1ccccc1,3.273511868,1.3679,-1.905611868
CCOc1ccc(C2=CCN(C(=O)c3ccccc3F)CC2)cc1,2.000346516,4.1539,2.153553484
CC(=O)N1CC[C@@H]([NH2+][C@@H](C)CSCC(C)C)C1,4.483674272,0.9483,-3.535374272
CCO[P@](=O)(CC(=O)[O-])c1ccccc1,3.510193788,0.3764,-3.133793788
O=C(CCc1nc2ccccc2c(=O)[nH]1)N1CCC[C@H]1C1CCCC1,2.774610155,3.0369,0.262289845
O=C(/C=C/SCc1ccco1)Nc1cccc(N2CCCC2=O)c1,2.584851266,3.792,1.207148734
CC(=O)c1c(C)[nH]c(C(=O)OCC(=O)N2C[C@H](C)C[C@@H](C)C2)c1C,3.068452859,2.49544,-0.573012859
Cc1ccc(C(=O)N2CCC[C@@H](C(=O)N(C)CC(=O)NC(C)C)C2)cc1,2.492863438,1.83022,-0.662643438
C[C@@H](C#N)Sc1nnc(C23CC4CC(CC(C4)C2)C3)o1,4.576896432,3.54158,-1.035316432
CC1(C)CCC[C@H]1n1c(N)[nH+]c2ccccc21,4.151966768,2.7888,-1.363166768
CCOc1ccc(C[NH+]2CCS[C@H]3COCC[C@@H]32)cc1OC,4.514122047,1.3831,-3.131022047
COc1cc(OC)c([C@@H](C)[NH2+]C[C@H](O)C2CCOCC2)cc1Cl,3.960984106,1.7691,-2.191884106
COC(C[C@@](C)(O)C[C@@H]1CCCN(S(C)(=O)=O)C1)OC,3.669896168,0.8081,-2.861796168
C=CCSc1nnc(C2CCOCC2)n1N,2.825013358,1.164,-1.661013358
O=C(Nc1c[nH]c2ccccc12)N1CCCC[C@@H]1CCO,2.674797385,2.9367,0.261902615
Cc1nc(-c2ccc(-c3noc(C4CC4)n3)cc2)cs1,2.133219774,4.04592,1.912700226
C[C@H]1CCCCN1C(=O)[C@@H](C)NC(=O)C(=O)Nc1ccnn1C(C)(C)C,3.418504414,1.4823,-1.936204414
c1cc2c(cc1N[C@@H]1CCOC3(CCC3)C1)CCC2,3.363061129,3.6889,0.325838871
O=C(Nc1ccc(CNC(=O)N2CCCCCCC2)cc1)c1ccco1,1.931563902,4.0076,2.076036098
CNC(=O)[C@H]1CCCCN1c1cccc(F)c1C(N)=S,2.82917551,1.5648,-1.26437551
O=C(NCc1nnc2n1CCC2)c1cc(-c2ccccc2)[nH]n1,2.376871447,1.5444,-0.832471447
Cc1nnc(-c2cc(CC(C)C)n(-c3cccc(C(=O)N[C@@H]4C[C@@H](C)CC(C)(C)C4)c3)n2)o1,3.548164288,5.37382,1.825655712
COc1cccc([C@@H]2CC(=O)C3=C(C2)NC(=O)C[C@H]3c2ccc(OC)c(OC)c2OC)c1,3.226440403,3.7253,0.498859597
C[NH2+]Cc1ccc(-c2cc(C)ccc2F)s1,3.128631552,2.55582,-0.572811552
CCC[C@H](NC(N)=O)C(=O)NC[C@@H]1CCCO1,2.869359841,0.1186,-2.750759841
COc1cc(F)cc(CNC(=O)[C@H]2CCCN2C(=O)Cc2ccccc2)c1,2.427176885,2.6842,0.257023115
CCc1cc(C#N)c(NC(=O)CCC(=O)N(CC)CC)s1,2.493551313,2.76928,0.275728687
C#Cc1cccc(NC(=O)C[NH+](C)[C@H](C)c2nnc(-c3ccccc3)o2)c1,3.779297449,1.9323,-1.846997449
CCOC[C@H](O)[C@](C)(CC)[NH+]1CCCC1,5.127905513,0.2312,-4.896705513
C[NH+](C)Cc1cccc(CNC(=O)NC[C@H](O)c2ccc(F)cc2)c1,3.24179101,1.003,-2.23879101
Cc1ccsc1C(=O)NCCNC(=O)c1ccco1,2.114582277,1.80932,-0.305262277
COC(=O)[C@H](C)N(Cc1ccccc1)C(=O)Cc1ccon1,2.852054716,1.8074,-1.044654716
C/C=C(/C)C(=O)NC[C@H]1C[C@@]12CCc1ccccc12,3.940919906,2.9729,-0.968019906
O=C1Nc2ccccc2Oc2cc([N+](=O)[O-])cc(Oc3ccc(Cl)cc3)c21,2.400946341,5.3985,2.997553659
CCc1onc(C)c1NC(=O)CCCC(C)(C)C,2.601600041,3.70032,1.098719959
COC(=O)C(C)(C)COc1ccc2c(c1)OC[C@@H]2[NH3+],3.576525387,0.94,-2.636525387
CC#CCCC(=O)Nc1cccc2c1C(=O)c1ccccc1C2=O,2.298959953,3.204,0.905040047
Cc1nc(C)c(S(=O)(=O)/N=C(\[O-])C[C@H]2CCCO2)s1,3.812988405,0.77654,-3.036448405
CCC(=O)N[C@H](CCSC)C(=O)Nc1ccc2[nH]c(C)cc2c1,2.732126257,3.06272,0.330593743
COCC[NH+](C)Cc1c(C)cc(C)c(C(C)=O)c1C,3.955103091,1.47556,-2.479543091
Cc1cscc1CNC(=O)N[C@@H](C)c1nc(C(=O)[O-])cs1,3.628458628,1.43692,-2.191538628
CNC(=O)NCC(=O)NC[C@H](c1cccnc1)C(C)C,2.795116489,0.8664,-1.928716489
COc1cccc(C(=O)N2CCC[C@H]2[C@H]2CCC[C@@H]2O)c1,3.002385916,2.4608,-0.541585916
COc1ccc2c(c1)[C@H]([NH2+][C@H](C)CCN1CCOCC1)CCCO2,3.789130146,1.5831,-2.206030146
COc1cccc(OCC(=O)N2CCN(c3nc4ccc(S(C)(=O)=O)cc4s3)CC2)c1,2.245396536,2.436,0.190603464
COc1cccc(-c2nc(C(=O)O[C@@H](C)CNC(C)=O)c(C)[nH]2)c1,2.86962049,2.07512,-0.79450049
CCO[C@H]1C[C@@H]1C(=O)Nc1ccc(Sc2nncs2)c(Cl)c1,3.445502237,3.7062,0.260697763
Cc1ccc(N(CC(=O)N2CCCC[C@H]2C)S(=O)(=O)c2c(C)nn(C)c2C)cc1,2.872766629,2.94166,0.068893371
COc1ccc(C(=O)Nc2nc(CC(=O)Nc3ccc(N(C)C)cc3)cs2)cc1,1.983649537,3.6512,1.667550463
O=c1c2ccccc2sn1-c1ncc(Br)s1,2.853247472,3.2712,0.417952528
Cc1ocnc1CNC(=O)N(C)Cc1ccc(OC(F)F)cc1,2.60278761,2.92602,0.32323239
COc1cc(Cl)c(C)cc1NC(=O)C(=O)NCc1ccccc1C[NH+](C)C,2.870575026,1.55642,-1.314155026
C[C@@H]([NH3+])c1cccc(Oc2cc(Br)ccc2Cl)c1,2.965149266,4.1977,1.232550734
CC(C)NS(=O)(=O)c1cccc(C(=O)NCc2ccco2)c1,1.961616674,1.8963,-0.065316674
CN(Cc1ncnn1C)C(=O)CSCc1ccccn1,2.529083407,1.1019,-1.427183407
CCOc1cc(/C=C(\C#N)C(=O)c2c[nH]c3cc(Cl)ccc23)ccc1OC,2.350767662,5.01848,2.667712338
CCOC(=O)Nc1ccc(NC(=O)N2C[C@@H](C)CC[C@H]2C)cc1C#N,3.067347555,3.77898,0.711632445
Cn1c(=O)oc2ccc(NC(=O)[C@@H]3CCN(C(=O)OC(C)(C)C)C3)cc21,2.748880773,2.327,-0.421880773
CC(C)N1CCO[C@@H]([C@H](O)c2cccs2)C1,3.356907136,1.8907,-1.466207136
CCN[C@H]1C[C@H](C)C[C@H](C)[C@@H]1[NH+]1C[C@@H](C)[C@@H](C)C1,5.501545361,1.5698,-3.931745361
CC1CCN(C(=O)N(CC[NH3+])C2CC2)CC1,3.063324,0.5446,-2.518724
COc1ccc(NC(=O)Cn2nc3c4scc(-c5ccc(F)cc5)c4ncn3c2=O)c(OC)c1,2.583698935,3.5677,0.984001065
CC(C)OC(=O)N1CCC(NC(=O)[C@H]2SCCc3ccccc32)CC1,3.087047482,3.1426,0.055552518
COc1ccc2c(c1)SC(=O)C2=O,2.401574365,1.5102,-0.891374365
COc1ccc(CCC(=O)N2CCC3(CC2)Nc2ccccc2-n2cccc23)cc1,2.913337418,4.3619,1.448562582
COc1ccccc1Cc1nnc(NC(=O)Cc2ccc(Br)cc2)s1,2.051226236,4.0812,2.029973764
CNC(=O)O/N=C(/N)c1ccc(Br)cc1,2.250317734,1.4254,-0.824917734
CC1(C)CN(C[C@@H](CS)c2ccccc2)CCO1,3.027019854,2.8108,-0.216219854
CCOC(=O)[C@H]1CCCN(C(=O)Cn2c(SC(F)F)nc3ccccc32)C1,2.799716077,3.1527,0.352983923
[NH3+][C@@H](Cc1ccccn1)c1ccc(Cl)cn1,3.324055225,1.6557,-1.668355225
CCOCCN(C)C(=O)C(=O)Nc1cccnc1-c1ccccc1,2.256607012,2.182,-0.074607012
CCn1cc([C@H](NC(=O)c2ccc(OC)c(OC)c2)c2ccc(F)cc2)c2cc[nH+]cc21,3.447988059,4.151,0.703011941
O=C([C@@H]1CC12CCOCC2)N1CC[C@@H](Oc2cccc(Cl)c2)C1,3.713785706,3.1364,-0.577385706
C[NH2+][C@]1(C(N)=O)CCC[C@H]1CCSC1CCOCC1,4.614268117,0.5061,-4.108168117
Cc1cc(NC(=O)c2ccc(-n3cncn3)nc2)ccc1N(C)C,2.267592306,2.28902,0.021427694
Cc1ccccc1CNC(=O)N1CCC([C@@H](O)c2ccc(Cl)cc2)CC1,2.524142332,4.30362,1.779477668
CCc1nc(CNCCc2cccs2)cs1,2.300594957,3.0993,0.798705043
O=C(Nc1ccc2nc(-c3cc(F)ccc3F)[nH]c2c1)c1ccc([N+](=O)[O-])cc1,2.145744096,4.6686,2.522855904
N#Cc1ccccc1NC(=O)C(=O)NC[C@H]1COC2(CCCC2)O1,3.412921889,1.29868,-2.114241889
O=C(NC1(C(=O)[O-])CC[NH2+]CC1)OCC1c2ccccc2-c2ccccc21,3.490460281,0.371,-3.119460281
Cc1ccc(C(=O)N2CCC[C@H]2CNC(=O)OC(C)(C)C)cc1,2.424440646,3.12432,0.699879354
C[NH+](C)CC(=O)NNC(=O)c1ccc(C2CCCCC2)cc1,2.819298295,0.6398,-2.179498295
C[C@@H]1CCCC[C@@H]1OCC(=O)N(C)Cc1cccc(O)c1,2.912373702,2.9459,0.033526298
C#CCNS(=O)(=O)c1ccc(C(=O)N[C@@H]2CCO[C@@]3(CCOC3)C2)cc1,3.898168643,0.666,-3.232168643
Cc1ccc(C2(CN3CCN(S(=O)(=O)N(C)C)CC3)CCCC2)cc1,2.439754533,2.23082,-0.208934533
Cc1sc2ncn(CCC(=O)NCC(N)=O)c(=O)c2c1C,2.347207183,0.06644,-2.280767183
COc1ccc(CCNC(=O)c2cc(C(C)C)nc3c2c(C)nn3C)cc1OC,2.282558575,3.38982,1.107261425
O=[N+]([O-])c1cnc(Br)s1,3.179818392,1.8138,-1.366018392
CC(C)(C)CC(=O)N1CCN(C(=O)c2ccn3ccsc23)CC1,2.712888331,2.7214,0.008511669
C/[NH+]=C(/NCCCc1cccc(Br)c1)NC1CC1,2.991361127,0.7897,-2.201661127
O=C(CCC1CCCC1)N1CCC(COc2ccccc2C(=O)N2CCCC2)CC1,2.19573155,4.5104,2.31466845
CC(C)(C)c1n[nH]cc1/C=C1\SC(NC2CCCCC2)=NC1=O,3.076214803,3.5998,0.523585197
CN(CC(F)(F)F)C(=O)CNC(=O)N1CCO[C@H](C#N)C1,3.449633505,-0.05892,-3.508553505
CC[C@@H](NC(=O)NCc1ccc(C#N)cc1)c1ccc(C)c(F)c1,2.506404532,3.9563,1.449895468
CC(C)c1noc(CCNC(=O)/C=C/c2cn(C)c3ccccc23)n1,2.489753078,3.0568,0.567046922
CC(C)=C1Oc2c3c(cc(C)c2C1=O)OCN(Cc1ccccc1)C3,2.840364996,4.21612,1.375755004
O=C1/C(=N/O)c2ccc(Cl)cc2N1Cc1ccccc1,2.0410745,3.0651,1.0240255
c1ccc(CC2(Cc3ccc4c(c3)CCC4)C[NH2+]C2)cc1,2.844717318,2.5239,-0.320817318
CC[C@H](C)NC(=O)N[C@H](c1ccccc1)C(F)(F)F,2.819638554,3.3877,0.568061446
NC(=O)COc1ccc2ccccc2c1C[NH2+]C1CCCCCCC1,2.95041288,2.8802,-0.07021288
O=C(COc1ccc(Cl)cc1[N+](=O)[O-])N1CCc2cc([N+](=O)[O-])ccc21,2.251658336,3.1245,0.872841664
O=C(Cc1ccc(F)cc1F)NC1CCN(c2cccc(F)c2)CC1,2.033335093,3.4316,1.398264907
COC(=O)c1ccc(C[NH+](C2CC2)[C@@H](C)c2ccco2)cc1F,4.038401414,2.5138,-1.524601414
Cc1ccc([C@@H]2C[NH2+]CC[C@@H]2c2c(F)cccc2F)cc1,3.825954589,3.10772,-0.718234589
Cc1ccc(-c2ccc(=O)n(CC(=O)NCc3ccco3)n2)c(C)c1,2.180504107,2.43654,0.256035893
CCNC(=O)NC(=O)CSc1nnc(N2C[C@@H](C)C[C@@H](C)C2)n1Cc1ccco1,3.320744633,2.3395,-0.981244633
CC(C)(C)NC(=O)N1CCC(CNC(=O)C(=O)Nc2ccccc2[N+](=O)[O-])CC1,2.351664532,1.8696,-0.482064532
Cc1ccc(-n2cnnn2)cc1NC(=O)c1cnn(Cc2ccccc2)c1,2.221673771,2.46782,0.246146229
O=C(Nc1ccc(F)cc1OCC(F)F)c1cccnc1N1CCCC1,2.273178554,3.7171,1.443921446
CCCNC(=O)CCNC(=O)CN1CCC([NH3+])CC1,2.682391179,-1.2748,-3.957191179
COCCNC(=O)COc1cc2c(c3oc(=O)c4c(c13)CCC4)CCC(C)(C)O2,2.819768111,2.5267,-0.293068111
CC(=O)N[C@@H](CC(=O)Nc1cnn(C)c1)c1cccs1,2.760825185,1.6876,-1.073225185
O=C(c1cccc(S(=O)(=O)[N-]c2ccccc2F)c1)N1CCC[C@@H]1c1nc2ccccc2[nH]1,3.413113671,5.0734,1.660286329
C[C@H](CNC(=O)c1[nH]nc(C2CC2)c1Cl)Oc1cccc(F)c1,3.06692193,3.2769,0.20997807
COc1ccccc1C(=O)/C(C#N)=C\c1cnc(-c2ccccn2)s1,2.549035852,4.00358,1.454544148
O=C(NCc1ccco1)[C@H]1CCCN1C(=O)C[NH+]1CCc2sccc2C1,4.132206287,0.5895,-3.542706287
CC[C@@H]1CN(C(=O)c2ccc3c(c2)OCO3)CC[NH2+]1,3.69238642,0.2131,-3.47928642
Cc1cc(C(=O)Nc2ccc(F)cc2C)on1,1.934252507,2.68284,0.748587493
C[C@@H](N[C@H](C[C@@H]1CCOC1)c1ccccc1)c1ccc([N+](=O)[O-])cc1,3.168218916,4.4133,1.245081084
O=C(NNS(=O)(=O)c1ccc(F)cc1)c1cc2c(s1)CCCC2,2.219578826,2.3893,0.169721174
Cc1cc(C#N)ccc1Oc1ccc([N+](=O)[O-])cc1C#N,2.231562188,3.43888,1.207317812
COc1ccc(-c2nc(CNC(=O)c3ccccc3)n[nH]2)cc1,1.980697063,2.4103,0.429602937
Cc1cccc(OCC(=O)N/N=C/c2cc(Br)c(O)c(Br)c2O)c1,2.453904832,3.46032,1.006415168
CC1=Nc2ccccc2Oc2c1c(=O)n(-c1ccccc1)c1ccccc21,2.458237938,5.2371,2.778862062
COC[C@@](C)(O)CNC(=O)[C@@H]1CC(=O)N(c2ccc(F)cc2)C1,3.005123049,0.6922,-2.312923049
Fc1ccccc1[C@@H]([NH2+]Cc1ccoc1)C1CCCC1,3.69757193,3.4136,-0.28397193
CC1CCN(C(=O)CN2C(=O)N[C@](C)(c3ccc(Cl)cc3Cl)C2=O)CC1,2.732754879,3.0189,0.286145121
COc1cccc(NC(=O)NCc2ccc3c(c2)N(C(=O)c2cccc(C)c2)CC3)c1,2.09420016,4.52822,2.43401984
O=C(/C=C/c1ccccc1)NC(=S)Nc1cc([N+](=O)[O-])ccc1F,2.105372184,3.2603,1.154927816
CCCn1nccc1NC(=O)c1nnn(-c2ccc(C)cc2)c1C,2.315214931,2.74294,0.427725069
COc1cccc(-c2nc(CC(=O)N[C@@H]3CCS(=O)(=O)C3)cs2)c1,2.722418907,1.6645,-1.057918907
COc1cc(NC(=O)c2cnn(-c3ccccc3)n2)cc(OC)c1OC,2.151091028,2.5454,0.394308972
COc1ccc2c(c1)cc(C(=O)N[C@H](C)Cc1cnccn1)n2C,2.807843567,2.3379,-0.469943567
CC(C)COCCNC(=O)NNc1ccccc1Cl,2.165718029,2.6387,0.472981971
CCC[C@@H](C)[NH2+]C[C@H](C)Oc1cccnc1C,4.126301903,1.90932,-2.216981903
Cc1ccc(C)c(OCCSc2ccc(N)c(C)c2)c1,2.060838323,4.36516,2.304321677
C[C@H](CC#N)N(C)C[C@@H]1CSc2ccccc21,3.590360435,3.10988,-0.480480435
CCN[C@@]1(C#N)CC[C@@H](Oc2cccc(F)c2F)C1,3.52564354,2.76798,-0.75766354
Cc1cc(C[NH2+][C@@H](CO)C(C)C)c(C)n1-c1ccccc1,3.596987275,2.17444,-1.422547275
CC1CCC(NC(=O)C(=O)Nc2cccc(-c3nncn3C)c2)CC1,2.347143544,2.1155,-0.231643544
CC(=O)Nc1cccc(C(=O)/C=C/c2ccco2)c1,2.003848944,3.1341,1.130251056
CCCNC(=O)c1cccc(NC(=O)C(=O)N2C[C@H]3CC=CC[C@@H]3C2)c1,3.148539376,2.1895,-0.959039376
Cc1nc(C)c(C(=O)N2CCN(C(=O)Cc3ccc(Cl)cc3)CC2)o1,2.226794753,2.47194,0.245145247
CCc1nc(CN/C(=[NH+]/C)N2CC[NH+](CC(C)C)CC2)cs1,4.614668767,-1.282,-5.896668767
CN(C)c1ccc(C(=O)Nc2c3c(nn2C)CCC3)cc1,2.254443099,2.2271,-0.027343099
O=C1[C@H](Nc2ccccc2O[C@H]2CCOC2)CCCN1Cc1ccccc1,2.979200776,3.4574,0.478199224
N#Cc1cccc(CNC(=O)CCOc2ccc(C=O)cc2)c1,1.99088313,2.45608,0.46519687
COc1ccc(CCn2cc(C(=O)[O-])c(=O)[nH]c2=O)cc1,2.453088713,-0.8486,-3.301688713
CN(CCc1ccccc1)C(=O)C(=O)Nc1ccc(Oc2ccncc2)cc1,2.089518656,3.5135,1.423981344
Cc1cc(C)cc(NC(=O)[C@H]2CCCN(S(=O)(=O)c3cn(C)cn3)C2)c1,2.830417176,2.07634,-0.754077176
Cc1nc(CNC(=O)[C@@H]2CCCCN2C(=O)OC(C)(C)C)sc1C,2.787253271,3.16574,0.378486729
CCSC1=NC(=O)[C@H]2C(=N1)NC1=C(C(=O)CCC1)[C@H]2c1cccnc1,4.10432105,2.4395,-1.66482105
COCC[C@H](C)C(=O)Nc1ccc(Oc2cc[nH+]c3ccccc23)cc1,3.383061791,4.0573,0.674238209
COC(=O)c1sccc1S(=O)(=O)N1CCC[C@@H](NC(C)=O)C1,2.758371043,0.8239,-1.934471043
CC(=O)O[C@H]1CC[C@]2(C)[C@@H]3CC[C@@]4(C)[C@@H](C[C@@H]5CCCC[C@@]54C(C)=O)[C@@H]3C[C@@H](O)[C@@]2(O)C1,4.807691125,4.422,-0.385691125
CCNS(=O)(=O)[C@H]1CCN(CCSc2ccccc2)C1,2.893618866,1.7923,-1.101318866
CC(C)OCCCNC(=O)N1CCC[NH+](CC2CCCCC2)CC1,3.869396076,1.682,-2.187396076
C[C@]1(CCc2ccccc2)NC(=O)N(CC(=O)N2CCc3sccc3[C@H]2c2cccs2)C1=O,3.330561001,4.227,0.896438999
Oc1n[nH]c(-c2ccccc2)c1/C=N\c1cccc(Cl)c1,2.575508438,4.1863,1.610791562
C[C@@](NC(=O)c1scnc1C1CC1)(C(N)=O)c1ccccc1,3.152593137,2.151,-1.001593137
Cn1cc(C[NH2+]C2CN(C(=O)OC(C)(C)C)C2)c(C(C)(C)C)n1,3.531149004,1.4003,-2.130849004
O=C(Cn1ncc2c([nH]c3ccccc32)c1=O)N1CC[C@@H](c2ccccc2)C1,2.801434255,2.8939,0.092465745
COc1cccn(CC(=O)N2CCC[C@H]2c2nccs2)c1=O,2.926470384,1.6771,-1.249370384
Cc1ccc(S(=O)(=O)N2CCCSCC2)c(C)c1,2.172121888,2.43104,0.258918112
CC(C)CSc1ccc(N)c(N)n1,2.677260409,1.9941,-0.683160409
Cc1ccccc1O[C@@H](C)CC[C@H](C)[NH+]1CCCCC1,4.183468302,2.99982,-1.183648302
CC1=CC(=O)C(C)=C(C)C1=O,2.554710776,1.4209,-1.133810776
COC(=O)c1c(NC(=O)c2cc3cc(Br)ccc3o2)sc2c1CCC2,2.326967163,4.7844,2.457432837
O=C(c1cc(=O)c2ccccc2o1)N1CCN(c2ccc(-c3noc(C(F)(F)F)n3)c[nH+]2)CC1,3.316210434,2.6383,-0.677910434
CCN1C(=O)c2cc(NC(=O)c3cccc(OC)c3OC)ccc2Oc2ccccc21,2.188834997,4.7285,2.539665003
CCC[C@H](C)C(=O)NCc1c(O)ccc2c1CCCC2,2.79815046,3.3234,0.52524954
Cc1c(CCC(=O)NC2CC2)c(=O)n2nc(-c3ccccc3)nc2n1Cc1ccccc1,2.39384803,3.12592,0.73207197
CC(C)(C)C(=O)N1N=C(c2cccc(NS(C)(=O)=O)c2)C[C@H]1c1cccs1,3.033061881,3.8434,0.810338119
COc1ccc(/C=C/C(=O)Nc2ccc(Cl)c(-c3nc4ncccc4o3)c2)cc1,2.282822064,5.2037,2.920877936
Cc1cc(C(=O)Nc2ccc(C[NH+]3CCCC3)cc2)c2cnn(Cc3cccs3)c2n1,3.308540728,3.28052,-0.028020728
C=CCn1c(CC)nnc1Sc1nc2c([nH]1)c(=O)n(C)c(=O)n2C,2.9507363,0.4514,-2.4993363
CCN(C(=O)[C@H](C)c1cccc(F)c1)c1ccc(C#N)c(Cl)c1,2.787640646,4.50738,1.719739354
COC(=O)CSc1nnc(-c2ccc(OC)cc2OC)o1,2.12901572,2.0189,-0.11011572
C[C@@H](NC(=O)CCc1cccc(F)c1)c1ccc2c(c1)CCC(=O)N2,2.559093635,3.5204,0.961306365
COC(=O)[C@H](NC(=O)c1cc(C)on1)c1ccc(F)c(F)c1,2.709930274,1.90532,-0.804610274
O=C([O-])c1ccccc1NCc1ccccc1[N+](=O)[O-],2.300858927,1.5704,-0.730458927
Cc1cc(=O)c(C(=O)NCCc2ccccc2F)nn1-c1ccccc1F,2.138447824,2.79162,0.653172176
CC(C)[C@H](NC(=O)c1ccccc1)C(=O)N[C@H](C)C(N)=O,2.47185794,0.431,-2.04085794
Cc1ccccc1OC1CN(c2ncccc2C(=O)N2C[C@@H]3CC[C@H]2C3)C1,3.852979496,3.28212,-0.570859496
CCc1nc(CN2CCC[C@H]([NH2+]CC3CCN(C(C)=O)CC3)C2)no1,3.812233153,0.4183,-3.393933153
CCN(CC)C(=O)CSc1c(C)cnc2c1c(=O)n(C)c(=O)n2C,2.632741217,0.90112,-1.731621217
CCOc1cc2c(cc1OCC)C[NH+](Cn1nc3n(c1=S)CCCCC3)CC2,4.018751641,2.53659,-1.482161641
O=C(CNCC(F)(F)F)N(Cc1cccc(O)c1)C1CC1,2.389515685,2.0351,-0.354415685
Cc1cc(CN2CCn3c(nnc3-c3ccccc3)C2)ccc1-n1cncn1,2.418767322,2.85002,0.431252678
C[C@@H]1C[C@@H](C)CN(S(=O)(=O)c2cccc(C(=O)Nc3nc(C4CC4)cs3)c2)C1,3.076699104,3.9394,0.862700896
COC(=O)c1c(NC(=O)CCC(=O)[O-])sc2c1CCCCCC2,2.562728956,1.6623,-0.900428956
S=C(Nc1cccnc1)N(Cc1ccccc1)C[C@H]1CCCO1,2.556251215,3.4596,0.903348785
C[NH+]1CC[C@@H](N2CC3(CCC2=O)CC[NH+](Cc2c[nH]cn2)CC3)C1,6.165717612,-1.5158,-7.681517612
CC[C@@](C)([C@@H]([NH2+]C)C1C[C@@H](C)C[C@@H](C)C1)[NH+]1CCCCC1,5.7683801,1.858,-3.9103801
Cn1cc(NC(=O)N2CCN(c3ccc([N+](=O)[O-])cc3)CC2)c2ccccc2c1=O,2.24714082,2.8008,0.55365918
CC(C)C[C@H](NC(=O)c1cc(-c2cccn2C)n[nH]1)c1ncnn1C,3.319192988,2.0609,-1.258292988
CCNC(=O)CN(C1CCCCC1)S(=O)(=O)c1ccccc1,2.045861074,2.1461,0.100238926
CCSc1cc(C(=O)NC[C@H](C)Nc2ccccc2)ccn1,2.637650604,3.424,0.786349396
Cc1noc(C(C)C)c1C(=O)N1CCN(C[C@H](C)O)CC1,2.859488392,1.24502,-1.614468392
CSc1ccc(Cl)c(C(=O)Nc2cc(C(=O)N(C)C)ccc2Cl)c1,2.013567479,4.6694,2.655832521
CC(=O)c1ccc(OC[C@@H](O)CN2CCn3c(nn(C)c3=O)C2)cc1,3.001332769,0.0399,-2.961432769
Cc1ccc([C@H]2C[NH+]=C(N)N2Cc2ccccc2)c(C)c1,3.297715153,1.25564,-2.042075153
Cc1nc(SCC(=O)c2ccc3[nH]c(=O)[nH]c3c2)nc(C)c1C,2.466697225,2.54646,0.079762775
Cn1cc(C(=O)N[C@H]2CCC[C@H]3OCC[C@H]23)c(-c2ccccc2)n1,3.352877627,2.7745,-0.578377627
CC[NH2+]C[C@]1(c2ccc(F)cc2Cl)CCC[C@@H]1C,4.165842394,3.1202,-1.045642394
CCN(CCNC(=O)N[C@@H](C)c1ncc(C)s1)c1cccc(C)c1,2.919644026,3.64664,0.726995974
COCC(=O)Nc1ccc(-c2noc([C@@H]3CCCN3Cc3[nH]cc[nH+]3)n2)cn1,3.85152994,1.1958,-2.65572994
Cc1cc(NC(=O)[C@H]2COc3ccccc3O2)c2ncccc2c1,2.627262029,3.32172,0.694457971
CC[C@H]1CC(=O)N(C[C@@H]([NH3+])c2ccc(OC(C)C)cc2)C1,3.641910716,2.0153,-1.626610716
Cc1ccc(C(=O)N(C)CCC#N)cc1,1.82915826,1.9807,0.15154174
CN(O)[C@H]1CC[C@H](c2ccc(C(C)(C)C)cc2)C1,3.088427273,3.9412,0.852772727
Cc1cc(C)n(-c2nc([O-])cc(C(C)C)n2)n1,3.071945177,1.47614,-1.595805177
CO[C@@H](C)c1nc(C[NH+](C)Cc2cccc(C)c2C)cs1,4.259931405,2.68224,-1.577691405
CCc1ncc(CN(C)C(=O)c2c[nH]nc2-c2ccc(OC)cc2)s1,2.753719275,3.3764,0.622680725
Cc1ccc2[nH]c3c(=O)n(CC(=O)N(C)c4ccc(C)c(C)c4)ncc3c2c1,2.543058432,3.46606,0.923001568
CC[NH2+]C[C@H]1CCO[C@@H]1c1ccc(F)cc1F,4.211152742,1.6257,-2.585452742
Cn1nc(C(C)(C)C)cc1C(=O)Nc1ccc(C(N)=O)cc1,2.062408364,2.0688,0.006391636
O=C(Nc1cccnc1)c1ccc(NC(=O)c2ncn(-c3ccccc3)n2)cc1,2.128494967,3.1669,1.038405033
O=c1cc(C2CCC2)nc(-c2cccc(C[NH+]3CC(O)C3)c2)[nH]1,3.57334498,0.4638,-3.10954498
C[C@@H](OC[C@H]1CCCO1)C(=O)Oc1cc(Cl)ccc1Cl,3.004023247,3.4829,0.478876753
COc1ccccc1C1(C(=O)Nc2ccc3c(c2)N(C(=O)C2CC2)CC3)CC1,2.34415068,3.6646,1.32044932
O=C([O-])[C@@H]1Cc2c([nH]c3ccccc23)[C@H](C(=O)[O-])[NH2+]1,4.686340492,-2.8031,-7.489440492
O=C1NC2(CCCC2)C(=O)N1Cc1ccc(Br)s1,2.905374606,2.8752,-0.030174606
CC(C)N1CCO[C@H](c2nnn[n-]2)C1,4.545882887,-0.3895,-4.935382887
Cc1ccc2c(c1)C(=O)N([C@H](C)C(=O)NCCCC(C)C)C2=O,2.543137633,2.53192,-0.011217633
CCN(CC)S(=O)(=O)N[C@H](c1ccc(F)cc1)c1nccn1C,2.880328558,1.8248,-1.055528558
CCN(CC)C(=O)C1CCN(c2cccc(Cl)c2)CC1,1.925061949,3.4248,1.499738051
O=C(NCC(=O)N1CCN(c2ccc(F)cc2)CC1)N[C@@H]1CCS(=O)(=O)C1,2.699990976,-0.0394,-2.739390976
CCn1c(SCC(N)=O)nnc1[C@H](C)Oc1cccc(Cl)c1,2.695897046,2.6688,-0.027097046
COC(C)(C)C(=O)Cc1cccc2ccccc12,2.090800657,3.3764,1.285599343
CC1CCN(C(=O)c2ccccc2NCC(=O)N[C@H](C)C(C)C)CC1,2.513852635,3.1313,0.617447365
COCC[C@@H](C)C(=O)Nc1ccc2c(C)n[nH]c2c1,2.813255353,2.48242,-0.330835353
CC[C@H]([NH3+])[C@H](Sc1cc2ccccc2[nH]1)c1cccnc1,3.893539633,3.4168,-0.476739633
N#C[C@@H](c1ccc(Cl)cc1)c1ccc([C@H](O)c2ccccc2)cc1,2.754553872,5.07718,2.322626128
CCS(=O)(=O)c1ccccc1NCc1c[nH+]cn1C1CC1,3.319676997,2.0428,-1.276876997
Cc1ccc(OC[C@@H](O)C[NH+](C)Cc2cc(C)on2)c(C)c1,3.945702345,1.05446,-2.891242345
COc1cccc2c1OC[C@@H](NC(=O)Nc1cncc3ccccc13)C2,2.816092277,3.3686,0.552507723
Cc1cc([C@H]2CCCN2CCc2ccc([N+](=O)[O-])cc2)no1,2.770692362,3.27082,0.500127638
COc1ccccc1-n1nc(C(=O)NC2CCCCCC2)c2ccccc2c1=O,2.082487998,3.8469,1.764412002
O=C(Cn1ccc(=O)[nH]c1=O)NC[C@H]1CCOC1,2.849533882,-1.3107,-4.160233882
CC(=O)N1C(C)(C)C=C(CO)C1(C)C,3.451082344,1.3244,-2.126682344
Cc1cnc(CNCc2c[nH+]cn2C2CC2)cn1,3.652209805,1.02532,-2.626889805
Cc1cc(C(=O)N2CCc3cc(Cl)cc(Cl)c3C2)[nH]n1,2.669253158,3.22342,0.554166842
CCOC(=O)[C@H](CC)N1CCC(C(=O)N2CCCCCC2)CC1,2.5693103,2.4427,-0.1266103
CC(C)[C@H](C)NC(=O)C(=O)Nc1cc(Br)ccc1-n1cccn1,2.804510976,2.734,-0.070510976
O=C(CSc1ccc(F)cc1)Nc1nnc(-c2ccccn2)o1,2.191540921,3.0015,0.809959079
C/C=C/c1ccc(OCC(=O)Nc2ccc3[nH]c(=O)[nH]c3c2)c(OC)c1,2.253707868,2.9154,0.661692132
CN(CC[NH+](C)C)C(=O)c1cc(F)c(Cl)cc1Cl,3.312684493,1.349,-1.963684493
O=[N+]([O-])c1cnccc1NCc1cccc(F)c1,2.013819951,2.741,0.727180049
Cc1ccc(F)c(NC(=O)c2cccc(CS(C)(=O)=O)c2)c1,1.842203357,2.93102,1.088816643
Cc1nc(N2CCN(c3ccccc3O)CC2)nc2cc3c(cc12)CCC3,2.344492661,3.45912,1.114627339
CC(C)c1ccsc1C(=O)N1CCN(c2ncccc2C#N)CC1,2.467553166,3.10058,0.633026834
Cc1ccc(S(=O)(=O)N2CCC[C@@H](c3nc(-c4ccccc4Br)no3)C2)cc1,2.679753116,4.37582,1.696066884
CCc1ccc2nc(C)cc(C(=O)N[C@H]3CCC(=O)N(C)C3)c2c1,2.726969629,2.45622,-0.270749629
CS(=O)(=O)[N-]c1ccc(C2=NN/C(=N\c3ccccc3)SC2)cc1,3.686623439,3.3796,-0.307023439
COc1ccc(Br)cc1C[NH2+][C@H]1CCCC1(C)C,3.803916035,3.0998,-0.704116035
O=C1CCC[C@@H]1CC[S@@](=O)c1cc(Cl)ccc1Cl,3.495879625,3.8603,0.364420375
C[C@@H]([NH2+]Cc1ccn[nH]1)c1ccc(Cl)s1,4.249743882,1.9492,-2.300543882
NC(=O)[C@@H]1CCCN1c1nc(-c2cccnc2)nc2scc(-c3ccccc3)c12,2.881834056,3.8744,0.992565944
COc1ccccc1CNC(=O)c1nn(C)c(=O)c2ccccc12,1.894141175,1.8721,-0.022041175
Cc1ccc(F)cc1S(=O)(=O)NCCCCn1ccccc1=O,2.142833523,2.05452,-0.088313523
Cc1cc(C)nc(N(C)CC(C)(C)O)n1,2.621362705,1.30054,-1.320822705
COC(=O)c1sc2ccc(NC(=O)NC(C)C)cc2c1OC,2.204937973,3.2264,1.021462027
Oc1c(F)ccc2cc[nH]c12,2.532800833,2.0126,-0.520200833
Cc1cccc(-c2nc(C[NH+]3CCC[C@@H](Cn4cncn4)C3)no2)c1,4.25447131,1.13162,-3.12285131
O[C@H]1Cc2ccccc2[C@H]1[NH2+]Cc1nccn1Cc1ccccc1,3.674403729,1.6531,-2.021303729
Cc1cccc(S(=O)(=O)[C@H](C)C(=O)Nc2cc(F)ccc2F)c1,2.496263936,3.07412,0.577856064
O=C1C([O-])=C(O)C(=O)c2ccccc21,2.704172084,0.1955,-2.508672084
CC(C)(C)CC(=O)Nc1cc(F)c(F)cc1Cl,2.036293505,3.9929,1.956606495
COc1ccc(C(=O)NC(=S)Nc2ccc3oc(-c4ccc(O)c(Br)c4)nc3c2)cc1,2.285489018,5.0983,2.812810982
COC(=O)C1(NCc2cnc3ccccn23)CCCC1,2.69067309,1.9097,-0.78097309
COC(=O)c1cc(-c2oc3ccc(Br)cc3c(=O)c2C)cc([N+](=O)[O-])c1,2.387240107,4.22572,1.838479893
Fc1ccc(CNC2=NC=NC3=NC=N[C@H]32)cc1,3.602406741,1.1647,-2.437706741
OC[C@@H](CCc1cccs1)c1cccc(F)c1,2.630519417,3.5959,0.965380583
C[C@H]1CCc2c(C(=O)NCc3ccc(S(N)(=O)=O)cc3)csc2C1,2.683701432,2.4503,-0.233401432
COC(=O)NCc1ccc(NCc2ccc(Br)c(C)c2)cc1,1.910394325,4.22562,2.315225675
CNS(=O)(=O)CC(=O)N[C@H](C)c1ccc(SC(C)C)cc1,2.831783505,1.9135,-0.918283505
c1cc(N2CCCCCC2)ccc1C[NH2+]Cc1nnc2n1CCC2,3.039500112,1.8683,-1.171200112
CC[C@@H](C)NC(=O)[C@@H](C)S(=O)(=O)Cc1ncc(Cl)n1C,3.496099959,1.2915,-2.204599959
CC(=O)c1cccc(OC(=O)/C=C/c2cn(CCC#N)c3ccccc23)c1,2.377963524,4.37638,1.998416476
CC(C)c1ccccc1NC(=O)C1CCN(S(C)(=O)=O)CC1,1.946704896,2.4201,0.473395104
C[NH+](C)[C@H]1CC[C@@H](NC(=O)N2CCN(c3nnnn3-c3ccccc3)CC2)C1,3.830633555,-0.4405,-4.271133555
CCn1cc(C(=O)C(=O)N(C)c2c(Cl)cccc2Cl)cn1,2.653209612,3.0555,0.402290388
CC(C)CCN1N[C@H](C2=NC(=O)C3=C4CCCC[C@@H]4SC3=N2)c2ccccc2C1=O,4.330683963,4.0574,-0.273283963
Cc1ccnc2nc(C(=O)N(CCC[NH+](C)C)Cc3ccccc3)nn12,3.171964209,0.60972,-2.562244209
CC[C@H](NC(=O)c1ccc([N+](=O)[O-])cc1F)c1ccc(OC)cc1,2.384004406,3.6236,1.239595594
CCc1ccccc1NC(=O)N[C@H](C)C(=O)N1CCCC[C@@H]1C,2.748723049,3.16,0.411276951
O=C(CS(=O)(=O)c1ccc2ccccc2n1)N1CCCC1,2.204420544,1.6309,-0.573520544
Cc1nc(-c2ccc(S(=O)(=O)N[C@H](C)C[NH+]3C[C@@H](C)C[C@@H](C)C3)cc2)co1,4.544819199,1.87762,-2.667199199
CCOc1ccccc1NC(=O)C[C@@H]1CSc2nc(C(C)(C)C)cc(=O)n21,3.009533648,3.6151,0.605566352
C[C@H]([NH2+]Cc1ccc(F)c(CO)c1)c1ccc(-c2cccs2)cc1,3.417228851,3.8711,0.453871149
C[C@@H](Oc1ccccc1Cl)C(=O)Nc1cccc(I)c1,2.252775185,4.3506,2.097824815
COc1ccc(CCNC(=O)c2c[nH]nc2-c2ccccc2)cc1,2.153124233,3.0578,0.904675767
C[NH+]1CCN(CC(=O)NC[C@@H]2COc3ccccc3O2)CC1,3.69680263,-1.2271,-4.92390263
COc1ccc2c(c1)N(C(=O)c1cccc([N+](=O)[O-])c1)CCO2,2.081577406,2.6426,0.561022594
CCc1nc(C(=O)Nc2ccc(N(C)C(=O)OC)cc2)nn1-c1c(Cl)cccc1Cl,2.515200631,4.5914,2.076199369
O=C(Nc1sc2c(c1C(=O)Nc1ccccc1)CCC2)c1ccccc1Cl,1.933911169,5.3948,3.460888831
Cc1ccc(Sc2ccc(=O)n(-c3ccc(C(=O)[O-])cc3)n2)cc1,2.544596831,2.05562,-0.488976831
O=C1CCCN1CC#CC[NH+]1CCCC1,4.434869482,-0.7091,-5.143969482
COc1cc2c(cc1C[NH+]1C[C@@H]3CCC[C@H](C1)C3(O)c1ccccn1)CCC2,5.299426023,2.2815,-3.017926023
CC(C)CN(C)CN1C(=O)C(=O)N(C23CC4CC(CC(C4)C2)C3)C1=O,4.036679938,2.2913,-1.745379938
C[C@@H](O)c1ccc(N(C)[C@H](C)c2ccccc2F)cc1O,3.081308678,3.782,0.700691322
COc1ccc(NC(=O)N[C@@H]2CCO[C@@H]2C)c(Br)c1,3.049111174,2.7566,-0.292511174
Cn1c(C[NH+]2CCC(CS(N)(=O)=O)CC2)nnc1-c1ccccc1,3.609551356,-0.4345,-4.044051356
CCc1cccc(NC(=O)Cc2nc(Cc3ccc(F)cc3)n[nH]2)c1,2.201228159,3.2782,1.076971841
Cc1cc(O)c(-c2cc(C(=O)Nc3cccnc3)n[nH]2)cc1C,2.368833974,3.04644,0.677606026
Cc1cnc(CN2CCO[C@H](CN(C)c3cccn[nH+]3)C2)o1,4.141147342,0.52932,-3.611827342
CC[NH2+][C@@H](Cc1ccc(Br)s1)[C@@H]1CSCCO1,4.741884708,2.137,-2.604884708
O=C(NC(=S)Nc1ccc(O)cc1)c1ccccc1[N+](=O)[O-],1.926072916,2.4272,0.501127084
CC(=O)Nc1cccc(NC(=O)C(=O)N(C)CCc2cccs2)c1C,2.321622474,2.65452,0.332897526
Clc1cccc(CC[NH2+]Cc2cc[nH]c2)c1,3.452394629,1.9742,-1.478194629
O=C(CSC(=S)N1CCCC1)N1CCc2ccc([N+](=O)[O-])cc21,2.513903323,2.5978,0.083896677
COc1ccc(N2C[C@@H](C(=O)NN3CCCCC3)CC2=O)c(OC)c1,2.730423213,1.5738,-1.156623213
O=C(c1cccc(O)c1)N1CCN(C/C=C/c2ccccc2[N+](=O)[O-])CC1,2.224870018,2.7716,0.546729982
COc1cc(-c2cccc(CN3CCOCC3)c2)ncn1,2.083956027,1.9844,-0.099556027
Cc1csc(C(=O)[O-])c1NC(=O)c1cccc([N+](=O)[O-])c1,2.643339977,1.58052,-1.062819977
CCC(CC)C(=O)Oc1ccc(S(=O)(=O)N(C)C)cc1,2.090592718,2.2785,0.187907282
CN(C)S(=O)(=O)c1ccc(C(=O)N2CCC(Oc3nc4c(F)cccc4s3)CC2)cc1,2.426432086,3.3693,0.942867914
COC(=O)Cc1ccc(OCC(=O)N2CCCC2)cc1,1.744125512,1.4033,-0.340825512
CC[C@H](Oc1ccc2c(c1)CCC2)C(=O)N1CCCCCC1,2.545510082,3.7353,1.189789918
CO[C@H](C(=O)Nc1ccc(C(=O)NCC(C)C)cc1)c1ccccc1,2.252969911,3.3986,1.145630089
O=C1C[C@@H](CNC(=O)Nc2ccc3c(c2)COC3)c2ccccc2N1,2.934345456,2.9643,0.029954544
C=CCN(CC(=O)[O-])C(=O)CSc1cccs1,3.162627302,0.6047,-2.557927302
CCOC(=O)C1CCN(CN2C(=O)NC(c3ccccc3)(c3ccccc3)C2=O)CC1,2.379611396,2.7146,0.334988604
CC(C)([C@H](O)[C@H]1CCOC2(CCCC2)C1)[NH+]1CCCCC1,5.244483813,1.9341,-3.310383813
Nc1cc(-c2nc3cc(-n4cnnc4)ccc3o2)ccc1Cl,2.535858453,3.3111,0.775241547
C[NH+](C)[C@@H]1CC[C@H](NC(=O)C(C)(C)CCCc2ccccc2)C1,3.853817039,2.2173,-1.636517039
CCOc1ccc([C@H](C)NC(=O)c2ccc(F)cc2F)cc1,2.159335886,3.8545,1.695164114
COC(=O)C(C)(C)[C@@H]1CCC[NH+](Cc2nnc(C)n2C2CC2)C1,4.613226345,0.91552,-3.697706345
CCc1cnc(CN(C)C(=O)c2ccc(SC(F)F)cc2)s1,2.537210369,4.2924,1.755189631
C[NH2+]Cc1cc(=O)[nH]c(-c2ccc(F)cn2)n1,3.604829082,-0.3358,-3.940629082
C[C@@H](NC(=O)COc1ccccc1F)c1ccccc1,2.002354089,3.0819,1.079545911
CC[C@@H](C)Oc1cccc(C(=O)Nc2cccc(C(=O)N3CC[NH+](C)CC3)c2)c1,3.343060863,2.0867,-1.256360863
COC(=O)c1cccc(CNC(=O)Nc2cc(F)ccc2OC)c1,1.778129704,2.9426,1.164470296
NC(=O)c1cncc(C#CC2(NC(=O)C3CCCC3)CCCCC2)c1,2.735607005,2.5413,-0.194307005
O=C(CN1CC2(CCOCC2)Oc2ccccc2C1=O)NCCN1CCCC1=O,3.166655355,0.809,-2.357655355
CC[C@H]1CCC[C@@H](C(=O)[C@@](C)(CC)N2CCOCC2)C1,3.690512322,3.2728,-0.417712322
COc1ccc(OC)c(-n2nnnc2S[C@@H](C)c2nc3ccccc3n2C(F)F)c1,3.036983258,4.2776,1.240616742
CC(=O)NN1C(N)=C(C#N)[C@@H](c2ccc(Cl)cc2)C2=C1CC(C)(C)CC2=O,3.23336918,3.12738,-0.10598918
Cc1cc(-c2cncnc2C2CCN(C(=O)c3cn(C)nc3C)CC2)on1,2.788500188,2.50174,-0.286760188
C[C@H](NC(=O)Cc1cc2ccccc2[nH]c1=O)c1ccc2ccccc2c1,2.464508914,4.1012,1.636691086
Cc1ccc(CC(=O)N2CCC[C@H](C)C2)cn1,2.432306191,2.19102,-0.241286191
CCn1/c(=N/C(=O)Cn2cccn2)[nH]c2ccc(Br)cc21,3.05958439,2.0758,-0.98378439
Cc1cc(C(=O)N2CCN(Cc3cnc4ncccn34)CC2)c(C)o1,2.619295467,1.89714,-0.722155467
COc1ccccc1CNC[C@@H](c1ccc(C)o1)N1CCOCC1,2.661936754,2.75972,0.097783246
C#CCN(Cc1ccccc1Cl)C1CCCC1,2.276202846,3.7178,1.441597154
CCn1cc(Cn2cc(NC(=O)C3CCN(S(C)(=O)=O)CC3)cn2)cn1,2.468456254,0.7579,-1.710556254
CC[C@@H](C)NC(=O)[C@@H](C)NCc1cccc(C(N)=O)c1,2.653152917,1.1783,-1.474852917
NC(=O)C1CC[NH+](C/C=C/c2ccccc2)CC1,3.578814538,0.48,-3.098814538
c1ccc(-c2cc(CSc3ncnc4sccc34)on2)cc1,2.382726154,4.6386,2.255873846
C[C@H](NC(=O)N(C)C)c1cccc(C#CCCO)c1,2.827051719,1.7527,-1.074351719
CC(=O)CCc1ccc(O[C@H](C)C(=O)N[C@H]2CCCC[C@@H]2C)cc1,2.92425479,3.6704,0.74614521
O=Cc1c(C2CC2)oc2ccc(OCc3ccc(Cl)nc3)cc12,2.624109703,4.7501,2.125990297
CN(Cc1cscn1)c1ccc(S(=O)(=O)C(C)(C)C)nc1,2.900280867,2.7467,-0.153580867
CC(C)[NH+]1CCN(c2ccc(C(=O)N3C[C@H](C)OCC3(C)C)cc2)CC1,4.047651791,1.4394,-2.608251791
O=C(NCCC[NH+]1CCCCC1)N1CCO[C@H](c2ccccc2)C1,3.814447251,1.2284,-2.586047251
C[C@H](C(=O)N(C)CCC#N)[NH+](C)Cc1ccccc1,3.961452338,0.46188,-3.499572338
O=C(/C=C/c1ccc(Cl)cc1)Nc1nnc([C@H]2CCCO2)s1,2.806479793,3.6949,0.888420207
CC[C@@](C)([C@H](NC)c1cccc(OC)c1)[NH+]1CCCCC1,4.2931591,2.1932,-2.0999591
Cn1c(=O)c2[nH]c(NCCCCl)nc2n(C)c1=O,2.736699884,0.0011,-2.735599884
Cc1csc(C2(NC(=O)CCCc3nnc(-c4ccccc4)o3)CCCC2)n1,2.643283154,4.40992,1.766636846
CCSc1ccc(Cl)cc1C(=O)Nc1cccnc1C,2.132314147,4.40772,2.275405853
C[C@@H]1CCCN(C(=O)N[C@@H]2CCCCC[C@@H]2[NH3+])C1,3.76711659,1.3711,-2.39601659
O=C(CN1CCCOC1=O)N1CCC[C@@H]([NH+]2CCCCCC2)C1,4.143119308,0.2786,-3.864519308
Cc1cc(SCCCSCC#N)nc2ccccc12,2.540944048,4.2822,1.741255952
O=C(CCNc1ccccc1[N+](=O)[O-])OC1CCC1,2.06472993,2.4925,0.42777007
CCCSC1=NC(=O)[C@H]2C(=N1)NC(=O)C[C@H]2c1cc(Br)ccc1OC,3.954159567,3.1153,-0.838859567
C=CCN(Cc1cccc(C#N)c1)C[C@H](O)c1cccc(F)c1,2.763633613,3.41898,0.655346387
O=C([O-])c1cccc2c1ccn2Cc1cscn1,2.841347083,1.5096,-1.331747083
C[C@@H]1CCCCN1C(=O)c1ccc(NS(=O)(=O)c2ccc3c(c2)=[NH+]C(=O)[NH+]=3)cc1,3.780275122,-1.964,-5.744275122
CC[C@@H](Cl)CCc1cc(C)cc(F)c1,2.71715977,4.08412,1.36696023
[NH3+][C@H](Cc1cccc(F)c1F)c1cc(Cl)sc1Cl,3.588641574,3.8588,0.270158426
Cc1cnc(N(C)CC2CCCC2)c([N+](=O)[O-])c1,2.421682195,2.92462,0.502937805
Cc1cccc2c(CCC(=O)N3CCC[C@@H](n4cncn4)C3)c[nH]c12,2.996277595,2.86412,-0.132157595
Cc1noc(C)c1[C@@H]1CCCN1C(=O)CN1CCNC1=O,3.158593888,0.98014,-2.178453888
CCCCCCSc1nc2ncccn2n1,2.464580554,2.7967,0.332119446
Cc1ccc(OC(=O)C2CCN(C(=O)c3ccc(F)cc3)CC2)c(C)c1,1.905636655,3.90034,1.994703345
CC[C@]1(C2CC2)CC(=O)NC(=O)[C@H]1Cc1ccccc1,3.395847148,2.6982,-0.697647148
O=C(CSc1ncc2ccccn12)N1CCc2sccc2C1,2.587505606,3.0728,0.485294394
CC(=O)Nc1ccc(NC(=O)c2ccccc2C(=O)c2ccc(F)cc2)cc1Cl,1.8770974,4.9208,3.0437026
Cc1ccc(Cl)cc1NC(=O)N1CC[C@@H](N(C)C(=O)OC(C)(C)C)C1,2.673373595,4.12152,1.448146405
Nc1nc(CC(=O)N2CCC[C@@H](c3[nH]ncc3-c3ccccc3)C2)cc(=O)[nH]1,3.16264224,1.6909,-1.47174224
C[C@H](NS(=O)(=O)c1ccccc1)C(=O)N1C[C@H](C)c2ccccc21,2.879521476,2.5037,-0.375821476
Cc1cccc2cc(C(=O)NC[C@H](C)C(=O)[O-])oc12,3.101639588,0.85702,-2.244619588
CNC(=O)c1cccc(OC(=O)c2oc3ccc(OC)cc3c2C)c1,2.081208379,3.32862,1.247411621
CC[C@@H](NC(=O)Nc1cc(COC)ncn1)c1ccc(C)c(F)c1,2.843322787,3.34332,0.499997213
CCc1nn(C)cc1NC(=O)N1CCN(C(=O)[C@H]2CCCO2)CC1,3.02663316,0.8376,-2.18903316
CC(C)Nc1nnc(SCc2cn3cc(Cl)ccc3n2)s1,2.651955928,3.9518,1.299844072
CC(=O)Nc1cccc(N[C@H](C)C(=O)NC[C@H](C)c2ccccc2)c1C,2.750741611,3.67372,0.922978389
CC(C)(CCC(=O)OC(C)(C)C)NC(=O)c1ccc(Br)o1,2.503298166,3.6724,1.169101834
CCOC(=O)c1c(NC(=O)c2cc3cccc(OC)c3oc2=O)sc(C)c1C,2.294956795,3.90894,1.613983205
CC1(C)C[C@H]([NH2+]C(C)(C)C(N)=O)C(C)(C)O1,4.587654623,0.1598,-4.427854623
Fc1cccc(C[NH2+][C@H]2CCC[C@@H](C(F)(F)F)C2)c1,4.065181131,3.0102,-1.054981131
COc1ccc(-c2nc(S(=O)(=O)c3ccccc3)c(NCc3ccc4c(c3)OCO4)o2)cc1,2.369919411,4.5238,2.153880589
CCN(C(=O)c1sc(NC(=O)c2ccco2)cc1C)[C@@H](C)C(C)C,3.067582502,4.40842,1.340837498
CC(=O)c1ccc(N2CCN(CC(=O)Nc3ccc(F)cc3)CC2)cc1,1.745600799,2.789,1.043399201
CC(C)(C)c1ccccc1NC(=O)CNc1cc2c(cc1Cl)NC(=O)CO2,2.379469962,4.019,1.639530038
COc1ccc(NC(=S)NC(=O)c2ccccc2OC(C)C)cc1Cl,1.95587331,4.2626,2.30672669
COCCn1ccc2cc(NC(=O)c3cccs3)ccc21,2.079198728,3.6015,1.522301272
CCOc1ccc(C(=S)NC[C@@H]2CCCO2)cc1,2.528851633,2.5294,0.000548367
CCCc1c(C(=O)NC2CCN(C(=O)OCC)CC2)cnn1-c1ccccc1,2.229874275,3.1755,0.945625725
C#Cc1cccc(NC(=O)NCCC[NH+]2CCCCCC2)c1,3.486046434,1.6384,-1.847646434
CCc1ccc([C@@H](O)[C@@]2(C)CCCO2)cc1,3.291365597,2.8515,-0.439865597
COC(=O)[C@@]1(F)CCN(C(=O)Cc2cccc(OC)c2)C1,2.848120431,1.3513,-1.496820431
CNC(=O)CC1CCN(C(=O)c2nc(-c3ccccc3)oc2C2CC2)CC1,2.470756132,3.2073,0.736543868
O=C1N[C@H](CO)C(=O)N2CCN(Cc3cnn(-c4ccccc4Cl)c3)C[C@H]12,3.293818928,0.0292,-3.264618928
CNc1nc(C)cc(-c2cccc3cnccc23)n1,2.234845867,3.04192,0.807074133
CCn1cc(-c2nnc(SCc3ccccc3Cl)n2C)cn1,2.291699181,3.6442,1.352500819
O=C(CSc1nc(-c2ccco2)nc2ccccc12)Nc1nccs1,2.346398845,4.0771,1.730701155
O=C1CCc2cc(S(=O)(=O)Oc3cccc(C(F)(F)F)c3)ccc2N1,2.341990367,3.3578,1.015809633
CC[C@@H](C)c1noc([C@H]2CCCN(C(=O)Cc3cccs3)C2)n1,3.228844727,3.5933,0.364455273
CCCOc1ccc(Br)cc1C[NH+]1CCC(c2noc(C)n2)CC1,3.614952536,2.89182,-0.723132536
CCNc1cc[nH+]cc1S(=O)(=O)[N-]c1ccc(C)[nH+]c1C,5.185505424,1.75754,-3.427965424
Cc1cc([C@H]([NH3+])C2CCCCCC2)sc1Br,3.776193085,4.07242,0.296226915
CC1(C)CCC[C@H]1NS(=O)(=O)c1ccc(N)cc1Cl,3.001833462,2.7792,-0.222633462
Cc1ccc(Cl)c(-n2nnc(C(=O)Nc3ccccc3F)c2C)c1,2.064234641,3.92894,1.864705359
CCOC(=O)[C@H]1CCCN(C(=S)Nc2ccccc2F)C1,2.457567813,2.7976,0.340032187
C=CC[C@H](C)[C@@H](C)[NH2+][C@@H](C)CS(C)(=O)=O,4.867243714,0.5836,-4.283643714
COc1cc([C@@H]2CC(O)=Nc3c(C(=O)[O-])cn(-c4ccc(Cl)cc4)c32)cc(OC)c1O,3.620591927,3.3406,-0.279991927
CCC([NH3+])(CC)C(=O)N[C@@H]1C[C@H]2C[C@@H]1[C@H]1CCC[C@@H]12,5.358506374,1.728,-3.630506374
O=C(CNC(=O)N1CCC(OCc2ccccc2F)CC1)N1CCCCC1,2.212233062,2.5288,0.316566938
COC(=O)c1ccc(Oc2ncnc(Oc3cccc4cccnc34)c2[N+](=O)[O-])cc1,2.403730151,4.3042,1.900469849
CC[NH2+]C[C@@H](O)CN1C(=O)CCCC1=O,4.011495477,-1.5303,-5.541795477
[NH3+][C@H]1C=C[C@@H](C(=O)NC2(CC(=O)[O-])CCCCC2)C1,4.700839766,-0.8679,-5.568739766
COc1cccc([C@H]2C(C(=O)c3ccc(Cl)cc3)=C([O-])C(=O)N2c2cc(C)on2)c1,3.126908422,3.23022,0.103311578
CC(C)CC[NH2+]Cc1cc(F)ccc1F,3.319744001,2.0743,-1.245444001
CC(C)C[C@H](NC(N)=O)C(=O)NCCc1ccc(Cl)cc1Cl,2.501281448,2.7351,0.233818552
Cc1nc(C(=O)N(C)Cc2ccc(C(F)(F)F)cc2)nn1-c1ccc(F)cc1,2.28443192,4.00582,1.72138808
CCCc1nnc(NC(=O)c2c(Cl)ccc(Cl)c2Cl)s1,2.26792167,4.7031,2.43517833
O=C([O-])c1ccc[nH+]c1NC[C@H](c1ccccc1)N1CCOCC1,3.813179172,0.3496,-3.463579172
COCCOc1ccc(CNC(=O)N2CCC[C@H](C)CC2)cn1,2.587574312,2.4384,-0.149174312
Cc1nccc2c1=C[C@@H](C(=O)N[C@@H](C)c1ccccc1)C(=O)[NH+]=2,4.094326362,-1.09548,-5.189806362
COc1ccccc1NC(=O)c1ccc(NC(=O)Cn2nc(C(=O)[O-])c3ccccc3c2=O)cc1,2.34156721,1.6596,-0.68196721
O=C(NC[C@@H]1COc2ccccc2C1)c1n[nH]c(=O)c2ccccc12,2.702355993,1.9042,-0.798155993
CC1(C)CCC[C@@H]1[NH2+][C@@H]1CCCC1(C)C,4.747288252,2.7072,-2.040088252
CC(C)n1nccc1C(=O)OC[C@H]1CN(Cc2ccccc2)CCO1,2.811890846,2.5218,-0.290090846
CCc1ccc([C@@H](Br)c2ccc3c(c2)C[C@@H](C)O3)o1,3.294719399,4.6497,1.354980601
Cc1ccc(S(=O)(=O)N2CCCCC2)cc1C(=O)N(C)CC[NH+](C)C,2.941929517,0.38612,-2.555809517
CC[C@@H]1CCc2c(sc(=O)n2CN2CC[NH+](C)C[C@@H]2c2ccccc2)C1,4.511671939,1.9538,-2.557871939
C=CCC[C@H](O)c1ccc(F)cn1,3.059250513,2.2203,-0.838950513
CC(C)(C)NC(=S)N/N=C/c1cccnc1,2.423634839,1.6781,-0.745534839
CC1CCN(C(=O)c2ccc(CSc3nc4ccccc4[nH]3)cc2)CC1,2.05068455,4.7273,2.67661545
Cc1c(C)n(-c2ccc(Br)cn2)c2nc[n+](Cc3ccncc3)c(N)c12,2.971114521,3.11284,0.141725479
COc1ccc([C@H]2Nn3c(C)nnc3S[C@@H]2C(=O)N2CCCC2)cc1,3.304234512,1.97662,-1.327614512
Cc1ncc(CC(=O)N2CCc3c(c(C(=O)[O-])nn3CC3CC3)C2)c(=O)[nH]1,3.159885346,-0.82428,-3.984165346
COCc1nc2n(n1)CCC[C@@H]2NC(=O)Nc1cnccc1C,3.196257529,1.78452,-1.411737529
Cc1ccc(NC(=O)C[C@H](c2cccc(C)c2)n2cccc2)cc1,2.522819487,4.72314,2.200320513
CC1CCN(C(=O)CN2CCN(c3cc(C#N)ccn3)CC2)CC1,2.242431466,1.33378,-0.908651466
N#Cc1ccnc(N2CCC(CCC(=O)NCc3cccc(Cl)c3)CC2)c1,2.250365015,3.91968,1.669314985
COCC[C@@H](C)C(=O)Nc1cccc(Oc2ccncc2)c1,2.484806648,3.485,1.000193352
O=C(Cn1nc(C(=O)[O-])c2ccccc2c1=O)Nc1ccc2c(c1)C(=O)c1ccccc1C2=O,2.536245035,1.1741,-1.362145035
CC(C)(C)c1csc(CNC(=O)N[C@@H]2CCc3c(O)cccc32)n1,3.019480995,3.6329,0.613419005
CCn1cncc1[C@@H]1OCC[C@H]1C[NH2+]C(C)(C)C,4.646004856,1.3425,-3.303504856
O=C(CN1C(=O)N[C@]2(CCCc3sccc32)C1=O)N[C@H](c1ccccc1)c1cccs1,3.709928512,3.7988,0.088871488
Cc1ccc(C(=O)N[C@H](CC[NH+](C)C)c2ccc(Cl)cc2)s1,3.43628494,2.71562,-0.72066494
C/C=C/C[S@@](=O)Cc1nc(-c2cccs2)oc1C,3.517488633,3.53632,0.018831367
Nc1nonc1-c1noc(COc2ccc(Br)cc2)n1,2.399600676,2.0433,-0.356300676
COC[C@H]1CCCN(C(=O)N[C@@H]2C[C@](C)(OC)C2(C)C)C1,3.874154216,2.258,-1.616154216
C[NH+](C)[C@H]1CC[C@H](NC(=O)N(Cc2c(F)cccc2Cl)C2CC2)C1,3.994602094,2.2187,-1.775902094
CC(=O)/N=C1\S[C@@H]2CS(=O)(=O)C[C@@H]2N1c1ccc(Br)cc1Cl,3.526675094,2.7238,-0.802875094
Cc1c(Cl)cccc1S(=O)(=O)N1CCC(C(N)=O)CC1,2.029710667,1.53442,-0.495290667
CN(Cc1ncnn1C)C(=O)[C@@H]1CC(=O)N(CC(F)(F)F)C1,3.223841813,0.1843,-3.039541813
CC(=O)/C=C(/NNC(=O)c1cccc([N+](=O)[O-])c1)c1ccccc1,2.269320979,2.4593,0.189979021
CC(C)c1cc(C(=O)NC[C@@H]2CCC[NH+](C(C)C)C2)n(C)n1,4.366273556,0.9766,-3.389673556
CC[C@](C)([NH3+])CO[C@H]1CCOC2(CCCCC2)C1,4.663770551,2.2955,-2.368270551
Cc1ccc(-n2nc(C(=O)NC[C@H]3CCCO3)c(=O)n(Cc3cccc(F)c3)c2=O)cc1Cl,2.883985307,2.45222,-0.431765307
C[C@H](Cc1cccs1)N(C)C(=O)NCC1CC1,2.835861306,2.7305,-0.105361306
COc1ccc(C(=O)N[C@@H](Cc2ccccc2)c2nc3ccccc3[nH]2)cc1[N+](=O)[O-],2.584541779,4.1935,1.608958221
CCCn1c(N)c(Br)c(=O)n(CC(=O)NC)c1=O,2.537467799,-0.4893,-3.026767799
Cc1nc(/C=C/C(=O)Nc2nc(-c3ccccc3)ns2)cs1,2.462739944,3.62192,1.159180056
Cc1ccccc1OCCCC(=O)NCc1ccccc1,1.537882208,3.47042,1.932537792
CC[C@H](C)OCc1ccc(C(=O)NN)cn1,2.8132977,1.0002,-1.8130977
CCCc1noc(-c2cc(S(=O)(=O)NC)ccc2OC)n1,2.261944947,1.6058,-0.656144947
Cc1cc(C)n2cc(CN3C[C@@H](C)C[C@H]([NH3+])C3)nc2n1,4.026418302,0.79844,-3.227978302
Cc1noc(C)c1[C@@H](C)NC(=O)NCCCC[NH+]1C[C@@H](C)C[C@H](C)C1,4.640605204,1.99264,-2.647965204
C[C@@H](NC(=O)C(C)(C)C)C(=O)Nc1ccc(F)c(C(=O)N(C)C)c1,2.557460441,2.0168,-0.540660441
O=c1oc2cccnc2n1CC[NH+](Cc1ccccc1F)C1CCCCC1,3.845392421,2.5464,-1.298992421
COc1cccc(CCC(=O)N2CCC(OCc3ccccc3F)CC2)c1,2.033454364,3.9747,1.941245636
CCn1c(N/N=C/c2ccc(N(C)C)cc2)nc2c1c(=O)[nH]c(=O)n2C,2.59704872,0.9552,-1.64184872
Cc1cc2n(n1)CCC(=O)N2[C@@H](C)C(=O)NCc1ccccn1,3.100623436,1.02812,-2.072503436
Cc1ccc(CC(=O)NC[C@@H]2COc3ccccc3O2)cc1,2.34855287,2.49372,0.14516713
CC[C@H]1COCCN1Cc1c[nH]nc1-c1ccccc1F,3.202033955,2.8266,-0.375433955
CC[C@H](C)N1C(=O)c2ccccc2N[C@@H]1c1ccc(Cl)c([N+](=O)[O-])c1,3.14156896,4.6132,1.47163104
CS(=O)(=O)c1ccc(NC(=O)Cn2c(-c3ccccc3)cc3ccccc32)cc1,1.961669185,4.3505,2.388830815
CS[C@@H]1CC[C@@H](NC(=O)/C=C/c2ccc(OCc3cccnc3)cc2)C1,3.120059729,4.0741,0.954040271
CS[C@H](CO)[C@@H](C)NC(=O)NCc1cc(=O)[nH]c2ccccc12,3.361951895,1.4397,-1.922251895
O=C(NCC1CC1)C1([NH2+]Cc2cnn(-c3ccccc3)c2)CCCC1,3.257340383,1.7747,-1.482640383
CC(C)[C@@H]1CN(C(=O)NC[C@H]2CC[NH+](C3CC3)C2)CCS1,5.10628001,0.8366,-4.26968001
[NH3+]C[C@H](CC(=O)NC1CC1)N1CCn2cnnc2C1,3.791785348,-1.6271,-5.418885348
O=C(NN1Cc2ccccc2C1)c1cnn(-c2ccccc2)n1,2.463468958,1.9279,-0.535568958
COc1ccc(S(=O)(=O)[N-]c2ccc(Cl)c(Cl)c2)cc1[N+](=O)[O-],3.09463754,4.3043,1.20966246
O=C(NCC[C@@H]1C[C@H]2CC[C@@H]1C2)C(=O)Nc1nc(-c2ccccc2)cs1,3.907753927,3.6911,-0.216653927
CCCS(=O)(=O)c1ncc(Cl)c(C(=O)Nc2sc3c(c2C(=O)OC)CCC3)n1,2.655303848,2.9028,0.247496152
CC(C)(C)c1csc(CNc2cc(F)cc(F)c2)n1,2.393327346,4.3309,1.937572654
CCOc1ccc(OCC(=O)N2CCN(c3nccn3-c3cccc(Cl)c3)CC2)cc1,2.239043704,3.652,1.412956296
CNC(=O)[C@@H]1C[C@H]([NH3+])CN1Cc1ccc(-n2cccn2)cc1C,3.745433108,0.11152,-3.633913108
C[C@@H](Cc1nc2ccccc2s1)NCc1ncn(C)n1,3.139160511,2.1456,-0.993560511
CCC(CC)n1ccc(Cn2nnc(N)c2C(C)(C)C)n1,3.056509207,2.7637,-0.292809207
CCC[C@H]1CN(C(=O)[C@H]2C[C@@H]2c2ccccc2C)CCO1,3.194344378,3.12602,-0.068324378
C[C@H]1CCCC[C@@H]1OCCCOc1ccccc1C[NH3+],3.348375729,2.7927,-0.555675729
O=c1oc(-c2ccccc2)nn1CN1CCN(c2ccc(F)cn2)CC1,2.311079692,1.8171,-0.493979692
O=C(CCc1ccco1)NCc1cnn(-c2ccccc2)c1,2.047571641,2.7143,0.666728359
COc1ccc2ccccc2c1/C=C1/N=C(c2cccc([N+](=O)[O-])c2)OC1=O,2.34548966,4.1011,1.75561034
O=C([O-])[C@H]1CCO[C@H]1Cc1ccccc1,3.392413353,0.3841,-3.008313353
O=C([O-])C1[NH+]=c2ccccc2=[NH+]1,5.228431607,-5.8234,-11.05183161
C=CCN1C(=O)N=C(N)[C@@H]1c1ccc(OC(C)C)cc1,3.176426978,2.4937,-0.682726978
Cc1n[nH]c(SCC(=O)NC2(C)Cc3ccccc3C2)n1,2.992188236,1.87892,-1.113268236
C[C@H](NC(=O)NC[C@@H]1C[NH+](C)CCN1C)c1ccc(C(C)(C)C)cc1,4.117589731,1.173,-2.944589731
C=CCN1CCN(S(C)(=O)=O)[C@@H](C(=O)NCc2ccccc2)C1,2.679932748,0.4346,-2.245332748
O=S(=O)(c1cccn2c(SCc3c(F)cccc3Cl)nnc12)N1CCCC1,2.468769119,3.5986,1.129830881
CN[C@@H](c1c[nH+]ccc1N)[C@@H]1CN2CCC[C@@H]2CO1,4.894342078,0.2066,-4.687742078
C[C@@H](Sc1nc2ccccc2n1C)C(=O)N(C)Cc1ccccc1,2.431248545,3.7125,1.281251455
C[C@H]1CCCC[C@@H]1[NH+](C)Cc1cc(F)cc(C#CC[NH3+])c1,5.04524194,1.0125,-4.03274194
O=C(Nc1ccc2c(c1)CCCN2S(=O)(=O)c1ccccc1)c1cc2ccccc2o1,2.17677755,4.8266,2.64982245
C[C@@H]1CCCC[C@H]1[NH2+][C@@H]1CCN(C2CCCCC2)C1=O,4.263772335,2.0621,-2.201672335
CCc1nn(C)c2c1nc(N)n2[C@H](C)c1ccccc1F,3.276272156,2.6628,-0.613472156
CC(C)CNC(=O)CNC(=O)C(=O)Nc1ccc2ccn(C)c2c1,2.263583154,1.0052,-1.258383154
Cc1nc(-c2nnc(NC(=O)c3ccc(S(=O)(=O)N4CCC[C@H](C)C4)cc3)o2)cs1,2.887521357,3.17442,0.286898643
O=C(NCCc1ccccc1)[C@H]1CCCN(C(=O)c2cccc(Cl)c2)C1,2.231072868,3.5511,1.320027132
CC1(C)C[C@H]([NH2+]C[C@@H]2CCC[C@@H]2NS(C)(=O)=O)C(C)(C)O1,4.794950037,0.6138,-4.181150037
Cc1ccc2cccc(NC(=O)[C@H](C)SCc3nc4scc(-c5ccccc5)c4c(=O)[nH]3)c2n1,2.981012578,5.76862,2.787607422
CN(C(=O)CS(=O)(=O)c1nnc(N2CCCC2)s1)C1CC1,2.737794284,0.5328,-2.204994284
CC(C)c1ocnc1C[S@@](=O)[C@@H](C)c1ccc(F)c(F)c1,3.919654427,4.0861,0.166445573
C/C(Cn1nnc2ccccc21)=N\NC(=O)c1ccc(Br)cc1,2.193402011,2.9997,0.806297989
CCOc1ccc(-n2ccnc(SCC(=O)Nc3ccccc3Cl)c2=O)cc1,2.118518314,4.0154,1.896881686
Cc1ccc(OC(F)F)c(CN2CCN(c3nc(C)ns3)CC2)c1,2.451202487,3.07854,0.627337513
Cc1cc2ncc([C@H](C)NC3CC[NH+](Cc4ccccn4)CC3)c(C)n2n1,4.106032583,1.63924,-2.466792583
CC[NH2+][C@@]1(C(=O)[O-])CC[C@@H](n2cnc3ccccc32)C1,4.410980043,-0.1667,-4.577680043
CCCN1C(=O)CCc2cc(NC(=O)N(CC)CC(C)(C)O)ccc21,2.532985399,3.0005,0.467514601
COC(C)(C)c1noc(-c2cc(Br)cn2C(C)C)n1,3.212215459,3.763,0.550784541
Cc1nc(NC[C@H](c2ccc(F)cc2)[NH+]2CCCC2)cc(-c2cc[nH]n2)n1,4.361277329,2.14612,-2.215157329
C[C@@H](O)CCC(=O)Nc1cccc(NC(N)=O)c1,2.34605947,1.2767,-1.06935947
CC(C)(C)c1csc([C@H]2CN(Cc3ccc(O)cc3O)CCO2)n1,3.143730594,3.4253,0.281569406
Cc1ccc(-c2c[n+](-c3cccc(C(F)(F)F)c3)c3n2CCCCC3)cc1,2.729851836,5.48542,2.755568164
O=c1[nH]c(CCl)nc2ccc(O)cc12,2.419924373,1.3675,-1.052424373
COc1cc([C@H](C)N[C@@H]2CCCc3nc(C)sc32)ccc1F,3.302915814,4.32742,1.024504186
Cc1ccc([N-]S(=O)(=O)[C@@H]2CCC[NH2+]C2)c(C)[nH+]1,6.031753882,0.17834,-5.853413882
Cc1ccccc1-n1nc2c(c1NC(=O)c1cc([N+](=O)[O-])ccc1Cl)CS(=O)(=O)C2,2.635545395,3.42302,0.787474605
Cc1ccc(NC(=O)C[C@@H]2C(=O)Nc3nc(-c4ccccc4)nn32)cc1C,2.991716945,3.08394,0.092223055
CC(C)[C@H]1CC[C@@H](C)C[C@H]1[NH2+]CC1(CS(C)(=O)=O)CC1,4.753834564,1.8354,-2.918434564
O=C(NCCc1nnc(-c2ccccc2)o1)c1ccc2c(c1)C(=O)NC2=O,2.221256139,1.5927,-0.628556139
Cc1ncc(CC(=O)N2CCC(=O)N(CC3CC3)[C@@H](C(C)C)C2)c(=O)[nH]1,3.25717113,1.11632,-2.14085113
CC[NH2+][C@@]1(C(=O)[O-])CCC[C@@H]1CCN1CCS(=O)(=O)CC1,4.746850504,-2.021,-6.767850504
Cc1ccccc1NN/C=C1/C(=O)NC(=O)c2ccccc21,2.204343137,2.22262,0.018276863
CCN(CCC(=O)NC1CC1)C(=O)c1cnc(C)cn1,2.306046425,0.91582,-1.390226425
Cc1cccc([C@H]2CCC[NH2+]2)c1,4.094583094,1.39332,-2.701263094
CS(=O)(=O)N1CCc2cc(C(=O)Nc3cc(Br)ccc3Br)ccc21,2.207749587,3.786,1.578250413
CC(C)Cn1ncc(C(=O)N(C)[C@@H](C)Cc2ccsc2)c1C(F)F,3.336934928,4.2414,0.904465072
Cc1ccc(F)c(NC(=O)C(=O)NCCC[NH+]2CCCCCC2)c1,3.312323302,1.03782,-2.274503302
Cc1cc(NC(=O)[C@H](C)NS(=O)(=O)c2cccc(Cl)c2)ccc1N1CCCC1=O,2.630654339,3.08072,0.450065661
O=C(Cn1nc(-c2ccccc2F)ccc1=O)NC[C@@H]1CCCO1,2.613433534,1.3446,-1.268833534
CC(C)COC(=O)N1CCC([NH2+]CCc2nnc3ccccn23)CC1,3.358366621,1.0922,-2.266166621
CC(=O)N1CC[C@@H]([NH2+][C@H](C)c2c[nH]c3ccc([N+](=O)[O-])cc23)C1,4.043087472,1.3213,-2.721787472
Cc1cnc(C(=O)N2CCC(c3[nH]ncc3-c3ccncc3)CC2)cn1,2.56977868,2.58992,0.02014132
CCCNC(=O)CN1CCC([NH2+][C@H](C)c2ccc(F)cn2)CC1,3.633696098,0.8357,-2.797996098
C1CCC([NH2+]CC[C@H]2C[NH2+]CCO2)CC1,5.438513113,-0.7652,-6.203713113
CCOC(=O)C1(C)CCN(C(=O)CSc2ccccc2Cl)CC1,2.317976552,3.6239,1.305923448
CCN(CC)C(=O)[C@@H]1CCC[NH+]1Cc1cnc(Cl)s1,4.76589579,1.2122,-3.55369579
CCOc1ccccc1N/C(=C\C(=O)OC)C(=O)NC,2.268032439,1.3001,-0.967932439
CNC(=O)COc1ccccc1-c1noc(-c2ccc(S(=O)(=O)N(C)C)o2)n1,2.535585059,1.3717,-1.163885059
COC(=O)CSc1cc(Cl)ccc1Cl,1.950704472,3.2585,1.307795528
COC(=O)[C@@H]1C(=O)C2=C(C[C@@H]1C)Nc1ccccc1N[C@@H]2c1ccc(Cl)c(F)c1,3.528144568,4.71,1.181855432
COCC[C@@H](C)C(=O)Nc1ccccc1S(=O)(=O)C(F)F,2.730644768,2.294,-0.436644768
CCN(c1ccc(C(=O)NCc2ccc([S@](C)=O)cc2)cc1)C(C)C,2.670970361,3.5887,0.917729639
CC[C@@H]1CCCC[C@H]1[C@H]([NH3+])c1c(F)cccc1F,3.827628952,3.4642,-0.363428952
Cc1cccc(C(=O)NN2Cc3ccccc3C2)c1F,2.30064616,2.79472,0.49407384
CCn1c(=O)n(CC(=O)Nc2cc(OC)ccc2OC)c(=O)c2ccccc21,2.001306361,1.839,-0.162306361
Cc1cc(=O)c(C(=O)N2CCC[C@H](Cn3cc(CC4CCCCC4)nn3)C2)c[nH]1,3.262503707,2.95002,-0.312483707
Cc1c(-c2ccnc(N3c4ccccc4C[C@H]3C)n2)nnc2nc(-c3ccco3)nn12,3.333401872,3.62742,0.294018128
Cc1ccc(NC(=O)c2cnn(-c3ccccc3)c2C(F)(F)F)cc1S(=O)(=O)N(C)C,2.297134473,3.70212,1.404985527
CC[C@@H](CSC)N(C)C(=O)Nc1ccc(C(=O)N(C)C)c(C)c1,2.971901104,3.30212,0.330218896
COc1cc2c(cc1NC(=O)c1cc(OC)c(OC)c(OC)c1)CCCC2,2.044842359,3.8521,1.807257641
COc1cc(C)c(Br)cc1[C@@H](Cl)[C@H]1CCO[C@@H]1C,3.81993083,4.47102,0.65108917
Cc1cnc(SCC[NH+]2CCOCC2)nc1[C@@H]1CCCN(C(=O)/C=C/c2cccnc2)C1,4.141789854,1.60662,-2.535169854
Cc1ccc(NC(=O)CCn2nnc3cc(C(=O)NCc4cccc(Cl)c4)ccc32)cc1,2.111270605,4.35192,2.240649395
Cc1ccc(SCCC(=O)NC[C@@H]2CN3CCCC[C@@H]3CO2)cc1,3.075083992,2.84672,-0.228363992
Cn1cc(CNC(=O)c2cc(-c3ccccc3)nc3ccc(Br)cc23)cn1,2.113376849,4.3278,2.214423151
Cc1cccn2c(=O)c(C(=O)Nc3ccc([S@](C)=O)c(F)c3)cnc12,2.962959658,2.13172,-0.831239658
Cc1nonc1C(=O)NCc1csc(=O)[nH]1,3.075637706,0.05782,-3.017817706
COc1cc2[nH]c(C(=O)N3CCN(c4ccc(F)cc4)CC3)cc2c(OC)c1OC,2.277064802,3.2952,1.018135198
O=C([O-])[C@H]1C[C@@H]2CCCC[C@@H]2N1C(=O)[C@@H]1CCS(=O)(=O)C1,4.106939223,-0.6693,-4.776239223
CCn1c([C@@H](C)[NH2+]CC2([C@H](O)c3ccccc3)CC2)nc2ccccc21,3.791835426,3.1944,-0.597435426
CCSC[C@H](C)N(C)C(=O)CCc1c[nH]c2ccccc12,2.930423354,3.7005,0.770076646
COc1ccc2c(C)c(CCC(=O)Nc3c(C(C)C)cccc3C(C)C)c(=O)oc2c1,2.335432391,5.92812,3.592687609
O=C(NNC(=O)c1cccc(NC(=O)c2ccco2)c1)c1ccccc1,1.806007951,2.6067,0.800692049
Cc1ncc([C@H](C)NC[C@H]2CN3CCCC[C@@H]3CO2)c(C)n1,3.635850378,1.99734,-1.638510378
CC[C@H]1CCCC[C@H]1n1cc(C(=O)[O-])c(=O)[nH]c1=O,3.611392278,0.0414,-3.569992278
N#C[C@H](C(=O)c1cccc(N2CCCC2=O)c1)c1ccc(C(F)(F)F)cn1,2.994926879,3.71728,0.722353121
CCOc1cccc([C@@H](C)[NH2+][C@H](C)Cn2cnc(C)c2C)c1,3.8512171,2.61174,-1.2394771
O=C(OCc1nnsc1Cl)c1cccc(N2CCCS2(=O)=O)c1,2.690322495,2.0884,-0.601922495
Cc1cc(NC(=O)c2cc(Cl)ccc2Cl)n(C(C)C)n1,2.145072206,4.33152,2.186447794
Cc1ccc(OC[C@@H](O)C[NH2+]C[C@@]2(C)CCCO2)cc1,3.972963039,0.86722,-3.105743039
CSC(C)(C)CNC(=O)c1ccc(S(=O)(=O)NC2CC2)cc1,2.376249042,1.9987,-0.377549042
Cc1ccccc1-n1nnnc1[C@@H](c1ccccc1)N(C)Cc1cccc([N+](=O)[O-])c1,2.833420949,4.10032,1.266899051
COc1cc(C)c(NC(=O)N2CC[C@@H](C)C[C@@H]2C)cc1OC,2.80715971,3.66452,0.85736029
CCC[NH2+]CC/C=C(/C)c1ccc(C)c(C)c1,3.433729102,3.07024,-0.363489102
CS(=O)(=O)c1nc(C(=O)Nc2ccc3c(c2)Cc2ccccc2-3)c2ccccn12,2.440662697,3.5613,1.120637303
O=C(N[C@@H](c1ccc2c(c1)OCCO2)C1CC1)NC1(c2noc(C3CC3)n2)CCCC1,3.346508313,3.938,0.591491687
CC(=O)Oc1ccccc1-c1nc2s/c(=C\c3cc(C)c(OC(C)=O)c(C)c3)c(=O)n2n1,2.667587844,2.83314,0.165552156
O=C(NCC(=O)N1CCN(C(=O)c2ccccc2)CC1)c1ccc(Br)o1,2.03066504,1.7565,-0.27416504
O=C(NC1CCCC1)[C@@H]1CCCN1C(=O)Nc1cccc(Cl)c1,2.401089697,3.3951,0.994010303
Cc1c(C(=O)N2CCC3(CC2)C(=O)NCCC[NH+]3C)cnc2ccnn12,4.613407565,-0.95288,-5.566287565
CC[C@@H](C)CN(C)c1ccccc1C[NH2+]C1CC1,3.802154805,2.3947,-1.407454805
O=C(Nc1ccnn1Cc1cccc(Cl)c1Cl)c1cccc(S(=O)(=O)NC2CCCC2)c1,2.387381354,4.7114,2.324018646
COc1ccc(C(=O)Nc2ccccc2C(=O)Nc2ccccn2)cc1,1.688012874,3.5948,1.906787126
CC(=O)Nc1cccc(-n2cc3c(c2-c2ccccc2Cl)c(=O)n(C)c(=O)n3C)c1,2.439185247,3.3067,0.867514753
Cc1ccc2nc(C)c(C(=O)NCC(C)(C)c3ccncc3)cc2c1,2.306965961,3.95424,1.647274039
CSc1ccsc1C(=O)NC1(c2cccc(Cl)c2)CC1,2.584660576,4.5425,1.957839424
O=C(OCc1nnc(-c2ccccc2Cl)o1)C1CCOCC1,2.256423598,2.8598,0.603376402
COc1ccc(-c2csc(NC(=O)CSc3nc4ccccc4[nH]3)n2)cc1OC,2.179890746,4.4344,2.254509254
CCc1cc([C@@H]2CCC[NH2+]2)cc(CC)c1Cl,4.274119239,2.8631,-1.411019239
Cc1nc2sc3c(NC[C@H]4CCCO4)ncnc3c2c2c1COC(C)(C)C2,3.430342051,3.99012,0.559777949
Cc1cncc(NC(=O)NCCc2cccc(F)c2)c1,1.947457496,2.89332,0.945862504
CN(C)c1ccc(NC(=O)[C@@H]2CCCN2C(=O)C2CC2)cn1,2.57703349,1.4871,-1.08993349
O=C1O[C@H](CCN2CCOCC2)CN1Cc1ccccc1,2.614376716,1.7297,-0.884676716
O=C(Cn1c(Cl)nc2ccccc21)c1ccc2c(c1)OCCO2,2.226699819,3.3438,1.117100181
CC(=O)N[C@H](CC(=O)Nc1nc[nH]n1)c1ccc(C)cc1,3.10270471,1.31912,-1.78358471
CCC[C@@]1(CC)NC(=O)NC1=O,3.343567709,0.7747,-2.568867709
CC(C)[C@@H](O)c1ccn(CCC(C)(C)C)c1,3.326211986,3.6137,0.287488014
O=C(CNS(=O)(=O)c1ccccc1Cl)NCc1ccco1,2.015111156,1.5277,-0.487411156
CCSC1=C(C#N)[C@H](CC(C)C)C2=C(CCCC2=O)N1,3.509414391,3.74728,0.237865609
C[C@H]1CC[C@H](NC(=O)NC[C@](C)(O)c2ccsc2)[C@H](C)C1,3.828507393,3.0795,-0.749007393
O=C(Cc1ccc(-n2cccn2)cc1)Nc1cccc(CN2CCOC2=O)c1,2.246151822,3.0057,0.759548178
COCCSc1ccccc1C(=O)N1C[C@H](C)O[C@@H](C)C1,2.919174745,2.6745,-0.244674745
CN[C@@H]1[C@@H](C[NH+](C)CCO)C(C)(C)OC1(C)C,5.466499996,-0.715,-6.181499996
CC(C)COC1CCN(C(=O)/C=C/c2cccc3cccnc23)CC1,2.385662775,3.9116,1.525937225
Cc1cc(-n2c(C)cc(C(=O)CN3C(=O)NC(c4ccccc4)(c4ccccc4)C3=O)c2C)no1,2.659919657,4.06886,1.408940343
CCC[NH2+][C@@H](CC[C@H]1CCCO1)[C@@H]1C[NH+]2CCN1CC2,6.424619424,-1.1297,-7.554319424
CC[C@@H](C)NC(=O)[C@H](NC(=O)c1cccc(C)c1)C1CCN(C(=O)c2ccccc2)CC1,2.852569634,3.56052,0.707950366
Cc1ccc([C@H]2CSCCN2C(=O)N[C@H]2CC[C@H]([NH+](C)C)C2)cc1,4.245038493,1.86012,-2.384918493
C[C@H](NC(=O)c1ccnn1C)C12CC3CC(CC(C3)C1)C2,4.194819005,2.7548,-1.440019005
Cn1ncnc1CCNC(=O)N1CCC[C@H]1Cc1ccccc1,2.744029526,1.7743,-0.969729526
O=C([O-])COc1cc(-c2ccncc2)nc(N2CCN(c3ccccc3F)CC2)n1,2.663513821,1.133,-1.530513821
N#C[C@H]1CNCCN1C(=O)c1ccsc1,3.291177871,0.68568,-2.605497871
CC[NH+]1CC[C@H](N(C)CC(=O)OC(C)(C)C)[C@H](C)C1,4.803468008,0.5731,-4.230368008
O=C(Cc1ccc2c(c1)OCCCO2)NCCNC(=O)[C@H]1C=c2ccccc2=[NH+]1,3.610596228,-1.8141,-5.424696228
C[C@@H]1CCN(C(=O)NCCn2c(=O)[nH]c3ccccc32)C[C@@H]1O,3.115989084,0.7419,-2.374089084
CCC1(CC)CC(=O)N(CCOc2ccccc2)C1=O,2.279792993,2.6307,0.350907007
CSc1cccc(NC(=O)[C@@H]2CCCN2C(=O)c2cccs2)c1,2.519958928,3.7133,1.193341072
CCOC(=O)c1ccc(N2C(=O)c3oc4ccc(F)cc4c(=O)c3[C@@H]2c2ccc(SC)cc2)cc1,2.869982169,5.5805,2.710517831
Cc1ccsc1CN[C@@H]1COC[C@@H]1O,3.438656169,0.90582,-2.532836169
CO[C@H](C)C(=O)Nc1cccc(NC(=O)N(C)[C@@H](C)c2cccnc2)c1,2.920062765,3.2799,0.359837235
CN(C(=O)c1ccc2c(c1)CCO2)C1CCC(=O)CC1,2.371567617,2.2052,-0.166367617
CC(C)c1csc(/C(C#N)=C\c2csc(-c3ccc(F)cc3)n2)n1,2.706482804,5.59328,2.886797196
COc1ccc(CC(=O)N2CCO[C@@H](COCc3cccc(C)c3)C2)cc1,2.523511281,2.99032,0.466808719
O=C(COc1ccc(Cl)cc1)N[C@@H](c1ccccc1)c1cccs1,2.260327084,4.6861,2.425772916
CCOC(=O)[C@H]1C(=O)C(=O)N(c2ccccc2)[C@@H]1c1ccc([N+](=O)[O-])cc1,2.954844007,2.4311,-0.523744007
C[NH+](C)CCCOc1ccc(NC(=O)[C@H]2CCC(=O)O2)cc1,3.481344542,0.2441,-3.237244542
CC(C)(C)OC(=O)N(CCNC(=O)N1CCC([NH+]2CCCC2)CC1)C1CC1,3.689186885,1.2386,-2.450586885
CC1=C[C@@H](c2ccc(S(=O)(=O)Nc3ccc(Cl)cc3C)o2)N=N1,3.54172818,4.45292,0.91119182
Cc1nn(-c2ccccc2)c(COC(=O)C23C[C@@H]4C[C@@H](CC(O)(C4)C2)C3)c1C#N,4.885278242,3.4269,-1.458378242
CC(C)C(=O)Nc1cccc(NC(=O)C(=O)NCC(C)(C)c2ccncc2)c1,2.297021131,2.7086,0.411578869
Cc1cc(C)n2nc(C)c(C(=O)O[C@@H](C)[C@@H]3CCCO3)c2n1,3.472079899,2.37886,-1.093219899
CCCN[C@]1(C#N)CC[C@@H](Sc2nc(C)c(C)c(C)n2)C1,3.865569071,3.30844,-0.557129071
CCOC(=O)C1(c2nc([C@H]3CC[C@H](C)C3)no2)CCCC1,3.496369973,3.3481,-0.148269973
CCCNC(=O)c1ccc2c(c1)c(C)c(C)n2C,1.967924688,2.93494,0.967015312
CCCOc1ccc(NCC(=O)Nc2ccc(F)cc2)cc1,1.632813467,3.6651,2.032286533
S=c1n(CN2CCSCC2)nc(N2CCCCC2)n1-c1ccccc1,2.583555394,3.39989,0.816334606
Cc1cc(C(F)F)nn1CC(=O)N1CC(c2ccncc2)C1,2.61704593,2.15012,-0.46692593
O=[N+]([O-])c1ccc(Cl)c(S(=O)(=O)/N=C([O-])/C=C/C2CC2)c1,3.166163675,1.6619,-1.504263675
CC[C@@H](C)n1ncc(C(=O)N2CCO[C@H](C#N)C2)c1C1CC1,3.768596419,2.09608,-1.672516419
O=C(NC(C1CC1)C1CC1)N1CC[C@@H](Oc2ccc(C(F)(F)F)cn2)C1,3.144221022,3.4517,0.307478978
C[C@H]1CCCC[C@@H]1OCCN(C)Cc1nc(=O)c2ccccc2[nH]1,3.075784642,2.9502,-0.125584642
COc1ccc(Cl)cc1S(=O)(=O)N1CCN(C(=O)c2cc3ccccc3oc2=O)CC1,2.197795481,2.6017,0.403904519
C[C@@H]1CCN(c2ncc([N+](=O)[O-])cc2Cl)[C@H](C)C1,3.208323663,3.268,0.059676337
Cc1nc(N2CCN(C(=O)c3cccc(F)c3)CC2)cc(-n2cnc(C)c2C)n1,2.473499443,2.68906,0.215560557
COC(=O)[C@H]1CCCCN1C[C@H](O)COc1ccc(Cl)cc1Cl,2.853293962,2.7606,-0.092693962
COC[C@](C)(CCO)[NH2+]Cc1ncccc1C,4.061519898,0.24092,-3.820599898
O=C(NC1CCCCC1)C1CCN(C(=O)c2cc3cc(Cl)cnc3[nH]2)CC1,2.420386909,3.5174,1.097013091
CC(C)(C)N(CCC(=O)[O-])C(=O)N1CC[NH+]2CCC[C@H]2C1,5.296421228,-1.2902,-6.586621228
COC(=O)c1cc(NC(=O)NCc2ncc[nH]2)ccc1C,2.150937232,1.82642,-0.324517232
CN(Cc1ccc(C#N)cc1)C(=O)NCCN(C)c1ccccc1,2.099813573,2.83608,0.736266427
CCCNC(=O)C1CCN(C(=O)c2cccc(NS(=O)(=O)c3ccc4c(c3)CCCC4)c2)CC1,2.239853257,3.7446,1.504746743
O[C@H]1Cc2ccccc2[C@H]1[NH2+]Cc1cnc(-c2ccsc2)s1,4.127840834,2.5933,-1.534540834
N#Cc1cc([N+](=O)[O-])ccc1Oc1ccc(/C=N\NC(=O)c2ccccc2-n2cccc2)cc1,2.503753463,4.81338,2.309626537
C[C@H]1C[C@H](C)CN(C(=O)[C@@H](C)S(=O)(=O)c2nc3ccccc3[nH]2)C1,3.481529855,2.2296,-1.251929855
CCN(C(=O)CN1CC[NH+](CC2CC2)CC1)c1cccc2ccccc12,3.297787028,1.8032,-1.494587028
Cc1ccc(Nc2nc3c(c(=O)[nH]2)CCCC3)c(F)c1,2.46036033,2.83982,0.37945967
CCC[C@H]1[C@H](C)CCCN1C(=O)NCCCCSC,3.291429317,3.7398,0.448370683
Cc1noc(C)c1CN1C(=O)N(c2cccc(Cl)c2)c2ncccc2S1(=O)=O,2.790302811,3.80244,1.012137189
CCC[NH2+][C@@H](C)c1ccc(SCC(C)C)cc1,3.752956887,3.4691,-0.283856887
C[C@@H](C(=O)NCCc1ccc(F)cc1)[NH+](C)Cc1ccc(Cl)nc1,3.667307699,1.6362,-2.031107699
CC(C)(C)N1C[C@@H](C(=O)n2ccnc2)CC1=O,3.241710455,1.1703,-2.071410455
CN(c1ccc(C(=O)Nc2ccccc2C(=O)N2CCCC2)cc1)S(C)(=O)=O,1.980031213,2.5707,0.590668787
Nc1ccc(=O)n(CCN2CC[NH+]3CCC[C@@H]3C2)c1,4.656713618,-1.2066,-5.863313618
Cc1cc(NC(=O)C(=O)N2CCN(c3ccc(Br)cn3)CC2)no1,2.379687103,1.42782,-0.951867103
Cc1ccc(C(=O)N2CCN(C(=O)CN3CCOCC3)CC2)s1,2.130681032,0.67312,-1.457561032
COc1ccc(C(=O)NCc2ccc(C[NH+]3CCC[C@H](C)C3)cc2)cc1[N+](=O)[O-],3.752516678,2.3482,-1.404316678
C[NH+](Cc1ccc(OC(F)(F)F)cc1)C[C@H]1CCOC1,4.01571473,1.6364,-2.37931473
COC(=O)c1ccc(C(=O)N2CCC(Oc3nc4c(C)ccc(C)c4s3)CC2)cc1,2.358502992,4.38334,2.024837008
CC[C@@H](C)[C@@H]([NH2+]C)C1([NH+](C)C)CCC(C)CC1,5.323609858,0.6877,-4.635909858
CC[C@H](C)NC(=O)[C@H](C)NC(=O)Nc1ccc2c(c1)COC2,2.971019369,2.1415,-0.829519369
Cc1nn(CC(=O)Nc2ccc(N3CCOCC3)cc2)c(=O)c(C#N)c1C,2.242124545,1.20712,-1.035004545
O=C([O-])CCCS(=O)(=O)Nc1ccc(Cl)cc1F,2.651310618,0.7509,-1.900410618
CCOC(=O)c1ccccc1[N-]S(=O)(=O)c1cccc(F)c1,2.877115287,3.3965,0.519384713
CC(C)(C)C(=O)Nc1ccc(NC(=O)c2ccccc2)nc1,1.82913395,3.3185,1.48936605
Cc1ccc(/C=C/C(=O)N(C)Cc2sccc2C)o1,2.502480003,3.62974,1.127259997
CC(C)c1noc(CN(C)C(=O)c2csc3nc(-c4cccc(F)c4)cn23)n1,2.730284857,3.9805,1.250215143
CCC[C@H](CCCc1c([O-])[nH+]c(N)[nH]c1=O)C(=O)[O-],4.655077806,-1.6663,-6.321377806
Cc1ccsc1-c1nc(C)c(C)nc1N,2.624802162,2.71256,0.087757838
Cn1nnc2cc(C(=O)NCC(C)(C)c3ccncc3)ccc21,2.42817859,2.0709,-0.35727859
O=C(c1cccc(C2=NO[C@@H]([C@@H]3N=NC4=C3CCCC4)N2)c1)N1CCCC1,3.976111738,3.1926,-0.783511738
COC[C@H](O)CN(C)C(=O)c1cscc1C,3.166815768,1.13582,-2.030995768
COc1ccc2c(c1)C(N1CCN(C(=O)c3ccc(F)cc3)CC1)=Nc1ccccc1O2,2.297544373,4.4763,2.178755627
NS(=O)(=O)N1CCC(C(=O)NCCNC(=O)c2ccco2)CC1,2.251835206,-0.9589,-3.210735206
CCC(CC)C(C)(C)C[C@@H](C)[NH2+]C,4.576779185,2.4206,-2.156179185
CCc1ccc2ncc(C#N)c(Nc3cc(OC)ccc3OC)c2c1,2.189201832,4.42968,2.240478168
C[C@@H]1CCCC[C@H]1NC(=O)N(C)Cc1nccs1,3.124031281,2.8632,-0.260831281
CCc1ccc(OCC(=O)N(C(C)C)C2CCOCC2)cc1,2.18664966,3.0438,0.85715034
COC1CCN(C(=O)c2ccc(N)c(C)c2)CC1,1.975924657,1.82822,-0.147704657
CCOC(=O)c1cc(S(=O)(=O)N2CCN(c3ccc(C(C)=O)cc3)CC2)cn1C,2.234633575,1.9153,-0.319333575
O=C(c1occc1Br)N1CCS[C@H](c2ccccc2)C1,3.097373773,3.9724,0.875026227
Clc1cc(N2CCC[C@H](OCc3cccnc3)C2)nc2[nH]ccc12,3.074239667,3.7969,0.722660333
CC(C)[NH2+]Cc1ccc([N+](=O)[O-])c(OC/C(Cl)=C/Cl)c1,3.652626052,2.7644,-0.888226052
Cc1cc(C)nc(Nc2nc(CC(=O)Nc3ccc(-n4cnnn4)cc3)cs2)n1,2.491890746,2.45044,-0.041450746
Cc1c2c(cc3c1O/C(=C\c1c(F)cccc1Cl)C3=O)CN(CCCN1CCOCC1)CO2,2.96219473,4.27792,1.31572527
CSc1ncc(CNc2cc(-c3ccccc3)nc3ccnn23)n1C,2.639763986,3.4638,0.824036014
CC[C@@H](C)c1ccc(S(=O)(=O)[N-]c2cc(F)ccc2F)cc1,3.448403351,4.8724,1.423996649
COCc1cccc(NC2=[NH+]CCCCC2)c1,3.269881966,1.298,-1.971881966
C[C@@H](Oc1ccccc1F)C(=O)NNC(=O)Nc1ccccn1,2.508059175,1.8409,-0.667159175
Cc1ccc(-n2c(C)cc(/C=N\NC(=O)CN3CCOCC3)c2C)cc1[N+](=O)[O-],2.45769911,2.09306,-0.36463911
CCN1C(=O)Cc2cc(NC(=O)c3ccc(Cl)c([N+](=O)[O-])c3)ccc21,2.169784347,3.4095,1.239715653
Cc1cc(I)ccc1NC(=O)C[NH+](C)[C@@H]1CCc2ccccc21,3.83053583,2.74032,-1.09021583
CCn1cc(NC(=O)[C@H]2CSCN2C(=O)CC(C)(C)C)ccc1=O,3.085154336,2.1444,-0.940754336
N#Cc1c(Cl)nsc1N[C@@H]1CCc2cc(Cl)ccc21,3.258787912,4.42098,1.162192088
Cc1cc(C)c(NC(=O)CNC(=O)[C@@H](NC(=O)c2ccco2)C(C)C)c(C)c1,2.611430454,2.71416,0.102729546
C[C@@H]1CCC[C@@H](N(C)C(=O)NCc2cccc(Cn3cccn3)c2)C1,3.054786435,3.6515,0.596713565
C[C@H](NC(=O)CN(C)Cc1nc2ccccc2c(=O)[nH]1)c1ccccc1,2.556272893,2.2323,-0.323972893
Cc1ccc(Oc2ncnc(Nc3ncccc3C)c2[N+](=O)[O-])cc1C,2.404361839,4.24096,1.836598161
O=C(CCn1cncn1)Nc1nc(-c2ccc3ccccc3c2)cs1,2.191495935,3.5836,1.392104065
Cc1cc(C)c(NC(=O)[C@H](C)N(C)OCc2ccccc2)c(Cl)c1,2.804898113,4.34744,1.542541887
Cc1ncc([C@@H](C)[NH2+][C@H](C)c2ccc3c(c2)CC(=O)N3)s1,4.219180742,2.33172,-1.887460742
CCc1ccc(C(=O)OCC(=O)N2CCCC2)o1,2.044629001,1.6212,-0.423429001
O=C(NCCCn1ccc2ccccc21)C1CCN(C(=O)/C=C/c2ccccc2)CC1,2.216937347,4.0996,1.882662653
CC/[NH+]=C(/NCc1nnc2n1CCCCC2)N[C@@H]1C[C@@H]1C,4.195178435,-0.4514,-4.646578435
CC[C@H]1CCCN1c1nc(C(F)(F)F)ccc1C(N)=S,3.041634251,3.1134,0.071765749
Cc1ccc(C)c(NC(=O)[C@@H](C)[S@@](=O)Cc2nccn2C(F)F)c1,3.483063654,3.17094,-0.312123654
C[C@@H]1CCCC[NH+]1CCNC(=O)CCc1ccc2c(ccn2C)c1,4.085937819,1.6844,-2.401537819
Cc1ccc(OCCOc2ccccc2)c(C(=O)[O-])n1,2.306296362,1.21132,-1.094976362
Cc1csc([C@@H](C#N)C(=O)CSc2nnc(-c3ccccc3F)n2C2CCCCC2)n1,3.136642384,5.3229,2.186257616
O=C(CCl)N1CCCC2(CCCCC2)C1,2.906406136,2.7981,-0.108306136
N#CCN1CCN(CC(=O)NCCCC2CCCCC2)CC1,2.20926879,1.60428,-0.60498879
COc1ccc(F)cc1CSCc1nc(CC(C)C)no1,2.372806593,3.8492,1.476393407
[NH3+][C@@H]1CCCC[C@@H]1/N=C/[C@@H]1C(=O)N=C(S)N(c2ccccc2)C1=O,4.498557498,1.0857,-3.412857498
CNS(=O)(=O)c1cc(NC(=O)Cc2cccc(F)c2F)ccc1C,2.073399905,2.36252,0.289120095
N#CCCN(CCC(F)(F)F)C(=O)c1ccc2c(c1)CCCC2,2.435420061,3.87368,1.438259939
CCCC(=O)N1CCC(n2nc(C(=O)OCC)cc2C2CC2)CC1,2.485511105,2.9008,0.415288895
CC(=O)Nc1c(N)cc2c3c(cccc13)C(=O)c1ccccc1-2,2.268598471,3.5918,1.323201529
CCN(Cc1cccs1)CN1C(=O)N[C@](CC)(c2ccccc2)C1=O,2.892918554,3.3848,0.491881446
C[C@@H](O)[C@H]1CCOC2(CCC2)C1,4.084328607,1.7165,-2.367828607
O=C1CCCN1C[C@H](NC(=O)N1CCN(c2nccs2)CC1)c1ccccc1,2.838055644,2.3384,-0.499655644
C=CCc1cc(N)ccc1O[C@H]1CCOC2(CCCC2)C1,3.696392259,3.8679,0.171507741
COC[C@@H]1CN(C(=O)C(=O)Nc2ccccc2-c2ccccc2)CCO1,2.611122099,2.1659,-0.445222099
O=C(CCc1ccc(Cl)cc1)NCCc1nc2cc(F)ccc2s1,2.030294342,4.3803,2.350005658
Cc1cn2c(=O)c(C(=O)N3CCC[C@H](n4cccn4)C3)cnc2s1,3.246150591,1.73822,-1.507930591
Brc1cc(C[NH+]2CCC[C@@H](c3cn[nH]c3)C2)cc2c1OCCCO2,4.62014089,2.296,-2.32414089
COc1ccccc1OCC(=O)NCC1(c2cccs2)CCCCC1,2.253408876,4.1538,1.900391124
CC[C@@H](C)Oc1cccc(C(=O)Nc2cccc(-c3nn4cnnc4s3)c2)c1,2.865814889,4.2824,1.416585111
Cc1ccc(CN[C@@H](C(=O)NC2CC2)c2ccccc2)s1,2.532502845,3.16602,0.633517155
C[C@@H](NC(=O)NNC(=O)[C@@H]1CCCO1)c1ccc(Cl)cc1,2.857988826,1.9104,-0.947588826
CC[C@H](O)Cn1nc(Cn2cncn2)nc1Cc1cccc(Cl)c1,3.119028851,1.933,-1.186028851
CC(C)[C@H](Sc1nnc(-c2ccco2)n1C)C(=O)Nc1ccc(Cl)cc1,2.778211591,4.4839,1.705688409
CO[C@H](C)c1noc(CNc2ccc(Sc3ccccc3)cc2C)n1,2.950654307,4.84872,1.898065693
C[C@@H](c1ccccc1)N1C[C@@H](C(=O)NCCCN2CC[NH+](C)CC2)CC1=O,3.848030117,-0.0673,-3.915330117
CCCC[C@@H](C(=O)OC)[C@H](O)c1ccc(Br)cc1F,3.086125432,3.601,0.514874568
O=C(NCC1(O)CCOCC1)c1ccc(CSC(F)F)o1,2.852911417,2.0067,-0.846211417
C[C@](O)(CNC(=O)c1cccs1)CN1CCOCC1,2.825985348,0.5611,-2.264885348
CCOc1ccccc1NC[C@@H]1COc2ccccc21,2.555787576,3.6734,1.117612424
CCN1C=C(C[NH+]2CCC(C(=O)Nc3ccc([C@@H]4C=c5ccccc5=[NH+]4)cc3)CC2)[C@@H](C)N1,5.152966629,-0.7319,-5.884866629
CC(C)(C)n1nccc1NC(=O)C(=O)N1CCC[C@@H](c2cn[nH]c2)C1,3.491526175,1.7059,-1.785626175
Cc1ccc(-n2ncc3c2ncn2nc(CCc4ccccc4)nc32)cc1Cl,2.444150134,4.21022,1.766069866
CC(=O)N1CCN(Cc2cc(NC[C@H](O)c3ccsc3)cc[nH+]2)CC1,3.795660076,1.3718,-2.423860076
CCC[C@H]1CN(C(=O)Nc2ccc(CC)c(F)c2)CCO1,2.654457773,3.4209,0.766442227
CC[NH+]1CCN([C@H](C)CNC(=O)COc2ccccc2C#N)CC1,3.789513477,-0.33782,-4.127333477
CCOc1ccc(N)c2ccccc12,1.62683674,2.8207,1.19386326
CC[C@@](C)([C@@H](N)c1cccc(Cl)c1)[NH+](C)C,4.121757026,1.653,-2.468757026
CC1CCC(NC(=O)C2=CC(=O)CS2)CC1,2.872345398,1.8811,-0.991245398
COc1cccc([C@@H](CO)NC(=O)NCc2scnc2C)c1,2.77534479,1.99292,-0.78242479
C[C@@H](CCO)C[NH2+]Cc1ncc(-c2ccccc2)s1,3.718444882,1.892,-1.826444882
C[C@@H](c1ccccc1)[C@@H]1[NH2+][C@@H](C(=O)[O-])CS1,4.769985386,-0.4551,-5.225085386
O=C(NCCc1ccc(S(=O)(=O)NC(=O)c2ccc(Cl)cc2)cc1)c1ccc(Cl)cc1,1.894832086,4.0846,2.189767914
Cc1nc(-c2c[nH]c(C(=O)NC(C)(C)C)c2)cs1,2.51899596,2.97492,0.45592404
CCSc1nnc(NC(=O)c2sc3cccc(F)c3c2C)s1,2.398658841,4.56462,2.165961159
Cn1c(=O)c(C2=NN[C@H](c3cccs3)C2)c(O)c2ccccc21,3.233543543,2.7443,-0.489243543
Cc1noc(C)c1CCC(=O)N1CCC2(CC1)Nc1ccccc1S(=O)(=O)N2,3.384083936,1.94674,-1.437343936
COCc1ccccc1NC(=O)NCc1ccc(C)cc1N(C)C,2.104700542,3.52912,1.424419458
Cc1cc2c3c([nH]c2cc1S(=O)(=O)N[C@H](C)Cc1ccco1)CC(C)(C)CC3=O,3.268707608,4.13392,0.865212392
CCOc1ccc([C@H](C)NC(=O)C(=O)Nc2ccc(Cl)cc2C)cc1,2.322029223,3.86302,1.540990777
CO[C@H](CNC(=O)C(=O)Nc1cccnc1-n1cccn1)c1ccccc1,2.837716015,1.7097,-1.128016015
CCc1ccc([C@H]([NH3+])[C@@H](C)Oc2ccccc2C)cc1,3.183736434,3.30792,0.124183566
Cc1cccc([C@H]2CCCCC[NH+]2C2CCN(C(N)=O)CC2)c1,4.215146126,2.03812,-2.177026126
CCC[C@@H](NC(=O)c1ccc2[nH]c3c(c2c1)CCCC3)C(=O)OC,2.682829088,3.1182,0.435370912
C[C@H]1CN(C(C)(C)CNC(=O)[C@@H]2C[C@@H]2c2ccc(F)cc2)C[C@@H](C)O1,3.550507363,2.9332,-0.617307363
CC(C)CN(C[C@H](O)c1ccc(F)cc1)C(=O)Nc1cccnc1Cl,2.717118187,4.0976,1.380481813
CCCCOc1ccc(Cl)cc1,1.295802195,3.5189,2.223097805
CS(=O)(=O)NC1CCC([NH2+][C@@H]2C[C@H]3C[C@@H]2[C@H]2CCC[C@@H]23)CC1,5.544107265,1.2349,-4.309207265
COC[C@@H](C)CNC(=O)[C@@H]1C=C[C@@H]([NH3+])C1,4.562448454,-0.4283,-4.990748454
Cc1ccc(C[C@@H](C)[NH2+][C@H](C)c2ccc(C)c(F)c2)s1,3.86904984,3.75964,-0.10940984
COCCN1C(=O)[C@@H]2[C@@H](Cc3ccc(O)c(O)c3)N[C@@]3(C(=O)Nc4ccc(F)cc43)[C@@H]2C1=O,4.296201915,0.8464,-3.449801915
Cc1ccc(C(=O)C2=C([O-])C(=O)N(CCCN3CCOCC3)[C@@H]2c2cccs2)cc1,3.18358086,2.15942,-1.02416086
Cc1cc([C@H](O)[C@@H]2CCO[C@]3(CCOC3)C2)ccc1F,4.177699502,2.75322,-1.424479502
C[C@@H]1CCC[C@H](N(C)C(=O)c2cccc(NC(=O)C(C)(C)C)c2)C1,2.889836908,4.3219,1.432063092
COC(=O)[C@@H]1c2ccsc2CCN1S(=O)(=O)c1ccc(F)cc1,2.801380825,2.3483,-0.453080825
CC[NH+](CC)C[C@@H](C)NC(=O)CC1C[C@@H]2CC[C@H](C1)[NH2+]2,6.514367429,-0.6897,-7.204067429
COC(=O)c1c(N)sc2c1CCS2,3.06293975,1.7651,-1.29783975
CC[C@H](C(=O)N1CCCSC[C@H]1C[NH+](C)C)c1ccccc1,3.940231943,1.6588,-2.281431943
Cc1occc1C(=O)N/N=C/c1cc(Br)cc([N+](=O)[O-])c1[O-],3.024713357,2.09622,-0.928493357
CCOc1ccccc1O[C@H]1CCC[C@H]1NC(=O)c1ccc(OC)nc1,2.886927227,3.2188,0.331872773
Cc1cc(C)c(NC(=O)NCc2ccc(N3CCOCC3)cc2)c(C)c1,1.995046745,3.77016,1.775113255
COc1ccc(C[C@@H](C#N)C(=O)[O-])cc1OC,2.962785706,0.13598,-2.826805706
CC(C)NC(=O)Nc1nc2c(s1)CN(C(=O)c1ccc(C(F)(F)F)cc1)CC2,2.379930821,3.8903,1.510369179
Cc1cccnc1CSc1nc2cc(S(=O)(=O)N(C)C)ccc2o1,2.451381369,3.07382,0.622438631
Cc1ccccc1[C@@H](C)NC(=O)C(=O)Nc1ccc(N(C)C)c(C)c1,2.528571728,3.18534,0.656768272
CC(C)(C)OC(=O)N1CC(=CC(=O)NCC#N)C1,2.850974182,0.80328,-2.047694182
O=C1c2cccnc2C(=O)N1Cc1ccc(F)cc1S(=O)(=O)N1CCCCCC1,2.385687811,2.5816,0.195912189
CCCN(CC(=O)NC)C(=O)[C@@H]1CC[C@H]2CCCC[C@@H]2[NH2+]1,4.358803608,0.2556,-4.103203608
CS(=O)(=O)/N=c1/[nH]c(CC(=O)Nc2c(Cl)cccc2Cl)cs1,2.978326434,2.4245,-0.553826434
C=CCn1c([S-])nc2cc(C(=O)N(C)CC(=O)Nc3ccccc3C(F)(F)F)ccc2c1=O,2.867110276,3.2178,0.350689724
C[C@@H](c1ccc(S(C)(=O)=O)cc1)N(C)C(=O)c1cccc(Br)c1,2.415458229,3.6858,1.270341771
Cc1c(C(=O)NNC(=O)NC[C@H]2CCCO2)sc2ccc(F)cc12,2.847237911,2.47182,-0.375417911
CCOc1ccc(O[C@@H](C)C(=O)NNC(=O)c2[nH]c3ccccc3c2Br)cc1,2.67368861,3.5576,0.88391139
Cc1ccc([C@@H](C)[NH2+][C@H](C)c2nc3c(s2)CCCC3)s1,4.200584091,3.77742,-0.423164091
O=C(CN1C(=O)NC2(CCCCCC2)C1=O)NCc1cccs1,2.725502984,2.0091,-0.716402984
CC[C@H](NC(=O)CCc1cc(F)ccc1F)C(=O)OC,2.471739379,1.9652,-0.506539379
O=C(/C=C/c1c(F)cccc1F)N1CCN(c2ncccc2F)CC1,2.325210066,2.8609,0.535689934
CC(C)C[C@H]1NC(=O)[C@H]2CN(C(=O)c3ccc4ccccc4c3)CCN2C1=O,3.012230743,2.0373,-0.974930743
CCOc1cc(/C=C2/C(=N)N3N=CSC3=NC2=O)ccc1OC(=O)c1ccccc1Cl,2.837941116,4.20687,1.368928884
Cc1oc(-c2ccco2)nc1CC(=O)NC[C@H](C)Oc1ccccc1,2.739838922,3.36922,0.629381078
CCC[C@H]1C[NH2+]CC[C@@H]1c1cnn(-c2ccccc2)c1,4.019041705,2.3393,-1.679741705
O=C(c1c[nH]c(=O)cn1)N(CC1CC[NH+](C2CCCC2)CC1)C[C@H]1CCCO1,4.283016619,0.6286,-3.654416619
C[C@@H]1C[C@@H]1c1ccc(/C=C/C(=O)N2CCC[C@H](O)C2)o1,3.44240408,2.3995,-1.04290408
C[C@H](NC(=O)NC[C@@](C)(O)c1cccs1)c1cccc(C#N)c1,3.261078337,2.88768,-0.373398337
O=C(Nc1ccnn1C1CC[NH+](Cc2ccccc2F)CC1)c1cccnc1,3.31736458,2.0895,-1.22786458
CC(C)[C@@H](CNC(=O)NC[C@@]1(C)CCCO1)C(=O)[O-],3.979685613,-0.1232,-4.102885613
Cc1sc(NC(=O)C2CC2)c(C(=O)NCc2n[nH]c(=S)n2C(C)C)c1C,2.734164435,3.47843,0.744265565
Cc1nc(-n2cccc2)sc1C(=O)N1CCN(c2ccccc2Cl)CC1,2.289127951,3.85802,1.568892049
Cc1cc(NC(=O)N[C@H]2CCCN(c3ccccc3F)C2=O)no1,2.730594246,2.43922,-0.291374246
COc1ccc(Cl)c(NC(=O)[C@@H]2CC(O)=Nc3nncn32)c1,3.229683996,2.1116,-1.118083996
O=C(Nc1ccc(Cl)cc1)C1CCN(c2ccc(-n3cccc3)cn2)CC1,2.15321418,4.3808,2.22758582
CCc1c(N)cnn1[C@@]1(C)CCS(=O)(=O)C1,3.768605035,0.5614,-3.207205035
COc1ccc(OC)c(N2C(=O)/C(=C/c3ccc(O)cc3)SC2=S)c1,2.199361566,3.8152,1.615838434
NC(=O)c1cccc(CNC(=O)COc2ccc(F)cc2Cl)c1,1.814883042,2.2732,0.458316958
COC[C@H](Nc1nccc(OC)n1)c1ccc(F)c(F)c1,2.847808751,2.563,-0.284808751
COC(=O)[C@@]1(NC(=O)CCCc2cc(Cl)sc2Cl)CCOC1,3.349771447,2.8259,-0.523871447
C[C@H]1C[C@H](C)CN(S(=O)(=O)CCn2cnc3ccccc32)C1,3.092222018,2.344,-0.748222018
CCCC[C@@H]1CCC[C@@H]1NC(=O)NC1CC[NH+](CC(=O)N(C)C)CC1,4.320234072,0.78,-3.540234072
Cc1cc(C)cc(N(C)CC(=O)N(C)[C@@H]2CCS(=O)(=O)C2)c1,2.950781578,1.38514,-1.565641578
Cc1ccc(/C=C/C(=O)Nc2sc(C)c(C)c2C#N)o1,2.5068509,3.78994,1.2830891
Cn1nnc2c(=O)n(CC(=O)NC3CC3)cnc21,2.465292041,-1.1964,-3.661692041
COc1cc([C@@H](C)N[C@H]2CC[C@H]([NH+](C)C)C2)ccc1OC(F)F,4.124893168,2.0128,-2.112093168
CC(C)(C)OC(=O)NC1CCN(C(=O)C[NH3+])CC1,2.72243658,-0.256,-2.97843658
CC[C@](C)([C@@H]([NH2+]C)c1cc(Cl)cnc1N)N1CCOCC1,4.283133262,1.0524,-3.230733262
Cc1cnc([C@@H](NC(=O)N(C)[C@H](c2ccccc2)C(C)C)C2CC2)s1,3.46224187,4.94132,1.47907813
COc1ccc([C@H]2CC(=O)C3=C(C)Nc4ccccc4N[C@H]3C2)c(OC)c1OC,3.44999679,4.3391,0.88910321
CC(=O)N(C)C1CCC(C)(C)CC1,2.486653325,2.4335,-0.053153325
CNC(=O)c1cccc(NC(=O)Cc2ccccc2Cl)c1,1.657934364,2.8808,1.222865636
COc1cc(Br)c(O[C@H]2CCCCO2)cc1C,2.706881191,3.67152,0.964638809
O=C(C[C@@H]([NH2+]C[C@H]1CCCO1)C(=O)[O-])Nc1cccc(Cl)c1,4.003377104,-0.4705,-4.473877104
COc1ccc2cc(-c3csc(NC(=O)COc4ccccc4)n3)oc2c1,2.100462086,4.5824,2.481937914
C[C@H](Sc1nnc(-c2ccccc2)n1C)C(=O)N1CCC[C@@H](C(N)=O)C1,2.909074093,1.6866,-1.222474093
CCC[C@@H]1C[C@@H]1NC(=O)C(=O)Nc1cccc(-c2nnc(C)o2)c1,3.176625808,2.28832,-0.888305808
Cc1cc(C(=O)COC(=O)c2ccc(I)cc2)c(C)n1C[C@H]1CCCO1,2.718210326,3.92824,1.210029674
Cc1c(Cl)cccc1NC(=O)C(=O)NCc1ncn(C)n1,2.463583143,1.03182,-1.431763143
Cc1ccc([C@@]2(C(C)(C)C)CCC[NH2+]2)cc1,3.995007141,2.59362,-1.401387141
CN(C)C(=O)c1ccc(NC[C@@H]2CC[C@@H](C(=O)[O-])O2)nn1,3.770737241,-1.1122,-4.882937241
c1cncc([C@H](c2nc(-c3ccc4nc[nH]c4c3)no2)N2CCOCC2)c1,3.116341853,2.4295,-0.686841853
C[C@@H]1[C@H](C(=O)[O-])CCN1C(=O)N1CCc2ccccc21,3.598947972,0.6294,-2.969547972
Cn1cc(CCCOC(=O)c2cc3ccccc3c(=O)[nH]2)cn1,2.31302229,2.0512,-0.26182229
CCOc1ccc(NC(=O)Cn2cnc3sccc3c2=O)cc1,2.011111102,2.4954,0.484288898
O=C(Nc1ccccc1O)C1CC[NH+](CC2CCCCC2)CC1,3.239610037,2.2059,-1.033710037
Cc1ccc(CCNC(=O)c2cc(NC(=O)C3CCCC3)n(C)n2)o1,2.383830001,2.42272,0.038889999
O=C(Nc1cccnc1N1CCCC1)c1ccccc1,1.689281897,2.9341,1.244818103
COc1ccc(N2C(=O)NC(=O)/C(=C/Nc3ccc(F)cc3)C2=O)cc1,2.107915685,2.4131,0.305184315
C[C@@H]([NH2+]Cc1ccc(Cl)c(Cl)c1)c1ccc2c(c1)NC(=O)CO2,3.579272193,3.1489,-0.430372193
Cc1cccc(C)c1NC(=O)CN(C)C(=O)c1cc(C)n(-c2ccccc2)c1C,2.10301896,4.42168,2.31866104
CCc1ccsc1CNC(=O)Nc1ccc(NC(C)=O)c(OC)c1,2.285167592,3.5992,1.314032408
CC/C(C)=N\NC(=O)Cc1ccc(OC)cc1,1.94757722,2.1398,0.19222278
Cc1n[nH]c2cc(NC(=O)c3c(O)cc(F)cc3F)ccc12,2.437484013,3.10742,0.669935987
Cc1ccc(S(=O)(=O)NC[C@H](c2ccc(N(C)C)cc2)[NH+](C)C)c(C)c1,3.427929923,1.53354,-1.894389923
CCC(O)(CC)C(=O)NN(C(=O)c1csc(C)c1)c1ccccc1,2.822409727,3.28562,0.463210273
CC(C)CCNc1nc2c(c(=O)n(Cc3ccccc3Cl)c(=O)n2C)n1C,2.454505588,2.5934,0.138894412
COc1cc(C(=O)NC[C@H](C2CC2)[NH+](C)C)cc(OC)c1C,3.532251982,0.66512,-2.867131982
Cc1cccc2cc([C@H]3NC(=O)c4c(sc5c4CCCC5)N3)c(Cl)nc12,3.223424868,4.99102,1.767595132
Cc1[nH]cnc1C[NH+]1CCCCCCC1,4.107993259,1.06712,-3.040873259
CCC1CCC(C[NH3+])([NH+](CC)[C@@H](C)CC)CC1,5.245877951,1.2706,-3.975277951
N#Cc1ccc([C@@H]([NH3+])CO)cc1F,3.721661942,-0.02732,-3.748981942
CC1(C)SC[C@H](C(=O)N2CCC(OCc3cccnc3)CC2)NC1=O,3.235181826,1.5994,-1.635781826
O=C(C[NH+]1CCC[C@H]1c1nc2ccccc2s1)Nc1cc2[nH]c(=O)[nH]c2cc1Br,4.250457538,2.5869,-1.663557538
CS(=O)(=O)NC[C@H]1CCCCN1C(=O)Nc1cc(C(F)(F)F)ccc1N1CCCC1,2.897130764,3.2412,0.344069236
C[C@H](CC(=O)[C@@H](C#N)c1nc2cc(Cl)ccc2s1)NC(=O)C1CCCC1,3.325606232,4.21098,0.885373768
C=CC[NH+](Cc1cc(F)ccc1OC)[C@H]1CCS(=O)(=O)C1,4.383766153,0.5923,-3.791466153
CCc1cccc(OC2CN(C(=O)[C@H]3CCCCC[NH+]3C)C2)c1,3.985533936,1.2959,-2.689633936
C[NH+]1CCN(C(=O)[C@@H](O)c2ccccc2)CC1,3.840745614,-0.9231,-4.763845614
COc1ccc(C(=O)N2CCC[C@H](c3nc(O)c4nnn(Cc5ccc(F)cc5)c4n3)C2)cc1OC,3.02645316,3.1513,0.12484684
NC(=O)CCOc1ccccc1NC(=O)c1ccc(Cl)c(Cl)c1,1.802330638,3.4999,1.697569362
C[C@@H](O)c1ccc(-c2ccccc2)cc1,1.840468032,3.4069,1.566431968
C[NH2+]C1CCC(N(C)C(=O)c2sccc2Br)CC1,3.545652041,2.087,-1.458652041
C[C@H]1CCC[NH+]1CC(=O)Nc1ccc(F)c(N2CCCS2(=O)=O)c1,4.340830087,0.3713,-3.969530087
CC[C@@H](C)NC(=O)[C@@H](C)NC(=O)C#Cc1ccccc1,2.993557533,1.4575,-1.536057533
c1ccc2sc(C3CC[NH+](C4CCSCC4)CC3)nc2c1,3.761832631,2.9542,-0.807632631
C=C1C[C@@]23CC[C@@H]4[C@@](C)(CCC[C@@]4(C)C(=O)[O-])[C@H]2CC[C@]1(O)C3,5.990082746,2.8203,-3.169782746
Nc1c(NCCN2CCOCC2)ncnc1Nc1ccc(Br)cn1,2.469548408,1.704,-0.765548408
Cn1c(COC(=O)CCc2ncc(-c3ccccc3F)o2)cc(=O)n(C)c1=O,2.549319379,1.5541,-0.995219379
COc1ccc(Br)cc1/C=C/C(=O)NC[C@@H]1CCCO1,2.604302267,2.7661,0.161797733
CCC[C@H](C)NC(=O)[C@@H](C)Nc1cccc(C)c1,2.577453333,3.10022,0.522766667
C[C@H]1C[C@H](C)CN(C(=O)COC(=O)c2cncc(Br)c2)C1,2.968923183,2.5054,-0.463523183
Nc1cc(-c2cccc(Cl)c2)nn1-c1ccccc1F,2.017455876,3.914,1.896544124
COc1ccc(NC(=O)C(=O)NC2CC2)cc1C,1.795305536,1.22072,-0.574585536
CC(C)N(Cc1cccnc1)C(=O)Nc1cnn(C[C@H]2CCCO2)c1,2.91680623,2.8996,-0.01720623
C[C@@H](CNS(=O)(=O)c1ccc2c(c1)NC(=O)CO2)[NH+](C)C1CC1,4.101762609,-0.6386,-4.740362609
COc1ccc(NC(=O)N2CCC[C@@H](c3nnc(C(=O)Nc4cccc(F)c4)s3)C2)cc1,2.731919328,4.3496,1.617680672
COC1CC[NH+](C[C@@H]2CCC[C@H](C)C2)CC1,4.704832754,1.5064,-3.198432754
CSc1ccc([C@H](Br)Cc2ccccc2)cc1,2.313993515,5.0872,2.773206485
C[C@@]1(c2ccccc2F)NC(=O)N(CC(=O)NCC(F)(F)F)C1=O,2.754553183,1.2712,-1.483353183
O=c1[nH]c(Cn2c(-c3ccccc3)noc2=O)nc2sc3c(c12)CCCC3,2.594235212,2.7284,0.134164788
Cc1cncc(C(=O)NCCC[NH+]2C[C@H](C)C[C@@H](C)C2)c1,4.271486349,1.07072,-3.200766349
CCc1ccc(NC(=O)[C@@H](C)S(=O)(=O)Cc2nncn2C)cc1,2.848858229,1.3195,-1.529358229
COc1ccccc1-c1ccccc1C[C@H]1CN(C(=O)c2ccccc2)CCN(C)C1=O,2.69381381,4.1353,1.44148619
Cc1nsc(N)c1-c1nc2cnccc2n1C1CC1,2.945782277,2.78032,-0.165462277
Cc1ccsc1CN(CC[NH+](C)C)C(=O)c1ccc2c(c1)nnn2C,3.351786841,1.12512,-2.226666841
Cc1cccnc1CCNC(=O)c1ccc([C@H]2CCC[NH+]2C(C)C)s1,4.198988791,2.55222,-1.646768791
CCc1ccsc1CNC(=O)C1(c2ccccc2F)CC1,2.52839512,3.7976,1.26920488
COc1cccc([C@@H](C)NC(=O)COC(=O)c2cncc(Br)c2)c1,2.462586621,2.8869,0.424313379
CS(=O)(=O)N(CC(=O)N1CCOCC1)c1ccc(F)c(Cl)c1,2.227684896,1.1039,-1.123784896
COc1ccc(N2C(=O)CC[C@H](C(=O)Nc3ccc(-n4ccnc4)cc3)[C@H]2c2ccccc2OC)cc1,3.144845929,5.0125,1.867654071
CC[C@@H]1C(=O)N2CCC[C@H]2C(=O)N1CC1CCC1,3.231498394,1.3983,-1.833198394
Cn1ccc(C(=O)N2C[C@@H]3CC[C@H](C2)[NH+](Cc2ccccc2)C3)n1,5.015766837,0.7396,-4.276166837
NC1=NC(=O)N(Cc2ccccc2)[C@@H]1c1ccccc1Br,2.854435153,3.4832,0.628764847
CC(C)[NH+]1CCCN(C(=O)c2cc(C3CC3)n(C(C)(C)C)n2)CC1,4.137344869,1.6547,-2.482644869
Cc1cc(CNC(=O)[C@@]2(C)CCC[NH2+]2)cc(C)c1F,3.955747577,1.17464,-2.781107577
C[C@@H]1CC(=O)N(c2ccc(NCc3cccnc3)c([N+](=O)[O-])c2)C1=O,2.76947101,2.5013,-0.26817101
CC1CCC2(CC1)NC(=O)N(NC(=O)CNC(=O)c1cccs1)C2=O,3.018403098,1.0098,-2.008603098
CCOc1ccc(C(C)=O)cc1Cn1c(C(F)(F)F)nc2ccccc21,2.185433336,4.7047,2.519266664
Cc1ccccc1N1C(=O)c2ccc(C(=O)OCC(=O)NC[C@H]3CCCO3)cc2C1=O,2.664389642,2.24762,-0.416769642
COC1CCC(CC(=O)Nc2[nH+]cccc2[O-])CC1,3.890243815,1.1081,-2.782143815
Nc1ccc(F)cc1NC(=O)CN1CCc2ccccc21,1.963255159,2.4091,0.445844841
COc1c(Br)cc(Cl)cc1S(=O)(=O)[N-]c1cc(F)ccc1F,3.247974726,4.7834,1.535425274
CCOc1ccc(NC(=O)N(C)Cc2ncnn2C)cc1C,2.274019952,2.18612,-0.087899952
C[C@H]1OCC[C@@H]1C(=O)Nc1ccc(Cl)c(C(=O)[O-])c1,3.500226302,1.067,-2.433226302
O=C(NCc1cccnc1)C(=O)N1CCCC1,1.957724353,0.3202,-1.637524353
C#CCOc1ccc2ccccc2c1CN1CCN(CC#N)CC1,2.405771046,2.49298,0.087208954
CCOc1ncccc1-c1ncnc2scc(C)c12,2.507808944,3.46042,0.952611056
CC(=O)NC1(c2ccc(F)cc2)CCN(C(=O)C2CCCCC2)CC1,2.31470064,3.3598,1.04509936
Cc1ccc(NC(=O)C(=O)NC[C@@H](c2ccc3c(c2)CCN3C)[NH+]2CCCCC2)c([N+](=O)[O-])c1,3.902352433,1.76032,-2.142032433
CCc1ccc(Oc2ccc3nnnn3n2)c(N)c1,2.589085867,1.4562,-1.132885867
Cc1cc(OCC(=O)OC(C)C)cc2c1C(=O)/C(=C/c1cc(Br)cc3c1OCOC3)O2,2.988519088,4.57062,1.582100912
COC(=O)[C@H]1[C@@H](NC(=S)NC[C@@H]2C=CN=N2)S[C@@H](C)[C@H]1C,5.238676386,1.6853,-3.553376386
C[C@H]1C[C@@H](C)CN(C(=O)CSc2ccccc2S(C)(=O)=O)C1,3.009957402,2.6867,-0.323257402
CC1(C)COc2cscc2OC1,3.146666448,2.5455,-0.601166448
Cc1cc(C)c(NC(=O)CNC(=O)CCc2nc3ccccc3c(=O)[nH]2)c(C)c1,2.263700819,2.53586,0.272159181
COC[C@@H](C)NC(=O)Nc1ccc(F)cc1F,2.338775673,2.1212,-0.217575673
CCOc1ccc(N[C@H](C)c2ccc(F)cc2F)cc1CO,2.516868547,4.0289,1.512031453
CCCn1nnnc1CN1C(=O)c2ccc([N+](=O)[O-])cc2C1=O,2.398476792,0.7875,-1.610976792
CC(C)CC(C)(C)C(=O)Nc1ccc(F)c(C(=O)N(C)C)c1,2.287139552,3.5383,1.251160448
Cn1cc(/C=C/C(=O)c2cccc(F)c2)c(=O)n(C)c1=O,2.300985321,1.1192,-1.181785321
O=C(NCc1ccnc(OC2CCC2)c1)c1[nH]ncc1Br,2.55409362,2.4285,-0.12559362
Cc1ccc([C@@H](CN2CCOCC2)NC(=O)COc2ccc(F)cc2)cc1,2.421749151,2.70262,0.280870849
================================================
FILE: tests/data/micro_ZINC_corrupt.csv
================================================
SMILES1,SMILES2,SMILES3,SA,logp,score
CCc1ccc(C(=O)/C(=C(/S)NC2CC2)[n+]2ccc(CC)cc2)cc1,CCc1ccc(C(=O)/C(=C(/S)NC2CC2)[n+]2ccc(CC)cc2)cc1,CCc1ccc(C(=O)/C(=C(/S)NC2CC2)[n+]2ccc(CC)cc2)cc1,2.775189478,3.7897,1.014510522
XXX,C=CCOc1ccc(C(F)(F)F)cc1C(=O)NC[C@H]1CCC[C@H]1O,C=CCOc1ccc(C(F)(F)F)cc1C(=O)NC[C@H]1CCC[C@H]1O,3.071497898,3.161,0.089502102
COc1cc(OC)cc([C@@H](NC(=O)Cn2ccccc2=O)c2nccn2C)c1,COc1cc(OC)cc([C@@H](NC(=O)Cn2ccccc2=O)c2nccn2C)c1,COc1cc(OC)cc([C@@H](NC(=O)Cn2ccccc2=O)c2nccn2C)c1,2.84000896,1.5048,-1.33520896
CN1CCC[C@@]2(CCN(C(=O)CN3CCNC(=O)C3)C2)C1=O,CN1CCC[C@@]2(CCN(C(=O)CN3CCNC(=O)C3)C2)C1=O,CN1CCC[C@@]2(CCN(C(=O)CN3CCNC(=O)C3)C2)C1=O,3.605168457,-1.1109,-4.716068457
COc1ccc(Cl)cc1NC(=O)c1nn(C)cc1[N+](=O)[O-],COc1ccc(Cl)cc1NC(=O)c1nn(C)cc1[N+](=O)[O-],COc1ccc(Cl)cc1NC(=O)c1nn(C)cc1[N+](=O)[O-],2.132664673,2.2426,0.109935327
Cc1nn(-c2ccccc2)c(O)c1/C=[NH+]/Cc1ccncc1,Cc1nn(-c2ccccc2)c(O)c1/C=[NH+]/Cc1ccncc1,Cc1nn(-c2ccccc2)c(O)c1/C=[NH+]/Cc1ccncc1,3.117639223,0.98102,-2.136619223
C=CCN1C(=O)C(C)(C)COc2ccc(NC(=O)C3(c4ccc(OC)cc4)CCOCC3)cc21,C=CCN1C(=O)C(C)(C)COc2ccc(NC(=O)C3(c4ccc(OC)cc4)CCOCC3)cc21,C=CCN1C(=O)C(C)(C)COc2ccc(NC(=O)C3(c4ccc(OC)cc4)CCOCC3)cc21,2.744789746,4.3197,1.574910254
CON(C)C(=O)c1cnn2c(C)cc(C)nc12,XXX,CON(C)C(=O)c1cnn2c(C)cc(C)nc12,2.547847184,0.97954,-1.568307184
NC(=O)[C@@H](NC(=O)CCCc1cccs1)c1ccccc1,NC(=O)[C@@H](NC(=O)CCCc1cccs1)c1ccccc1,NC(=O)[C@@H](NC(=O)CCCc1cccs1)c1ccccc1,2.444743614,2.4136,-0.031143614
COc1cccc(CN2CCC[NH+](CC(=O)Nc3ccc(F)cc3)S2(=O)=O)c1,COc1cccc(CN2CCC[NH+](CC(=O)Nc3ccc(F)cc3)S2(=O)=O)c1,COc1cccc(CN2CCC[NH+](CC(=O)Nc3ccc(F)cc3)S2(=O)=O)c1,3.546311784,0.8084,-2.737911784
Cc1ccc(-c2nc3ccc(C)c(C)c3[nH]2)nc1,XXX,Cc1ccc(-c2nc3ccc(C)c(C)c3[nH]2)nc1,2.342516783,3.55016,1.207643217
Cc1n[nH]c(SCC(=O)Nc2cccc(C#Cc3ccccn3)c2)n1,Cc1n[nH]c(SCC(=O)Nc2cccc(C#Cc3ccccn3)c2)n1,Cc1n[nH]c(SCC(=O)Nc2cccc(C#Cc3ccccn3)c2)n1,2.685016252,2.63872,-0.046296252
COC(=O)c1cncc(C(=O)Nc2cccc(COC(C)C)c2)c1,COC(=O)c1cncc(C(=O)Nc2cccc(COC(C)C)c2)c1,COC(=O)c1cncc(C(=O)Nc2cccc(COC(C)C)c2)c1,2.04419285,3.0455,1.00130715
Cc1ccc(C)c(Oc2ccc(CNC(=O)N3CCCSCC3)cn2)c1,Cc1ccc(C)c(Oc2ccc(CNC(=O)N3CCCSCC3)cn2)c1,XXX,2.369895336,4.13924,1.769344664
CC[NH2+][C@@]1(C(=O)[O-])CCC[C@H](Oc2ccc(CC)cc2)C1,CC[NH2+][C@@]1(C(=O)[O-])CCC[C@H](Oc2ccc(CC)cc2)C1,CC[NH2+][C@@]1(C(=O)[O-])CCC[C@H](Oc2ccc(CC)cc2)C1,4.165551538,0.6424,-3.523151538
CC(=O)N1c2ccc(S(=O)(=O)N3CCCC3)cc2C[C@H]1C(=O)NCC[NH+](C)C1CCCCC1,CC(=O)N1c2ccc(S(=O)(=O)N3CCCC3)cc2C[C@H]1C(=O)NCC[NH+](C)C1CCCCC1,CC(=O)N1c2ccc(S(=O)(=O)N3CCCC3)cc2C[C@H]1C(=O)NCC[NH+](C)C1CCCCC1,3.989079408,0.7123,-3.276779408
C=CCN1CC(=O)N2[C@@H](Cc3c([nH]c4ccccc34)[C@@H]2c2ccccc2OC)C1=O,C=CCN1CC(=O)N2[C@@H](Cc3c([nH]c4ccccc34)[C@@H]2c2ccccc2OC)C1=O,C=CCN1CC(=O)N2[C@@H](Cc3c([nH]c4ccccc34)[C@@H]2c2ccccc2OC)C1=O,3.287567863,3.0474,-0.240167863
COc1ccc(-c2noc3ncnc(N4CCC[C@H](C(=O)[O-])C4)c23)cc1,COc1ccc(-c2noc3ncnc(N4CCC[C@H](C(=O)[O-])C4)c23)cc1,COc1ccc(-c2noc3ncnc(N4CCC[C@H](C(=O)[O-])C4)c23)cc1,3.152874099,1.2597,-1.893174099
N#Cc1cc(NC(=O)Cc2ccccc2Cl)ccc1N1CCC(O)CC1,N#Cc1cc(NC(=O)Cc2ccccc2Cl)ccc1N1CCC(O)CC1,N#Cc1cc(NC(=O)Cc2ccccc2Cl)ccc1N1CCC(O)CC1,2.173785623,3.35398,1.180194377
XXX,XXX,XXX,2.513723357,3.71262,1.198896643
Cc1ccc(NC(=O)c2ccc(F)cc2F)cc1S(=O)(=O)Nc1ccc(Cl)cc1,Cc1ccc(NC(=O)c2ccc(F)cc2F)cc1S(=O)(=O)Nc1ccc(Cl)cc1,Cc1ccc(NC(=O)c2ccc(F)cc2F)cc1S(=O)(=O)Nc1ccc(Cl)cc1,1.947448768,4.97972,3.032271232
CNC(=O)[C@@H]1CCC[NH+]1Cc1ccc(C)c(F)c1,CNC(=O)[C@@H]1CCC[NH+]1Cc1ccc(C)c(F)c1,CNC(=O)[C@@H]1CCC[NH+]1Cc1ccc(C)c(F)c1,4.391338086,0.42742,-3.963918086
CN1C(=O)N[C@@H](c2cccc([N+](=O)[O-])c2)C2=C1CN(c1ccc(F)cc1)C2=O,CN1C(=O)N[C@@H](c2cccc([N+](=O)[O-])c2)C2=C1CN(c1ccc(F)cc1)C2=O,CN1C(=O)N[C@@H](c2cccc([N+](=O)[O-])c2)C2=C1CN(c1ccc(F)cc1)C2=O,2.956072154,2.7309,-0.225172154
Cc1cc(CC(=O)N[C@H](c2ccc(F)cc2)C2CCC2)no1,Cc1cc(CC(=O)N[C@H](c2ccc(F)cc2)C2CCC2)no1,Cc1cc(CC(=O)N[C@H](c2ccc(F)cc2)C2CCC2)no1,2.665131639,3.32222,0.657088361
Cc1cc(N2CCN(CC(=O)NC3CC3)CC2)nc(-c2ccc([N+](=O)[O-])cc2)n1,Cc1cc(N2CCN(CC(=O)NC3CC3)CC2)nc(-c2ccc([N+](=O)[O-])cc2)n1,Cc1cc(N2CCN(CC(=O)NC3CC3)CC2)nc(-c2ccc([N+](=O)[O-])cc2)n1,2.249569987,1.76082,-0.488749987
Cc1cc(C(=O)N2CCN(C(=O)N[C@H]3CC(=O)N(C4CC4)C3)CC2)c(C)o1,Cc1cc(C(=O)N2CCN(C(=O)N[C@H]3CC(=O)N(C4CC4)C3)CC2)c(C)o1,Cc1cc(C(=O)N2CCN(C(=O)N[C@H]3CC(=O)N(C4CC4)C3)CC2)c(C)o1,2.921725033,1.12714,-1.794585033
O=c1c2c3nc4ccccc4nc3n(CCC3=CCCCC3)c2ncn1C[C@H]1CCCO1,O=c1c2c3nc4ccccc4nc3n(CCC3=CCCCC3)c2ncn1C[C@H]1CCCO1,O=c1c2c3nc4ccccc4nc3n(CCC3=CCCCC3)c2ncn1C[C@H]1CCCO1,3.257260117,4.3639,1.106639883
CC[NH+](C/C=C/c1ccc(C#N)cc1)C[C@H](C)C#N,CC[NH+](C/C=C/c1ccc(C#N)cc1)C[C@H](C)C#N,CC[NH+](C/C=C/c1ccc(C#N)cc1)C[C@H](C)C#N,4.36293875,1.63596,-2.72697875
COc1c(Cl)cc(C[NH2+][C@@H](Cc2c[nH]c3ccccc23)C(=O)[O-])cc1Cl,COc1c(Cl)cc(C[NH2+][C@@H](Cc2c[nH]c3ccccc23)C(=O)[O-])cc1Cl,COc1c(Cl)cc(C[NH2+][C@@H](Cc2c[nH]c3ccccc23)C(=O)[O-])cc1Cl,3.725368177,1.9079,-1.817468177
COc1ccccc1[C@H](C)NC(=O)C[C@H]1C[NH2+]CCO1,COc1ccccc1[C@H](C)NC(=O)C[C@H]1C[NH2+]CCO1,COc1ccccc1[C@H](C)NC(=O)C[C@H]1C[NH2+]CCO1,3.812797047,0.2247,-3.588097047
Cc1ccc(NC(=O)C(=O)NCCc2ccccc2F)c(C)c1,Cc1ccc(NC(=O)C(=O)NCCc2ccccc2F)c(C)c1,Cc1ccc(NC(=O)C(=O)NCCc2ccccc2F)c(C)c1,1.826753956,2.73994,0.913186044
XXX,XXX,XXX,3.228025092,3.2416,0.013574908
CC[C@H](C)C[NH+]1CCCC[C@@H]1C(=O)NC(C)(C)C,CC[C@H](C)C[NH+]1CCCC[C@@H]1C(=O)NC(C)(C)C,CC[C@H](C)C[NH+]1CCCC[C@@H]1C(=O)NC(C)(C)C,4.787067722,1.3846,-3.402467722
COC(=O)c1c(S(=O)(=O)NC2CC2)sc2c1CCN(Cc1ccc3ccccc3c1)C2,COC(=O)c1c(S(=O)(=O)NC2CC2)sc2c1CCN(Cc1ccc3ccccc3c1)C2,COC(=O)c1c(S(=O)(=O)NC2CC2)sc2c1CCN(Cc1ccc3ccccc3c1)C2,2.485526606,3.6869,1.201373394
Cc1cc(C)cc(NC(=O)[C@@H](Sc2nnnn2C2CC2)c2ccccc2)c1,Cc1cc(C)cc(NC(=O)[C@@H](Sc2nnnn2C2CC2)c2ccccc2)c1,Cc1cc(C)cc(NC(=O)[C@@H](Sc2nnnn2C2CC2)c2ccccc2)c1,2.708478583,4.09694,1.388461417
C=CCn1c([S-])nnc1-c1sc(NC(=O)c2cccc(NC(C)=O)c2)nc1C,C=CCn1c([S-])nnc1-c1sc(NC(=O)c2cccc(NC(C)=O)c2)nc1C,C=CCn1c([S-])nnc1-c1sc(NC(=O)c2cccc(NC(C)=O)c2)nc1C,2.943459024,3.01252,0.069060976
O=C(CNc1nc(C2CC2)no1)N1CCc2sccc2C1,O=C(CNc1nc(C2CC2)no1)N1CCc2sccc2C1,O=C(CNc1nc(C2CC2)no1)N1CCc2sccc2C1,2.781430253,2.0053,-0.776130253
Cc1ccccc1NC(=O)NCCn1c([N+](=O)[O-])cnc1C,Cc1ccccc1NC(=O)NCCn1c([N+](=O)[O-])cnc1C,Cc1ccccc1NC(=O)NCCn1c([N+](=O)[O-])cnc1C,2.258312512,2.22984,-0.028472512
C[C@H](NC=C(C#N)C#N)c1ccc(-n2cncn2)cc1,C[C@H](NC=C(C#N)C#N)c1ccc(-n2cncn2)cc1,C[C@H](NC=C(C#N)C#N)c1ccc(-n2cncn2)cc1,3.277555708,1.84896,-1.428595708
CN1C[C@H](C(=O)NC[C@H]2CCC(C)(C)c3ccccc32)CC1=O,CN1C[C@H](C(=O)NC[C@H]2CCC(C)(C)c3ccccc32)CC1=O,CN1C[C@H](C(=O)NC[C@H]2CCC(C)(C)c3ccccc32)CC1=O,3.259412472,2.4361,-0.823312472
CC(C)n1cccc1C(=O)N[C@H]1CCc2cc(N)ccc21,CC(C)n1cccc1C(=O)N[C@H]1CCc2cc(N)ccc21,CC(C)n1cccc1C(=O)N[C@H]1CCc2cc(N)ccc21,2.887282024,3.0685,0.181217976
CCN1C(=O)C(C#N)=C(C)/C(=C\Nc2ccc([N+](=O)[O-])cc2)C1=O,CCN1C(=O)C(C#N)=C(C)/C(=C\Nc2ccc([N+](=O)[O-])cc2)C1=O,CCN1C(=O)C(C#N)=C(C)/C(=C\Nc2ccc([N+](=O)[O-])cc2)C1=O,2.501067124,2.11938,-0.381687124
O=C(Nc1ccnn1Cc1cccc(Cl)c1)C1CCN(S(=O)(=O)c2ccccc2F)CC1,O=C(Nc1ccnn1Cc1cccc(Cl)c1)C1CCN(S(=O)(=O)c2ccccc2F)CC1,O=C(Nc1ccnn1Cc1cccc(Cl)c1)C1CCN(S(=O)(=O)c2ccccc2F)CC1,2.296421252,3.7633,1.466878748
COc1cc([C@@H]2N[C@H](C(=O)[O-])CS2)ccc1OC(=O)c1ccccc1,COc1cc([C@@H]2N[C@H](C(=O)[O-])CS2)ccc1OC(=O)c1ccccc1,COc1cc([C@@H]2N[C@H](C(=O)[O-])CS2)ccc1OC(=O)c1ccccc1,3.273511868,1.3679,-1.905611868
CCOc1ccc(C2=CCN(C(=O)c3ccccc3F)CC2)cc1,CCOc1ccc(C2=CCN(C(=O)c3ccccc3F)CC2)cc1,CCOc1ccc(C2=CCN(C(=O)c3ccccc3F)CC2)cc1,2.000346516,4.1539,2.153553484
CC(=O)N1CC[C@@H]([NH2+][C@@H](C)CSCC(C)C)C1,CC(=O)N1CC[C@@H]([NH2+][C@@H](C)CSCC(C)C)C1,CC(=O)N1CC[C@@H]([NH2+][C@@H](C)CSCC(C)C)C1,4.483674272,0.9483,-3.535374272
CCO[P@](=O)(CC(=O)[O-])c1ccccc1,CCO[P@](=O)(CC(=O)[O-])c1ccccc1,CCO[P@](=O)(CC(=O)[O-])c1ccccc1,3.510193788,0.3764,-3.133793788
O=C(CCc1nc2ccccc2c(=O)[nH]1)N1CCC[C@H]1C1CCCC1,O=C(CCc1nc2ccccc2c(=O)[nH]1)N1CCC[C@H]1C1CCCC1,O=C(CCc1nc2ccccc2c(=O)[nH]1)N1CCC[C@H]1C1CCCC1,2.774610155,3.0369,0.262289845
O=C(/C=C/SCc1ccco1)Nc1cccc(N2CCCC2=O)c1,O=C(/C=C/SCc1ccco1)Nc1cccc(N2CCCC2=O)c1,O=C(/C=C/SCc1ccco1)Nc1cccc(N2CCCC2=O)c1,2.584851266,3.792,1.207148734
CC(=O)c1c(C)[nH]c(C(=O)OCC(=O)N2C[C@H](C)C[C@@H](C)C2)c1C,CC(=O)c1c(C)[nH]c(C(=O)OCC(=O)N2C[C@H](C)C[C@@H](C)C2)c1C,CC(=O)c1c(C)[nH]c(C(=O)OCC(=O)N2C[C@H](C)C[C@@H](C)C2)c1C,3.068452859,2.49544,-0.573012859
Cc1ccc(C(=O)N2CCC[C@@H](C(=O)N(C)CC(=O)NC(C)C)C2)cc1,Cc1ccc(C(=O)N2CCC[C@@H](C(=O)N(C)CC(=O)NC(C)C)C2)cc1,Cc1ccc(C(=O)N2CCC[C@@H](C(=O)N(C)CC(=O)NC(C)C)C2)cc1,2.492863438,1.83022,-0.662643438
C[C@@H](C#N)Sc1nnc(C23CC4CC(CC(C4)C2)C3)o1,C[C@@H](C#N)Sc1nnc(C23CC4CC(CC(C4)C2)C3)o1,C[C@@H](C#N)Sc1nnc(C23CC4CC(CC(C4)C2)C3)o1,4.576896432,3.54158,-1.035316432
CC1(C)CCC[C@H]1n1c(N)[nH+]c2ccccc21,CC1(C)CCC[C@H]1n1c(N)[nH+]c2ccccc21,CC1(C)CCC[C@H]1n1c(N)[nH+]c2ccccc21,4.151966768,2.7888,-1.363166768
CCOc1ccc(C[NH+]2CCS[C@H]3COCC[C@@H]32)cc1OC,CCOc1ccc(C[NH+]2CCS[C@H]3COCC[C@@H]32)cc1OC,CCOc1ccc(C[NH+]2CCS[C@H]3COCC[C@@H]32)cc1OC,4.514122047,1.3831,-3.131022047
COc1cc(OC)c([C@@H](C)[NH2+]C[C@H](O)C2CCOCC2)cc1Cl,COc1cc(OC)c([C@@H](C)[NH2+]C[C@H](O)C2CCOCC2)cc1Cl,COc1cc(OC)c([C@@H](C)[NH2+]C[C@H](O)C2CCOCC2)cc1Cl,3.960984106,1.7691,-2.191884106
COC(C[C@@](C)(O)C[C@@H]1CCCN(S(C)(=O)=O)C1)OC,COC(C[C@@](C)(O)C[C@@H]1CCCN(S(C)(=O)=O)C1)OC,COC(C[C@@](C)(O)C[C@@H]1CCCN(S(C)(=O)=O)C1)OC,3.669896168,0.8081,-2.861796168
C=CCSc1nnc(C2CCOCC2)n1N,C=CCSc1nnc(C2CCOCC2)n1N,C=CCSc1nnc(C2CCOCC2)n1N,2.825013358,1.164,-1.661013358
O=C(Nc1c[nH]c2ccccc12)N1CCCC[C@@H]1CCO,O=C(Nc1c[nH]c2ccccc12)N1CCCC[C@@H]1CCO,O=C(Nc1c[nH]c2ccccc12)N1CCCC[C@@H]1CCO,2.674797385,2.9367,0.261902615
Cc1nc(-c2ccc(-c3noc(C4CC4)n3)cc2)cs1,Cc1nc(-c2ccc(-c3noc(C4CC4)n3)cc2)cs1,Cc1nc(-c2ccc(-c3noc(C4CC4)n3)cc2)cs1,2.133219774,4.04592,1.912700226
C[C@H]1CCCCN1C(=O)[C@@H](C)NC(=O)C(=O)Nc1ccnn1C(C)(C)C,C[C@H]1CCCCN1C(=O)[C@@H](C)NC(=O)C(=O)Nc1ccnn1C(C)(C)C,C[C@H]1CCCCN1C(=O)[C@@H](C)NC(=O)C(=O)Nc1ccnn1C(C)(C)C,3.418504414,1.4823,-1.936204414
c1cc2c(cc1N[C@@H]1CCOC3(CCC3)C1)CCC2,c1cc2c(cc1N[C@@H]1CCOC3(CCC3)C1)CCC2,c1cc2c(cc1N[C@@H]1CCOC3(CCC3)C1)CCC2,3.363061129,3.6889,0.325838871
O=C(Nc1ccc(CNC(=O)N2CCCCCCC2)cc1)c1ccco1,O=C(Nc1ccc(CNC(=O)N2CCCCCCC2)cc1)c1ccco1,O=C(Nc1ccc(CNC(=O)N2CCCCCCC2)cc1)c1ccco1,1.931563902,4.0076,2.076036098
CNC(=O)[C@H]1CCCCN1c1cccc(F)c1C(N)=S,CNC(=O)[C@H]1CCCCN1c1cccc(F)c1C(N)=S,CNC(=O)[C@H]1CCCCN1c1cccc(F)c1C(N)=S,2.82917551,1.5648,-1.26437551
O=C(NCc1nnc2n1CCC2)c1cc(-c2ccccc2)[nH]n1,O=C(NCc1nnc2n1CCC2)c1cc(-c2ccccc2)[nH]n1,O=C(NCc1nnc2n1CCC2)c1cc(-c2ccccc2)[nH]n1,2.376871447,1.5444,-0.832471447
Cc1nnc(-c2cc(CC(C)C)n(-c3cccc(C(=O)N[C@@H]4C[C@@H](C)CC(C)(C)C4)c3)n2)o1,Cc1nnc(-c2cc(CC(C)C)n(-c3cccc(C(=O)N[C@@H]4C[C@@H](C)CC(C)(C)C4)c3)n2)o1,Cc1nnc(-c2cc(CC(C)C)n(-c3cccc(C(=O)N[C@@H]4C[C@@H](C)CC(C)(C)C4)c3)n2)o1,3.548164288,5.37382,1.825655712
COc1cccc([C@@H]2CC(=O)C3=C(C2)NC(=O)C[C@H]3c2ccc(OC)c(OC)c2OC)c1,COc1cccc([C@@H]2CC(=O)C3=C(C2)NC(=O)C[C@H]3c2ccc(OC)c(OC)c2OC)c1,COc1cccc([C@@H]2CC(=O)C3=C(C2)NC(=O)C[C@H]3c2ccc(OC)c(OC)c2OC)c1,3.226440403,3.7253,0.498859597
C[NH2+]Cc1ccc(-c2cc(C)ccc2F)s1,C[NH2+]Cc1ccc(-c2cc(C)ccc2F)s1,C[NH2+]Cc1ccc(-c2cc(C)ccc2F)s1,3.128631552,2.55582,-0.572811552
CCC[C@H](NC(N)=O)C(=O)NC[C@@H]1CCCO1,CCC[C@H](NC(N)=O)C(=O)NC[C@@H]1CCCO1,CCC[C@H](NC(N)=O)C(=O)NC[C@@H]1CCCO1,2.869359841,0.1186,-2.750759841
COc1cc(F)cc(CNC(=O)[C@H]2CCCN2C(=O)Cc2ccccc2)c1,COc1cc(F)cc(CNC(=O)[C@H]2CCCN2C(=O)Cc2ccccc2)c1,COc1cc(F)cc(CNC(=O)[C@H]2CCCN2C(=O)Cc2ccccc2)c1,2.427176885,2.6842,0.257023115
CCc1cc(C#N)c(NC(=O)CCC(=O)N(CC)CC)s1,CCc1cc(C#N)c(NC(=O)CCC(=O)N(CC)CC)s1,CCc1cc(C#N)c(NC(=O)CCC(=O)N(CC)CC)s1,2.493551313,2.76928,0.275728687
C#Cc1cccc(NC(=O)C[NH+](C)[C@H](C)c2nnc(-c3ccccc3)o2)c1,C#Cc1cccc(NC(=O)C[NH+](C)[C@H](C)c2nnc(-c3ccccc3)o2)c1,C#Cc1cccc(NC(=O)C[NH+](C)[C@H](C)c2nnc(-c3ccccc3)o2)c1,3.779297449,1.9323,-1.846997449
CCOC[C@H](O)[C@](C)(CC)[NH+]1CCCC1,CCOC[C@H](O)[C@](C)(CC)[NH+]1CCCC1,CCOC[C@H](O)[C@](C)(CC)[NH+]1CCCC1,5.127905513,0.2312,-4.896705513
C[NH+](C)Cc1cccc(CNC(=O)NC[C@H](O)c2ccc(F)cc2)c1,C[NH+](C)Cc1cccc(CNC(=O)NC[C@H](O)c2ccc(F)cc2)c1,C[NH+](C)Cc1cccc(CNC(=O)NC[C@H](O)c2ccc(F)cc2)c1,3.24179101,1.003,-2.23879101
Cc1ccsc1C(=O)NCCNC(=O)c1ccco1,Cc1ccsc1C(=O)NCCNC(=O)c1ccco1,Cc1ccsc1C(=O)NCCNC(=O)c1ccco1,2.114582277,1.80932,-0.305262277
COC(=O)[C@H](C)N(Cc1ccccc1)C(=O)Cc1ccon1,COC(=O)[C@H](C)N(Cc1ccccc1)C(=O)Cc1ccon1,COC(=O)[C@H](C)N(Cc1ccccc1)C(=O)Cc1ccon1,2.852054716,1.8074,-1.044654716
C/C=C(/C)C(=O)NC[C@H]1C[C@@]12CCc1ccccc12,C/C=C(/C)C(=O)NC[C@H]1C[C@@]12CCc1ccccc12,C/C=C(/C)C(=O)NC[C@H]1C[C@@]12CCc1ccccc12,3.940919906,2.9729,-0.968019906
O=C1Nc2ccccc2Oc2cc([N+](=O)[O-])cc(Oc3ccc(Cl)cc3)c21,O=C1Nc2ccccc2Oc2cc([N+](=O)[O-])cc(Oc3ccc(Cl)cc3)c21,O=C1Nc2ccccc2Oc2cc([N+](=O)[O-])cc(Oc3ccc(Cl)cc3)c21,2.400946341,5.3985,2.997553659
CCc1onc(C)c1NC(=O)CCCC(C)(C)C,CCc1onc(C)c1NC(=O)CCCC(C)(C)C,CCc1onc(C)c1NC(=O)CCCC(C)(C)C,2.601600041,3.70032,1.098719959
COC(=O)C(C)(C)COc1ccc2c(c1)OC[C@@H]2[NH3+],COC(=O)C(C)(C)COc1ccc2c(c1)OC[C@@H]2[NH3+],COC(=O)C(C)(C)COc1ccc2c(c1)OC[C@@H]2[NH3+],3.576525387,0.94,-2.636525387
CC#CCCC(=O)Nc1cccc2c1C(=O)c1ccccc1C2=O,CC#CCCC(=O)Nc1cccc2c1C(=O)c1ccccc1C2=O,CC#CCCC(=O)Nc1cccc2c1C(=O)c1ccccc1C2=O,2.298959953,3.204,0.905040047
Cc1nc(C)c(S(=O)(=O)/N=C(\[O-])C[C@H]2CCCO2)s1,Cc1nc(C)c(S(=O)(=O)/N=C(\[O-])C[C@H]2CCCO2)s1,Cc1nc(C)c(S(=O)(=O)/N=C(\[O-])C[C@H]2CCCO2)s1,3.812988405,0.77654,-3.036448405
CCC(=O)N[C@H](CCSC)C(=O)Nc1ccc2[nH]c(C)cc2c1,CCC(=O)N[C@H](CCSC)C(=O)Nc1ccc2[nH]c(C)cc2c1,CCC(=O)N[C@H](CCSC)C(=O)Nc1ccc2[nH]c(C)cc2c1,2.732126257,3.06272,0.330593743
COCC[NH+](C)Cc1c(C)cc(C)c(C(C)=O)c1C,COCC[NH+](C)Cc1c(C)cc(C)c(C(C)=O)c1C,COCC[NH+](C)Cc1c(C)cc(C)c(C(C)=O)c1C,3.955103091,1.47556,-2.479543091
Cc1cscc1CNC(=O)N[C@@H](C)c1nc(C(=O)[O-])cs1,Cc1cscc1CNC(=O)N[C@@H](C)c1nc(C(=O)[O-])cs1,Cc1cscc1CNC(=O)N[C@@H](C)c1nc(C(=O)[O-])cs1,3.628458628,1.43692,-2.191538628
CNC(=O)NCC(=O)NC[C@H](c1cccnc1)C(C)C,CNC(=O)NCC(=O)NC[C@H](c1cccnc1)C(C)C,CNC(=O)NCC(=O)NC[C@H](c1cccnc1)C(C)C,2.795116489,0.8664,-1.928716489
COc1cccc(C(=O)N2CCC[C@H]2[C@H]2CCC[C@@H]2O)c1,COc1cccc(C(=O)N2CCC[C@H]2[C@H]2CCC[C@@H]2O)c1,COc1cccc(C(=O)N2CCC[C@H]2[C@H]2CCC[C@@H]2O)c1,3.002385916,2.4608,-0.541585916
COc1ccc2c(c1)[C@H]([NH2+][C@H](C)CCN1CCOCC1)CCCO2,COc1ccc2c(c1)[C@H]([NH2+][C@H](C)CCN1CCOCC1)CCCO2,COc1ccc2c(c1)[C@H]([NH2+][C@H](C)CCN1CCOCC1)CCCO2,3.789130146,1.5831,-2.206030146
COc1cccc(OCC(=O)N2CCN(c3nc4ccc(S(C)(=O)=O)cc4s3)CC2)c1,COc1cccc(OCC(=O)N2CCN(c3nc4ccc(S(C)(=O)=O)cc4s3)CC2)c1,COc1cccc(OCC(=O)N2CCN(c3nc4ccc(S(C)(=O)=O)cc4s3)CC2)c1,2.245396536,2.436,0.190603464
COc1cccc(-c2nc(C(=O)O[C@@H](C)CNC(C)=O)c(C)[nH]2)c1,COc1cccc(-c2nc(C(=O)O[C@@H](C)CNC(C)=O)c(C)[nH]2)c1,COc1cccc(-c2nc(C(=O)O[C@@H](C)CNC(C)=O)c(C)[nH]2)c1,2.86962049,2.07512,-0.79450049
CCO[C@H]1C[C@@H]1C(=O)Nc1ccc(Sc2nncs2)c(Cl)c1,CCO[C@H]1C[C@@H]1C(=O)Nc1ccc(Sc2nncs2)c(Cl)c1,CCO[C@H]1C[C@@H]1C(=O)Nc1ccc(Sc2nncs2)c(Cl)c1,3.445502237,3.7062,0.260697763
Cc1ccc(N(CC(=O)N2CCCC[C@H]2C)S(=O)(=O)c2c(C)nn(C)c2C)cc1,Cc1ccc(N(CC(=O)N2CCCC[C@H]2C)S(=O)(=O)c2c(C)nn(C)c2C)cc1,Cc1ccc(N(CC(=O)N2CCCC[C@H]2C)S(=O)(=O)c2c(C)nn(C)c2C)cc1,2.872766629,2.94166,0.068893371
COc1ccc(C(=O)Nc2nc(CC(=O)Nc3ccc(N(C)C)cc3)cs2)cc1,COc1ccc(C(=O)Nc2nc(CC(=O)Nc3ccc(N(C)C)cc3)cs2)cc1,COc1ccc(C(=O)Nc2nc(CC(=O)Nc3ccc(N(C)C)cc3)cs2)cc1,1.983649537,3.6512,1.667550463
O=c1c2ccccc2sn1-c1ncc(Br)s1,O=c1c2ccccc2sn1-c1ncc(Br)s1,O=c1c2ccccc2sn1-c1ncc(Br)s1,2.853247472,3.2712,0.417952528
Cc1ocnc1CNC(=O)N(C)Cc1ccc(OC(F)F)cc1,Cc1ocnc1CNC(=O)N(C)Cc1ccc(OC(F)F)cc1,Cc1ocnc1CNC(=O)N(C)Cc1ccc(OC(F)F)cc1,2.60278761,2.92602,0.32323239
COc1cc(Cl)c(C)cc1NC(=O)C(=O)NCc1ccccc1C[NH+](C)C,COc1cc(Cl)c(C)cc1NC(=O)C(=O)NCc1ccccc1C[NH+](C)C,COc1cc(Cl)c(C)cc1NC(=O)C(=O)NCc1ccccc1C[NH+](C)C,2.870575026,1.55642,-1.314155026
C[C@@H]([NH3+])c1cccc(Oc2cc(Br)ccc2Cl)c1,C[C@@H]([NH3+])c1cccc(Oc2cc(Br)ccc2Cl)c1,C[C@@H]([NH3+])c1cccc(Oc2cc(Br)ccc2Cl)c1,2.965149266,4.1977,1.232550734
CC(C)NS(=O)(=O)c1cccc(C(=O)NCc2ccco2)c1,CC(C)NS(=O)(=O)c1cccc(C(=O)NCc2ccco2)c1,CC(C)NS(=O)(=O)c1cccc(C(=O)NCc2ccco2)c1,1.961616674,1.8963,-0.065316674
CN(Cc1ncnn1C)C(=O)CSCc1ccccn1,CN(Cc1ncnn1C)C(=O)CSCc1ccccn1,CN(Cc1ncnn1C)C(=O)CSCc1ccccn1,2.529083407,1.1019,-1.427183407
CCOc1cc(/C=C(\C#N)C(=O)c2c[nH]c3cc(Cl)ccc23)ccc1OC,CCOc1cc(/C=C(\C#N)C(=O)c2c[nH]c3cc(Cl)ccc23)ccc1OC,CCOc1cc(/C=C(\C#N)C(=O)c2c[nH]c3cc(Cl)ccc23)ccc1OC,2.350767662,5.01848,2.667712338
CCOC(=O)Nc1ccc(NC(=O)N2C[C@@H](C)CC[C@H]2C)cc1C#N,CCOC(=O)Nc1ccc(NC(=O)N2C[C@@H](C)CC[C@H]2C)cc1C#N,CCOC(=O)Nc1ccc(NC(=O)N2C[C@@H](C)CC[C@H]2C)cc1C#N,3.067347555,3.77898,0.711632445
Cn1c(=O)oc2ccc(NC(=O)[C@@H]3CCN(C(=O)OC(C)(C)C)C3)cc21,Cn1c(=O)oc2ccc(NC(=O)[C@@H]3CCN(C(=O)OC(C)(C)C)C3)cc21,Cn1c(=O)oc2ccc(NC(=O)[C@@H]3CCN(C(=O)OC(C)(C)C)C3)cc21,2.748880773,2.327,-0.421880773
CC(C)N1CCO[C@@H]([C@H](O)c2cccs2)C1,CC(C)N1CCO[C@@H]([C@H](O)c2cccs2)C1,CC(C)N1CCO[C@@H]([C@H](O)c2cccs2)C1,3.356907136,1.8907,-1.466207136
CCN[C@H]1C[C@H](C)C[C@H](C)[C@@H]1[NH+]1C[C@@H](C)[C@@H](C)C1,CCN[C@H]1C[C@H](C)C[C@H](C)[C@@H]1[NH+]1C[C@@H](C)[C@@H](C)C1,CCN[C@H]1C[C@H](C)C[C@H](C)[C@@H]1[NH+]1C[C@@H](C)[C@@H](C)C1,5.501545361,1.5698,-3.931745361
CC1CCN(C(=O)N(CC[NH3+])C2CC2)CC1,CC1CCN(C(=O)N(CC[NH3+])C2CC2)CC1,CC1CCN(C(=O)N(CC[NH3+])C2CC2)CC1,3.063324,0.5446,-2.518724
COc1ccc(NC(=O)Cn2nc3c4scc(-c5ccc(F)cc5)c4ncn3c2=O)c(OC)c1,COc1ccc(NC(=O)Cn2nc3c4scc(-c5ccc(F)cc5)c4ncn3c2=O)c(OC)c1,COc1ccc(NC(=O)Cn2nc3c4scc(-c5ccc(F)cc5)c4ncn3c2=O)c(OC)c1,2.583698935,3.5677,0.984001065
CC(C)OC(=O)N1CCC(NC(=O)[C@H]2SCCc3ccccc32)CC1,CC(C)OC(=O)N1CCC(NC(=O)[C@H]2SCCc3ccccc32)CC1,CC(C)OC(=O)N1CCC(NC(=O)[C@H]2SCCc3ccccc32)CC1,3.087047482,3.1426,0.055552518
COc1ccc2c(c1)SC(=O)C2=O,COc1ccc2c(c1)SC(=O)C2=O,COc1ccc2c(c1)SC(=O)C2=O,2.401574365,1.5102,-0.891374365
COc1ccc(CCC(=O)N2CCC3(CC2)Nc2ccccc2-n2cccc23)cc1,COc1ccc(CCC(=O)N2CCC3(CC2)Nc2ccccc2-n2cccc23)cc1,COc1ccc(CCC(=O)N2CCC3(CC2)Nc2ccccc2-n2cccc23)cc1,2.913337418,4.3619,1.448562582
COc1ccccc1Cc1nnc(NC(=O)Cc2ccc(Br)cc2)s1,COc1ccccc1Cc1nnc(NC(=O)Cc2ccc(Br)cc2)s1,COc1ccccc1Cc1nnc(NC(=O)Cc2ccc(Br)cc2)s1,2.051226236,4.0812,2.029973764
CNC(=O)O/N=C(/N)c1ccc(Br)cc1,CNC(=O)O/N=C(/N)c1ccc(Br)cc1,CNC(=O)O/N=C(/N)c1ccc(Br)cc1,2.250317734,1.4254,-0.824917734
CC1(C)CN(C[C@@H](CS)c2ccccc2)CCO1,CC1(C)CN(C[C@@H](CS)c2ccccc2)CCO1,CC1(C)CN(C[C@@H](CS)c2ccccc2)CCO1,3.027019854,2.8108,-0.216219854
CCOC(=O)[C@H]1CCCN(C(=O)Cn2c(SC(F)F)nc3ccccc32)C1,CCOC(=O)[C@H]1CCCN(C(=O)Cn2c(SC(F)F)nc3ccccc32)C1,CCOC(=O)[C@H]1CCCN(C(=O)Cn2c(SC(F)F)nc3ccccc32)C1,2.799716077,3.1527,0.352983923
[NH3+][C@@H](Cc1ccccn1)c1ccc(Cl)cn1,[NH3+][C@@H](Cc1ccccn1)c1ccc(Cl)cn1,[NH3+][C@@H](Cc1ccccn1)c1ccc(Cl)cn1,3.324055225,1.6557,-1.668355225
CCOCCN(C)C(=O)C(=O)Nc1cccnc1-c1ccccc1,CCOCCN(C)C(=O)C(=O)Nc1cccnc1-c1ccccc1,CCOCCN(C)C(=O)C(=O)Nc1cccnc1-c1ccccc1,2.256607012,2.182,-0.074607012
CCn1cc([C@H](NC(=O)c2ccc(OC)c(OC)c2)c2ccc(F)cc2)c2cc[nH+]cc21,CCn1cc([C@H](NC(=O)c2ccc(OC)c(OC)c2)c2ccc(F)cc2)c2cc[nH+]cc21,CCn1cc([C@H](NC(=O)c2ccc(OC)c(OC)c2)c2ccc(F)cc2)c2cc[nH+]cc21,3.447988059,4.151,0.703011941
O=C([C@@H]1CC12CCOCC2)N1CC[C@@H](Oc2cccc(Cl)c2)C1,O=C([C@@H]1CC12CCOCC2)N1CC[C@@H](Oc2cccc(Cl)c2)C1,O=C([C@@H]1CC12CCOCC2)N1CC[C@@H](Oc2cccc(Cl)c2)C1,3.713785706,3.1364,-0.577385706
C[NH2+][C@]1(C(N)=O)CCC[C@H]1CCSC1CCOCC1,C[NH2+][C@]1(C(N)=O)CCC[C@H]1CCSC1CCOCC1,C[NH2+][C@]1(C(N)=O)CCC[C@H]1CCSC1CCOCC1,4.614268117,0.5061,-4.108168117
Cc1cc(NC(=O)c2ccc(-n3cncn3)nc2)ccc1N(C)C,Cc1cc(NC(=O)c2ccc(-n3cncn3)nc2)ccc1N(C)C,Cc1cc(NC(=O)c2ccc(-n3cncn3)nc2)ccc1N(C)C,2.267592306,2.28902,0.021427694
Cc1ccccc1CNC(=O)N1CCC([C@@H](O)c2ccc(Cl)cc2)CC1,Cc1ccccc1CNC(=O)N1CCC([C@@H](O)c2ccc(Cl)cc2)CC1,Cc1ccccc1CNC(=O)N1CCC([C@@H](O)c2ccc(Cl)cc2)CC1,2.524142332,4.30362,1.779477668
CCc1nc(CNCCc2cccs2)cs1,CCc1nc(CNCCc2cccs2)cs1,CCc1nc(CNCCc2cccs2)cs1,2.300594957,3.0993,0.798705043
O=C(Nc1ccc2nc(-c3cc(F)ccc3F)[nH]c2c1)c1ccc([N+](=O)[O-])cc1,O=C(Nc1ccc2nc(-c3cc(F)ccc3F)[nH]c2c1)c1ccc([N+](=O)[O-])cc1,O=C(Nc1ccc2nc(-c3cc(F)ccc3F)[nH]c2c1)c1ccc([N+](=O)[O-])cc1,2.145744096,4.6686,2.522855904
N#Cc1ccccc1NC(=O)C(=O)NC[C@H]1COC2(CCCC2)O1,N#Cc1ccccc1NC(=O)C(=O)NC[C@H]1COC2(CCCC2)O1,N#Cc1ccccc1NC(=O)C(=O)NC[C@H]1COC2(CCCC2)O1,3.412921889,1.29868,-2.114241889
O=C(NC1(C(=O)[O-])CC[NH2+]CC1)OCC1c2ccccc2-c2ccccc21,O=C(NC1(C(=O)[O-])CC[NH2+]CC1)OCC1c2ccccc2-c2ccccc21,O=C(NC1(C(=O)[O-])CC[NH2+]CC1)OCC1c2ccccc2-c2ccccc21,3.490460281,0.371,-3.119460281
Cc1ccc(C(=O)N2CCC[C@H]2CNC(=O)OC(C)(C)C)cc1,Cc1ccc(C(=O)N2CCC[C@H]2CNC(=O)OC(C)(C)C)cc1,Cc1ccc(C(=O)N2CCC[C@H]2CNC(=O)OC(C)(C)C)cc1,2.424440646,3.12432,0.699879354
C[NH+](C)CC(=O)NNC(=O)c1ccc(C2CCCCC2)cc1,C[NH+](C)CC(=O)NNC(=O)c1ccc(C2CCCCC2)cc1,C[NH+](C)CC(=O)NNC(=O)c1ccc(C2CCCCC2)cc1,2.819298295,0.6398,-2.179498295
C[C@@H]1CCCC[C@@H]1OCC(=O)N(C)Cc1cccc(O)c1,C[C@@H]1CCCC[C@@H]1OCC(=O)N(C)Cc1cccc(O)c1,C[C@@H]1CCCC[C@@H]1OCC(=O)N(C)Cc1cccc(O)c1,2.912373702,2.9459,0.033526298
C#CCNS(=O)(=O)c1ccc(C(=O)N[C@@H]2CCO[C@@]3(CCOC3)C2)cc1,C#CCNS(=O)(=O)c1ccc(C(=O)N[C@@H]2CCO[C@@]3(CCOC3)C2)cc1,C#CCNS(=O)(=O)c1ccc(C(=O)N[C@@H]2CCO[C@@]3(CCOC3)C2)cc1,3.898168643,0.666,-3.232168643
Cc1ccc(C2(CN3CCN(S(=O)(=O)N(C)C)CC3)CCCC2)cc1,Cc1ccc(C2(CN3CCN(S(=O)(=O)N(C)C)CC3)CCCC2)cc1,Cc1ccc(C2(CN3CCN(S(=O)(=O)N(C)C)CC3)CCCC2)cc1,2.439754533,2.23082,-0.208934533
Cc1sc2ncn(CCC(=O)NCC(N)=O)c(=O)c2c1C,Cc1sc2ncn(CCC(=O)NCC(N)=O)c(=O)c2c1C,Cc1sc2ncn(CCC(=O)NCC(N)=O)c(=O)c2c1C,2.347207183,0.06644,-2.280767183
COc1ccc(CCNC(=O)c2cc(C(C)C)nc3c2c(C)nn3C)cc1OC,COc1ccc(CCNC(=O)c2cc(C(C)C)nc3c2c(C)nn3C)cc1OC,COc1ccc(CCNC(=O)c2cc(C(C)C)nc3c2c(C)nn3C)cc1OC,2.282558575,3.38982,1.107261425
O=[N+]([O-])c1cnc(Br)s1,O=[N+]([O-])c1cnc(Br)s1,O=[N+]([O-])c1cnc(Br)s1,3.179818392,1.8138,-1.366018392
CC(C)(C)CC(=O)N1CCN(C(=O)c2ccn3ccsc23)CC1,CC(C)(C)CC(=O)N1CCN(C(=O)c2ccn3ccsc23)CC1,CC(C)(C)CC(=O)N1CCN(C(=O)c2ccn3ccsc23)CC1,2.712888331,2.7214,0.008511669
C/[NH+]=C(/NCCCc1cccc(Br)c1)NC1CC1,C/[NH+]=C(/NCCCc1cccc(Br)c1)NC1CC1,C/[NH+]=C(/NCCCc1cccc(Br)c1)NC1CC1,2.991361127,0.7897,-2.201661127
O=C(CCC1CCCC1)N1CCC(COc2ccccc2C(=O)N2CCCC2)CC1,O=C(CCC1CCCC1)N1CCC(COc2ccccc2C(=O)N2CCCC2)CC1,O=C(CCC1CCCC1)N1CCC(COc2ccccc2C(=O)N2CCCC2)CC1,2.19573155,4.5104,2.31466845
CC(C)(C)c1n[nH]cc1/C=C1\SC(NC2CCCCC2)=NC1=O,CC(C)(C)c1n[nH]cc1/C=C1\SC(NC2CCCCC2)=NC1=O,CC(C)(C)c1n[nH]cc1/C=C1\SC(NC2CCCCC2)=NC1=O,3.076214803,3.5998,0.523585197
CN(CC(F)(F)F)C(=O)CNC(=O)N1CCO[C@H](C#N)C1,CN(CC(F)(F)F)C(=O)CNC(=O)N1CCO[C@H](C#N)C1,CN(CC(F)(F)F)C(=O)CNC(=O)N1CCO[C@H](C#N)C1,3.449633505,-0.05892,-3.508553505
CC[C@@H](NC(=O)NCc1ccc(C#N)cc1)c1ccc(C)c(F)c1,CC[C@@H](NC(=O)NCc1ccc(C#N)cc1)c1ccc(C)c(F)c1,CC[C@@H](NC(=O)NCc1ccc(C#N)cc1)c1ccc(C)c(F)c1,2.506404532,3.9563,1.449895468
CC(C)c1noc(CCNC(=O)/C=C/c2cn(C)c3ccccc23)n1,CC(C)c1noc(CCNC(=O)/C=C/c2cn(C)c3ccccc23)n1,CC(C)c1noc(CCNC(=O)/C=C/c2cn(C)c3ccccc23)n1,2.489753078,3.0568,0.567046922
CC(C)=C1Oc2c3c(cc(C)c2C1=O)OCN(Cc1ccccc1)C3,CC(C)=C1Oc2c3c(cc(C)c2C1=O)OCN(Cc1ccccc1)C3,CC(C)=C1Oc2c3c(cc(C)c2C1=O)OCN(Cc1ccccc1)C3,2.840364996,4.21612,1.375755004
O=C1/C(=N/O)c2ccc(Cl)cc2N1Cc1ccccc1,O=C1/C(=N/O)c2ccc(Cl)cc2N1Cc1ccccc1,O=C1/C(=N/O)c2ccc(Cl)cc2N1Cc1ccccc1,2.0410745,3.0651,1.0240255
c1ccc(CC2(Cc3ccc4c(c3)CCC4)C[NH2+]C2)cc1,c1ccc(CC2(Cc3ccc4c(c3)CCC4)C[NH2+]C2)cc1,c1ccc(CC2(Cc3ccc4c(c3)CCC4)C[NH2+]C2)cc1,2.844717318,2.5239,-0.320817318
CC[C@H](C)NC(=O)N[C@H](c1ccccc1)C(F)(F)F,CC[C@H](C)NC(=O)N[C@H](c1ccccc1)C(F)(F)F,CC[C@H](C)NC(=O)N[C@H](c1ccccc1)C(F)(F)F,2.819638554,3.3877,0.568061446
NC(=O)COc1ccc2ccccc2c1C[NH2+]C1CCCCCCC1,NC(=O)COc1ccc2ccccc2c1C[NH2+]C1CCCCCCC1,NC(=O)COc1ccc2ccccc2c1C[NH2+]C1CCCCCCC1,2.95041288,2.8802,-0.07021288
O=C(COc1ccc(Cl)cc1[N+](=O)[O-])N1CCc2cc([N+](=O)[O-])ccc21,O=C(COc1ccc(Cl)cc1[N+](=O)[O-])N1CCc2cc([N+](=O)[O-])ccc21,O=C(COc1ccc(Cl)cc1[N+](=O)[O-])N1CCc2cc([N+](=O)[O-])ccc21,2.251658336,3.1245,0.872841664
O=C(Cc1ccc(F)cc1F)NC1CCN(c2cccc(F)c2)CC1,O=C(Cc1ccc(F)cc1F)NC1CCN(c2cccc(F)c2)CC1,O=C(Cc1ccc(F)cc1F)NC1CCN(c2cccc(F)c2)CC1,2.033335093,3.4316,1.398264907
COC(=O)c1ccc(C[NH+](C2CC2)[C@@H](C)c2ccco2)cc1F,COC(=O)c1ccc(C[NH+](C2CC2)[C@@H](C)c2ccco2)cc1F,COC(=O)c1ccc(C[NH+](C2CC2)[C@@H](C)c2ccco2)cc1F,4.038401414,2.5138,-1.524601414
Cc1ccc([C@@H]2C[NH2+]CC[C@@H]2c2c(F)cccc2F)cc1,Cc1ccc([C@@H]2C[NH2+]CC[C@@H]2c2c(F)cccc2F)cc1,Cc1ccc([C@@H]2C[NH2+]CC[C@@H]2c2c(F)cccc2F)cc1,3.825954589,3.10772,-0.718234589
Cc1ccc(-c2ccc(=O)n(CC(=O)NCc3ccco3)n2)c(C)c1,Cc1ccc(-c2ccc(=O)n(CC(=O)NCc3ccco3)n2)c(C)c1,Cc1ccc(-c2ccc(=O)n(CC(=O)NCc3ccco3)n2)c(C)c1,2.180504107,2.43654,0.256035893
CCNC(=O)NC(=O)CSc1nnc(N2C[C@@H](C)C[C@@H](C)C2)n1Cc1ccco1,CCNC(=O)NC(=O)CSc1nnc(N2C[C@@H](C)C[C@@H](C)C2)n1Cc1ccco1,CCNC(=O)NC(=O)CSc1nnc(N2C[C@@H](C)C[C@@H](C)C2)n1Cc1ccco1,3.320744633,2.3395,-0.981244633
CC(C)(C)NC(=O)N1CCC(CNC(=O)C(=O)Nc2ccccc2[N+](=O)[O-])CC1,CC(C)(C)NC(=O)N1CCC(CNC(=O)C(=O)Nc2ccccc2[N+](=O)[O-])CC1,CC(C)(C)NC(=O)N1CCC(CNC(=O)C(=O)Nc2ccccc2[N+](=O)[O-])CC1,2.351664532,1.8696,-0.482064532
Cc1ccc(-n2cnnn2)cc1NC(=O)c1cnn(Cc2ccccc2)c1,Cc1ccc(-n2cnnn2)cc1NC(=O)c1cnn(Cc2ccccc2)c1,Cc1ccc(-n2cnnn2)cc1NC(=O)c1cnn(Cc2ccccc2)c1,2.221673771,2.46782,0.246146229
O=C(Nc1ccc(F)cc1OCC(F)F)c1cccnc1N1CCCC1,O=C(Nc1ccc(F)cc1OCC(F)F)c1cccnc1N1CCCC1,O=C(Nc1ccc(F)cc1OCC(F)F)c1cccnc1N1CCCC1,2.273178554,3.7171,1.443921446
CCCNC(=O)CCNC(=O)CN1CCC([NH3+])CC1,CCCNC(=O)CCNC(=O)CN1CCC([NH3+])CC1,CCCNC(=O)CCNC(=O)CN1CCC([NH3+])CC1,2.682391179,-1.2748,-3.957191179
COCCNC(=O)COc1cc2c(c3oc(=O)c4c(c13)CCC4)CCC(C)(C)O2,COCCNC(=O)COc1cc2c(c3oc(=O)c4c(c13)CCC4)CCC(C)(C)O2,COCCNC(=O)COc1cc2c(c3oc(=O)c4c(c13)CCC4)CCC(C)(C)O2,2.819768111,2.5267,-0.293068111
CC(=O)N[C@@H](CC(=O)Nc1cnn(C)c1)c1cccs1,CC(=O)N[C@@H](CC(=O)Nc1cnn(C)c1)c1cccs1,CC(=O)N[C@@H](CC(=O)Nc1cnn(C)c1)c1cccs1,2.760825185,1.6876,-1.073225185
O=C(c1cccc(S(=O)(=O)[N-]c2ccccc2F)c1)N1CCC[C@@H]1c1nc2ccccc2[nH]1,O=C(c1cccc(S(=O)(=O)[N-]c2ccccc2F)c1)N1CCC[C@@H]1c1nc2ccccc2[nH]1,O=C(c1cccc(S(=O)(=O)[N-]c2ccccc2F)c1)N1CCC[C@@H]1c1nc2ccccc2[nH]1,3.413113671,5.0734,1.660286329
C[C@H](CNC(=O)c1[nH]nc(C2CC2)c1Cl)Oc1cccc(F)c1,C[C@H](CNC(=O)c1[nH]nc(C2CC2)c1Cl)Oc1cccc(F)c1,C[C@H](CNC(=O)c1[nH]nc(C2CC2)c1Cl)Oc1cccc(F)c1,3.06692193,3.2769,0.20997807
COc1ccccc1C(=O)/C(C#N)=C\c1cnc(-c2ccccn2)s1,COc1ccccc1C(=O)/C(C#N)=C\c1cnc(-c2ccccn2)s1,COc1ccccc1C(=O)/C(C#N)=C\c1cnc(-c2ccccn2)s1,2.549035852,4.00358,1.454544148
O=C(NCc1ccco1)[C@H]1CCCN1C(=O)C[NH+]1CCc2sccc2C1,O=C(NCc1ccco1)[C@H]1CCCN1C(=O)C[NH+]1CCc2sccc2C1,O=C(NCc1ccco1)[C@H]1CCCN1C(=O)C[NH+]1CCc2sccc2C1,4.132206287,0.5895,-3.542706287
CC[C@@H]1CN(C(=O)c2ccc3c(c2)OCO3)CC[NH2+]1,CC[C@@H]1CN(C(=O)c2ccc3c(c2)OCO3)CC[NH2+]1,CC[C@@H]1CN(C(=O)c2ccc3c(c2)OCO3)CC[NH2+]1,3.69238642,0.2131,-3.47928642
Cc1cc(C(=O)Nc2ccc(F)cc2C)on1,Cc1cc(C(=O)Nc2ccc(F)cc2C)on1,Cc1cc(C(=O)Nc2ccc(F)cc2C)on1,1.934252507,2.68284,0.748587493
C[C@@H](N[C@H](C[C@@H]1CCOC1)c1ccccc1)c1ccc([N+](=O)[O-])cc1,C[C@@H](N[C@H](C[C@@H]1CCOC1)c1ccccc1)c1ccc([N+](=O)[O-])cc1,C[C@@H](N[C@H](C[C@@H]1CCOC1)c1ccccc1)c1ccc([N+](=O)[O-])cc1,3.168218916,4.4133,1.245081084
O=C(NNS(=O)(=O)c1ccc(F)cc1)c1cc2c(s1)CCCC2,O=C(NNS(=O)(=O)c1ccc(F)cc1)c1cc2c(s1)CCCC2,O=C(NNS(=O)(=O)c1ccc(F)cc1)c1cc2c(s1)CCCC2,2.219578826,2.3893,0.169721174
Cc1cc(C#N)ccc1Oc1ccc([N+](=O)[O-])cc1C#N,Cc1cc(C#N)ccc1Oc1ccc([N+](=O)[O-])cc1C#N,Cc1cc(C#N)ccc1Oc1ccc([N+](=O)[O-])cc1C#N,2.231562188,3.43888,1.207317812
COc1ccc(-c2nc(CNC(=O)c3ccccc3)n[nH]2)cc1,COc1ccc(-c2nc(CNC(=O)c3ccccc3)n[nH]2)cc1,COc1ccc(-c2nc(CNC(=O)c3ccccc3)n[nH]2)cc1,1.980697063,2.4103,0.429602937
Cc1cccc(OCC(=O)N/N=C/c2cc(Br)c(O)c(Br)c2O)c1,Cc1cccc(OCC(=O)N/N=C/c2cc(Br)c(O)c(Br)c2O)c1,Cc1cccc(OCC(=O)N/N=C/c2cc(Br)c(O)c(Br)c2O)c1,2.453904832,3.46032,1.006415168
CC1=Nc2ccccc2Oc2c1c(=O)n(-c1ccccc1)c1ccccc21,CC1=Nc2ccccc2Oc2c1c(=O)n(-c1ccccc1)c1ccccc21,CC1=Nc2ccccc2Oc2c1c(=O)n(-c1ccccc1)c1ccccc21,2.458237938,5.2371,2.778862062
COC[C@@](C)(O)CNC(=O)[C@@H]1CC(=O)N(c2ccc(F)cc2)C1,COC[C@@](C)(O)CNC(=O)[C@@H]1CC(=O)N(c2ccc(F)cc2)C1,COC[C@@](C)(O)CNC(=O)[C@@H]1CC(=O)N(c2ccc(F)cc2)C1,3.005123049,0.6922,-2.312923049
Fc1ccccc1[C@@H]([NH2+]Cc1ccoc1)C1CCCC1,Fc1ccccc1[C@@H]([NH2+]Cc1ccoc1)C1CCCC1,Fc1ccccc1[C@@H]([NH2+]Cc1ccoc1)C1CCCC1,3.69757193,3.4136,-0.28397193
CC1CCN(C(=O)CN2C(=O)N[C@](C)(c3ccc(Cl)cc3Cl)C2=O)CC1,CC1CCN(C(=O)CN2C(=O)N[C@](C)(c3ccc(Cl)cc3Cl)C2=O)CC1,CC1CCN(C(=O)CN2C(=O)N[C@](C)(c3ccc(Cl)cc3Cl)C2=O)CC1,2.732754879,3.0189,0.286145121
COc1cccc(NC(=O)NCc2ccc3c(c2)N(C(=O)c2cccc(C)c2)CC3)c1,COc1cccc(NC(=O)NCc2ccc3c(c2)N(C(=O)c2cccc(C)c2)CC3)c1,COc1cccc(NC(=O)NCc2ccc3c(c2)N(C(=O)c2cccc(C)c2)CC3)c1,2.09420016,4.52822,2.43401984
O=C(/C=C/c1ccccc1)NC(=S)Nc1cc([N+](=O)[O-])ccc1F,O=C(/C=C/c1ccccc1)NC(=S)Nc1cc([N+](=O)[O-])ccc1F,O=C(/C=C/c1ccccc1)NC(=S)Nc1cc([N+](=O)[O-])ccc1F,2.105372184,3.2603,1.154927816
CCCn1nccc1NC(=O)c1nnn(-c2ccc(C)cc2)c1C,CCCn1nccc1NC(=O)c1nnn(-c2ccc(C)cc2)c1C,CCCn1nccc1NC(=O)c1nnn(-c2ccc(C)cc2)c1C,2.315214931,2.74294,0.427725069
COc1cccc(-c2nc(CC(=O)N[C@@H]3CCS(=O)(=O)C3)cs2)c1,COc1cccc(-c2nc(CC(=O)N[C@@H]3CCS(=O)(=O)C3)cs2)c1,COc1cccc(-c2nc(CC(=O)N[C@@H]3CCS(=O)(=O)C3)cs2)c1,2.722418907,1.6645,-1.057918907
COc1cc(NC(=O)c2cnn(-c3ccccc3)n2)cc(OC)c1OC,COc1cc(NC(=O)c2cnn(-c3ccccc3)n2)cc(OC)c1OC,COc1cc(NC(=O)c2cnn(-c3ccccc3)n2)cc(OC)c1OC,2.151091028,2.5454,0.394308972
COc1ccc2c(c1)cc(C(=O)N[C@H](C)Cc1cnccn1)n2C,COc1ccc2c(c1)cc(C(=O)N[C@H](C)Cc1cnccn1)n2C,COc1ccc2c(c1)cc(C(=O)N[C@H](C)Cc1cnccn1)n2C,2.807843567,2.3379,-0.469943567
CC(C)COCCNC(=O)NNc1ccccc1Cl,CC(C)COCCNC(=O)NNc1ccccc1Cl,CC(C)COCCNC(=O)NNc1ccccc1Cl,2.165718029,2.6387,0.472981971
CCC[C@@H](C)[NH2+]C[C@H](C)Oc1cccnc1C,CCC[C@@H](C)[NH2+]C[C@H](C)Oc1cccnc1C,CCC[C@@H](C)[NH2+]C[C@H](C)Oc1cccnc1C,4.126301903,1.90932,-2.216981903
Cc1ccc(C)c(OCCSc2ccc(N)c(C)c2)c1,Cc1ccc(C)c(OCCSc2ccc(N)c(C)c2)c1,Cc1ccc(C)c(OCCSc2ccc(N)c(C)c2)c1,2.060838323,4.36516,2.304321677
C[C@H](CC#N)N(C)C[C@@H]1CSc2ccccc21,C[C@H](CC#N)N(C)C[C@@H]1CSc2ccccc21,C[C@H](CC#N)N(C)C[C@@H]1CSc2ccccc21,3.590360435,3.10988,-0.480480435
CCN[C@@]1(C#N)CC[C@@H](Oc2cccc(F)c2F)C1,CCN[C@@]1(C#N)CC[C@@H](Oc2cccc(F)c2F)C1,CCN[C@@]1(C#N)CC[C@@H](Oc2cccc(F)c2F)C1,3.52564354,2.76798,-0.75766354
Cc1cc(C[NH2+][C@@H](CO)C(C)C)c(C)n1-c1ccccc1,Cc1cc(C[NH2+][C@@H](CO)C(C)C)c(C)n1-c1ccccc1,Cc1cc(C[NH2+][C@@H](CO)C(C)C)c(C)n1-c1ccccc1,3.596987275,2.17444,-1.422547275
CC1CCC(NC(=O)C(=O)Nc2cccc(-c3nncn3C)c2)CC1,CC1CCC(NC(=O)C(=O)Nc2cccc(-c3nncn3C)c2)CC1,CC1CCC(NC(=O)C(=O)Nc2cccc(-c3nncn3C)c2)CC1,2.347143544,2.1155,-0.231643544
CC(=O)Nc1cccc(C(=O)/C=C/c2ccco2)c1,CC(=O)Nc1cccc(C(=O)/C=C/c2ccco2)c1,CC(=O)Nc1cccc(C(=O)/C=C/c2ccco2)c1,2.003848944,3.1341,1.130251056
CCCNC(=O)c1cccc(NC(=O)C(=O)N2C[C@H]3CC=CC[C@@H]3C2)c1,CCCNC(=O)c1cccc(NC(=O)C(=O)N2C[C@H]3CC=CC[C@@H]3C2)c1,CCCNC(=O)c1cccc(NC(=O)C(=O)N2C[C@H]3CC=CC[C@@H]3C2)c1,3.148539376,2.1895,-0.959039376
Cc1nc(C)c(C(=O)N2CCN(C(=O)Cc3ccc(Cl)cc3)CC2)o1,Cc1nc(C)c(C(=O)N2CCN(C(=O)Cc3ccc(Cl)cc3)CC2)o1,Cc1nc(C)c(C(=O)N2CCN(C(=O)Cc3ccc(Cl)cc3)CC2)o1,2.226794753,2.47194,0.245145247
CCc1nc(CN/C(=[NH+]/C)N2CC[NH+](CC(C)C)CC2)cs1,CCc1nc(CN/C(=[NH+]/C)N2CC[NH+](CC(C)C)CC2)cs1,CCc1nc(CN/C(=[NH+]/C)N2CC[NH+](CC(C)C)CC2)cs1,4.614668767,-1.282,-5.896668767
CN(C)c1ccc(C(=O)Nc2c3c(nn2C)CCC3)cc1,CN(C)c1ccc(C(=O)Nc2c3c(nn2C)CCC3)cc1,CN(C)c1ccc(C(=O)Nc2c3c(nn2C)CCC3)cc1,2.254443099,2.2271,-0.027343099
O=C1[C@H](Nc2ccccc2O[C@H]2CCOC2)CCCN1Cc1ccccc1,O=C1[C@H](Nc2ccccc2O[C@H]2CCOC2)CCCN1Cc1ccccc1,O=C1[C@H](Nc2ccccc2O[C@H]2CCOC2)CCCN1Cc1ccccc1,2.979200776,3.4574,0.478199224
N#Cc1cccc(CNC(=O)CCOc2ccc(C=O)cc2)c1,N#Cc1cccc(CNC(=O)CCOc2ccc(C=O)cc2)c1,N#Cc1cccc(CNC(=O)CCOc2ccc(C=O)cc2)c1,1.99088313,2.45608,0.46519687
COc1ccc(CCn2cc(C(=O)[O-])c(=O)[nH]c2=O)cc1,COc1ccc(CCn2cc(C(=O)[O-])c(=O)[nH]c2=O)cc1,COc1ccc(CCn2cc(C(=O)[O-])c(=O)[nH]c2=O)cc1,2.453088713,-0.8486,-3.301688713
CN(CCc1ccccc1)C(=O)C(=O)Nc1ccc(Oc2ccncc2)cc1,CN(CCc1ccccc1)C(=O)C(=O)Nc1ccc(Oc2ccncc2)cc1,CN(CCc1ccccc1)C(=O)C(=O)Nc1ccc(Oc2ccncc2)cc1,2.089518656,3.5135,1.423981344
Cc1cc(C)cc(NC(=O)[C@H]2CCCN(S(=O)(=O)c3cn(C)cn3)C2)c1,Cc1cc(C)cc(NC(=O)[C@H]2CCCN(S(=O)(=O)c3cn(C)cn3)C2)c1,Cc1cc(C)cc(NC(=O)[C@H]2CCCN(S(=O)(=O)c3cn(C)cn3)C2)c1,2.830417176,2.07634,-0.754077176
Cc1nc(CNC(=O)[C@@H]2CCCCN2C(=O)OC(C)(C)C)sc1C,Cc1nc(CNC(=O)[C@@H]2CCCCN2C(=O)OC(C)(C)C)sc1C,Cc1nc(CNC(=O)[C@@H]2CCCCN2C(=O)OC(C)(C)C)sc1C,2.787253271,3.16574,0.378486729
CCSC1=NC(=O)[C@H]2C(=N1)NC1=C(C(=O)CCC1)[C@H]2c1cccnc1,CCSC1=NC(=O)[C@H]2C(=N1)NC1=C(C(=O)CCC1)[C@H]2c1cccnc1,CCSC1=NC(=O)[C@H]2C(=N1)NC1=C(C(=O)CCC1)[C@H]2c1cccnc1,4.10432105,2.4395,-1.66482105
COCC[C@H](C)C(=O)Nc1ccc(Oc2cc[nH+]c3ccccc23)cc1,COCC[C@H](C)C(=O)Nc1ccc(Oc2cc[nH+]c3ccccc23)cc1,COCC[C@H](C)C(=O)Nc1ccc(Oc2cc[nH+]c3ccccc23)cc1,3.383061791,4.0573,0.674238209
COC(=O)c1sccc1S(=O)(=O)N1CCC[C@@H](NC(C)=O)C1,COC(=O)c1sccc1S(=O)(=O)N1CCC[C@@H](NC(C)=O)C1,COC(=O)c1sccc1S(=O)(=O)N1CCC[C@@H](NC(C)=O)C1,2.758371043,0.8239,-1.934471043
CC(=O)O[C@H]1CC[C@]2(C)[C@@H]3CC[C@@]4(C)[C@@H](C[C@@H]5CCCC[C@@]54C(C)=O)[C@@H]3C[C@@H](O)[C@@]2(O)C1,CC(=O)O[C@H]1CC[C@]2(C)[C@@H]3CC[C@@]4(C)[C@@H](C[C@@H]5CCCC[C@@]54C(C)=O)[C@@H]3C[C@@H](O)[C@@]2(O)C1,CC(=O)O[C@H]1CC[C@]2(C)[C@@H]3CC[C@@]4(C)[C@@H](C[C@@H]5CCCC[C@@]54C(C)=O)[C@@H]3C[C@@H](O)[C@@]2(O)C1,4.807691125,4.422,-0.385691125
CCNS(=O)(=O)[C@H]1CCN(CCSc2ccccc2)C1,CCNS(=O)(=O)[C@H]1CCN(CCSc2ccccc2)C1,CCNS(=O)(=O)[C@H]1CCN(CCSc2ccccc2)C1,2.893618866,1.7923,-1.101318866
CC(C)OCCCNC(=O)N1CCC[NH+](CC2CCCCC2)CC1,CC(C)OCCCNC(=O)N1CCC[NH+](CC2CCCCC2)CC1,CC(C)OCCCNC(=O)N1CCC[NH+](CC2CCCCC2)CC1,3.869396076,1.682,-2.187396076
================================================
FILE: tests/data/micro_ZINC_shard_1.csv
================================================
SMILES,SA,logp,score
CCc1ccc(C(=O)/C(=C(/S)NC2CC2)[n+]2ccc(CC)cc2)cc1,2.775189478,3.7897,1.014510522
C=CCOc1ccc(C(F)(F)F)cc1C(=O)NC[C@H]1CCC[C@H]1O,3.071497898,3.161,0.089502102
COc1cc(OC)cc([C@@H](NC(=O)Cn2ccccc2=O)c2nccn2C)c1,2.84000896,1.5048,-1.33520896
CN1CCC[C@@]2(CCN(C(=O)CN3CCNC(=O)C3)C2)C1=O,3.605168457,-1.1109,-4.716068457
COc1ccc(Cl)cc1NC(=O)c1nn(C)cc1[N+](=O)[O-],2.132664673,2.2426,0.109935327
Cc1nn(-c2ccccc2)c(O)c1/C=[NH+]/Cc1ccncc1,3.117639223,0.98102,-2.136619223
C=CCN1C(=O)C(C)(C)COc2ccc(NC(=O)C3(c4ccc(OC)cc4)CCOCC3)cc21,2.744789746,4.3197,1.574910254
CON(C)C(=O)c1cnn2c(C)cc(C)nc12,2.547847184,0.97954,-1.568307184
NC(=O)[C@@H](NC(=O)CCCc1cccs1)c1ccccc1,2.444743614,2.4136,-0.031143614
COc1cccc(CN2CCC[NH+](CC(=O)Nc3ccc(F)cc3)S2(=O)=O)c1,3.546311784,0.8084,-2.737911784
================================================
FILE: tests/data/micro_ZINC_shard_2.csv
================================================
SMILES,SA,logp,score
Cn1c(=O)oc2ccc(NC(=O)[C@@H]3CCN(C(=O)OC(C)(C)C)C3)cc21,2.748880773,2.327,-0.421880773
CC(C)N1CCO[C@@H]([C@H](O)c2cccs2)C1,3.356907136,1.8907,-1.466207136
CCN[C@H]1C[C@H](C)C[C@H](C)[C@@H]1[NH+]1C[C@@H](C)[C@@H](C)C1,5.501545361,1.5698,-3.931745361
CC1CCN(C(=O)N(CC[NH3+])C2CC2)CC1,3.063324,0.5446,-2.518724
COc1ccc(NC(=O)Cn2nc3c4scc(-c5ccc(F)cc5)c4ncn3c2=O)c(OC)c1,2.583698935,3.5677,0.984001065
CC(C)OC(=O)N1CCC(NC(=O)[C@H]2SCCc3ccccc32)CC1,3.087047482,3.1426,0.055552518
COc1ccc2c(c1)SC(=O)C2=O,2.401574365,1.5102,-0.891374365
COc1ccc(CCC(=O)N2CCC3(CC2)Nc2ccccc2-n2cccc23)cc1,2.913337418,4.3619,1.448562582
COc1ccccc1Cc1nnc(NC(=O)Cc2ccc(Br)cc2)s1,2.051226236,4.0812,2.029973764
CNC(=O)O/N=C(/N)c1ccc(Br)cc1,2.250317734,1.4254,-0.824917734
================================================
FILE: tests/data/pcqm4mv2-2k.csv
================================================
smiles,homo
"[H]C1C2=C(NC(=O)[C@@]1([H])C1=C([H])C([H])=C(C([H])([H])[H])C([H])=C1[H])C([H])=C([H])N=C2[H] |(6.4528,-1.5789,-1.2859;5.789,-0.835,-0.8455;4.8499,-0.2104,-1.5946;3.9134,0.7241,-0.934;3.9796,1.1019,0.3172;5.0405,0.6404,1.1008;5.2985,1.1457,2.1772;5.9121,-0.5519,0.613;6.9467,-0.2303,0.8014;5.677,-1.7955,1.4745;4.7751,-2.7953,1.0929;4.2336,-2.7113,0.154;4.5521,-3.9001,1.914;3.8445,-4.6636,1.5979;5.215,-4.0391,3.1392;4.9919,-5.2514,4.0126;5.1819,-5.0262,5.0671;5.6619,-6.0746,3.7296;3.966,-5.6247,3.925;6.1051,-3.0257,3.52;6.6247,-3.101,4.4725;6.3372,-1.9217,2.7029;7.0168,-1.1395,3.0281;2.8586,1.2252,-1.7853;2.1303,1.9004,-1.3493;2.8118,0.8707,-3.0956;2.0282,1.2549,-3.7434;3.716,0.0207,-3.7371;4.6658,-0.476,-3.0127;5.3755,-1.1468,-3.5021)|",3.0476751256
"[H]C1=C(OC([H])([H])[H])C([H])=C(OC([H])([H])[H])C(/C([H])=C(\[H])N(C(=O)C([H])([H])[H])C([H])([H])[H])=C1[H] |(2.1307,2.7235,7.6863;1.9474,1.819,7.119;1.0098,0.873,7.538;0.2593,0.9658,8.6759;0.4432,2.1011,9.5057;-0.2404,1.9714,10.3469;0.1957,3.0318,8.978;1.473,2.1641,9.8816;0.7754,-0.2729,6.7631;0.0389,-0.9814,7.1205;1.4835,-0.4763,5.5827;1.3108,-1.5731,4.7848;0.3366,-2.535,5.1551;0.3625,-3.3009,4.3776;-0.6682,-2.0951,5.1999;0.5723,-2.9938,6.1242;2.4537,0.4611,5.1348;3.183,0.2123,3.8873;2.7052,-0.4677,3.1921;4.387,0.7439,3.5935;4.8961,1.3664,4.3173;5.0856,0.5626,2.3959;6.3568,1.0842,2.1622;6.9369,0.8984,1.1006;7.0068,1.897,3.2727;7.1466,1.3059,4.1845;6.4174,2.7835,3.5327;7.9816,2.217,2.9038;4.465,-0.2257,1.3353;4.3066,-1.2574,1.6696;5.134,-0.2156,0.4776;3.4967,0.2086,1.0638;2.6396,1.6,5.9257;3.3352,2.3628,5.587)|",4.410965516605
"[H]C([H])=C([H])C([H])([H])N(C(=O)C([H])([H])[H])/C([H])=C(\[H])C1=C(C([H])([H])[H])C([H])=C([H])C([H])=C1[H] |(5.6398,-3.9451,-3.6978;5.5212,-2.8688,-3.6095;6.4169,-2.2685,-3.7605;4.3388,-2.3123,-3.3477;3.4569,-2.937,-3.2114;4.0912,-0.8272,-3.2721;3.4597,-0.5217,-4.1102;5.0313,-0.2687,-3.3174;3.3696,-0.4136,-2.0566;1.9835,-0.2888,-2.1584;1.409,-0.5259,-3.2127;1.2104,0.1511,-0.925;1.3377,-0.5489,-0.0921;0.157,0.1862,-1.2039;1.5195,1.1441,-0.5796;4.0697,-0.1952,-0.866;3.4734,0.2457,-0.0772;5.369,-0.4755,-0.639;5.9379,-1.0039,-1.3951;6.0662,-0.1542,0.6153;7.1526,-0.9547,1.0515;7.6182,-2.1495,0.2508;8.4165,-2.683,0.7758;8.0123,-1.8565,-0.7318;6.8033,-2.8593,0.0652;7.7894,-0.6353,2.2548;8.6184,-1.2554,2.5888;7.3853,0.4487,3.0347;7.9,0.6726,3.9652;6.3255,1.2451,2.602;6.0086,2.1051,3.1863;5.685,0.9485,1.4018;4.892,1.6015,1.0472)|",4.639541151025
"[H]C([H])=C([H])C([H])([H])N(C(=O)C([H])([H])[H])/C([H])=C(\[H])C1=C(F)C([H])=C([H])C([H])=C1[H] |(5.8405,2.9679,-4.1686;5.4898,1.975,-3.902;6.1513,1.1454,-4.1461;4.3093,1.7822,-3.3149;3.6657,2.6305,-3.0846;3.7368,0.4303,-2.9728;4.4721,-0.3627,-3.1422;2.8623,0.2362,-3.5985;3.2588,0.3282,-1.5827;1.891,0.5151,-1.3701;1.1412,0.7684,-2.3024;1.3602,0.392,0.0503;1.8224,1.1205,0.7253;1.5301,-0.608,0.4653;0.2869,0.5781,0.0063;4.1604,0.0686,-0.5486;3.6884,-0.1102,0.4087;5.5037,0.0223,-0.656;5.9814,0.2798,-1.5923;6.4088,-0.3052,0.4474;7.7788,-0.0242,0.3174;8.2146,0.5382,-0.8366;8.7136,-0.2875,1.3076;9.754,-0.0358,1.1291;8.2861,-0.865,2.5032;9.005,-1.0788,3.2885;6.9338,-1.1738,2.6743;6.5937,-1.6376,3.5959;6.0178,-0.9052,1.6608;4.9773,-1.1831,1.8028)|",4.492599671754999
"[H]C([H])=C([H])C([H])([H])N(C(=O)C([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1Cl |(-0.0541,0.451,-1.5824;0.8271,-0.1777,-1.4924;1.1729,-0.6618,-2.4037;1.4505,-0.3465,-0.3274;1.0864,0.1623,0.5654;2.6492,-1.2429,-0.1242;3.0469,-1.5762,-1.089;2.3502,-2.145,0.4138;3.7386,-0.61,0.6303;3.9548,-0.8423,1.9948;4.8671,-0.2947,2.5954;3.0165,-1.8039,2.7083;3.1354,-2.8311,2.3438;1.9626,-1.5277,2.5961;3.2808,-1.7789,3.7659;4.6049,0.2792,-0.0164;5.3824,0.63,0.6503;4.5334,0.6611,-1.3058;3.7509,0.2781,-1.9483;5.4798,1.5887,-1.9347;6.2554,2.4982,-1.1848;6.1239,2.528,-0.1076;7.1571,3.3707,-1.7824;7.7335,4.053,-1.164;7.3083,3.3803,-3.1708;8.0046,4.0633,-3.6486;6.5488,2.509,-3.9482;6.6448,2.4985,-5.0287;5.6551,1.6343,-3.333;4.7423,0.5451,-4.3871)|",4.612329765975001
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"[H]C([H])=C(C([H])([H])[H])[C@@]([H])(C([H])([H])C(=O)OC([H])([H])[H])C([H])([H])C([H])(OC([H])([H])[H])OC([H])([H])[H] |(2.137,-1.9918,-2.7779;2.6556,-1.7337,-1.858;3.6089,-2.2276,-1.6939;2.1375,-0.8553,-0.9931;0.8138,-0.178,-1.2572;0.344,-0.5459,-2.1744;0.9452,0.9087,-1.3499;0.1097,-0.3337,-0.4284;2.7967,-0.4903,0.3365;2.4681,0.5225,0.5992;2.258,-1.4487,1.4434;2.701,-2.4406,1.3185;1.1709,-1.537,1.351;2.5904,-0.9565,2.8358;3.5338,-1.3173,3.5081;1.7107,-0.0092,3.2389;1.9793,0.5724,4.5249;1.1856,1.3021,4.6883;2.9575,1.0608,4.5285;1.9641,-0.1943,5.3041;4.3368,-0.4788,0.325;4.7318,-1.4484,0.0006;4.6926,-0.323,1.3484;4.9336,0.6044,-0.5744;4.5473,0.5143,-1.5985;4.5557,1.8518,-0.0282;4.8226,2.9545,-0.8829;5.8933,3.049,-1.0993;4.4752,3.8497,-0.3609;4.2795,2.8596,-1.8356;6.334,0.4946,-0.7445;7.1127,0.5425,0.4467;6.8191,1.3825,1.0873;8.1494,0.6782,0.1278;7.0415,-0.3894,1.0233)|",6.759308046419999
"[H]O[C@@]([H])([C@@]([H])(O[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])=C([H])[H])[C@]([H])(O[H])C([H])([H])[H] |(11.476,6.0421,-0.3479;11.1205,5.6673,-1.173;9.712,5.5608,-0.9581;9.2539,6.5628,-0.994;9.1725,4.7546,-2.1506;9.3489,5.3892,-3.0369;9.8884,3.5326,-2.3028;10.825,3.7741,-2.1937;7.6801,4.4375,-2.0749;7.4949,3.768,-1.2243;7.1322,5.3693,-1.8725;7.1408,3.7861,-3.3557;7.3109,4.4639,-4.2056;7.7263,2.8828,-3.5582;5.6456,3.4513,-3.2672;5.4764,2.7618,-2.4264;5.0904,4.3683,-3.0219;5.0482,2.8417,-4.5462;3.9546,2.8034,-4.4401;5.2472,3.5136,-5.3946;5.56,1.4327,-4.8806;6.6478,1.4516,-5.0231;5.371,0.7692,-4.0248;4.8989,0.8465,-6.132;3.8046,0.8485,-6.0013;5.0785,1.5175,-6.9874;5.3276,-0.5457,-6.5216;4.8379,-0.9402,-7.4138;6.2206,-1.3152,-5.897;6.4594,-2.3105,-6.2622;6.7491,-0.9914,-5.0046;9.4438,5.0157,0.4564;8.3581,5.0319,0.6345;10.0796,5.9948,1.3034;10.1288,5.6279,2.1994;9.9933,3.6211,0.7462;9.5361,2.8694,0.0996;9.7984,3.3482,1.7923;11.0745,3.5984,0.579)|",7.58653415194
"[H]C1([H])C([H])([H])[C@@]2([H])C([H])([H])[C@]1([H])[C@]([H])(C([H])([H])Cl)[C@]2([H])C([H])([H])Cl |(-0.9631,-1.1488,-1.6151;-1.1898,-0.7714,-0.612;-2.1552,-1.1972,-0.3177;-1.2146,0.7906,-0.5403;-2.1928,1.1568,-0.2102;-1.001,1.2649,-1.5044;-0.1323,1.1104,0.5167;-0.2129,2.1142,0.9403;-0.2973,-0.0711,1.4952;0.4529,-0.0963,2.2948;-1.2888,-0.1086,1.9604;-0.0961,-1.152,0.4125;-0.1441,-2.1924,0.7424;1.2874,-0.7672,-0.1809;1.3506,-1.0731,-1.2309;2.4623,-1.4272,0.5415;2.4211,-1.2974,1.6248;3.4264,-1.0699,0.1776;2.4932,-3.2298,0.2624;1.2618,0.826,-0.1081;1.3133,1.2283,-1.1257;2.4178,1.4534,0.6717;3.3906,1.1719,0.2668;2.3928,1.2123,1.7363;2.3797,3.275,0.5775)|",7.92939760357
"[H]C1=C(C([H])([H])[H])[C@@]([H])(OC(=O)C([H])([H])Cl)[C@@]2([H])OC2([H])C1=O |(-0.4886,1.2461,-2.6486;0.5926,1.2381,-2.5293;1.1916,0.163,-1.9537;0.4246,-1.0514,-1.5109;-0.6378,-0.9739,-1.7561;0.5212,-1.2039,-0.4275;0.829,-1.95,-1.9943;2.7208,0.11,-1.6913;2.9413,-0.2891,-0.6968;3.2958,-0.7743,-2.6746;4.5247,-1.2612,-2.3764;5.14,-1.0042,-1.3709;5.0422,-2.161,-3.4854;6.0436,-2.4957,-3.2217;5.0526,-1.6263,-4.4371;4.003,-3.6246,-3.7052;3.1236,1.5609,-1.8782;2.7922,2.1727,-1.0374;4.0387,2.2799,-2.7099;2.7189,1.9952,-3.2202;2.7567,1.212,-3.9756;1.3108,2.499,-3.0045;0.7901,3.5866,-3.0933)|",4.982404602655
"[H]O[C@@]1([H])C([H])=C([C@@]([H])(OC([H])([H])OC([H])([H])[H])C([H])([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C1([H])[H] |(6.0197,-0.8795,-4.0111;5.2779,-0.9015,-4.6378;4.1799,-0.2459,-4.0056;3.3419,-0.4186,-4.6919;3.8561,-0.7674,-2.6251;3.7806,-1.828,-2.4106;3.6394,0.2097,-1.7381;3.2434,0.0627,-0.2951;2.2854,0.5822,-0.154;3.0606,-1.3347,-0.0177;2.0951,-1.6168,0.9659;2.2054,-2.6923,1.1648;2.2725,-1.0441,1.8853;0.7827,-1.3018,0.5897;0.2952,-2.0827,-0.4949;-0.7486,-1.7963,-0.6422;0.3448,-3.1575,-0.2613;0.8592,-1.8938,-1.4153;4.2907,0.6529,0.6613;4.4264,1.7257,0.4874;5.2525,0.1486,0.523;3.9786,0.5229,1.7031;3.7579,1.5763,-2.3897;4.349,2.284,-1.7959;2.4214,2.1105,-2.5158;2.3797,3.5053,-2.6945;3.0903,3.8329,-3.4641;1.3508,3.7239,-3.0126;2.7187,4.2383,-1.5461;1.7467,4.1578,-0.5123;2.1012,4.7917,0.304;1.6205,3.131,-0.148;0.77,4.5297,-0.8579;4.4047,1.2683,-3.7579;4.0071,1.9008,-4.5573;5.4857,1.4481,-3.7068)|",6.748423492400001
"[H]OC([H])([H])C([H])([H])[C@@]([H])(O[H])C(=C([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C([H])([H])[H] |(0.7775,3.2577,3.1107;1.3219,4.0648,3.0834;2.6496,3.6127,2.8877;3.2633,4.5103,2.7538;3.0328,3.0911,3.7837;2.8179,2.6967,1.6664;3.8906,2.5324,1.5042;2.4365,3.2096,0.7719;2.1059,1.3374,1.8193;2.492,0.8501,2.725;0.7131,1.5352,2.1169;0.3135,1.9402,1.3276;2.301,0.398,0.6305;1.2656,-0.2052,0.0407;1.4181,-0.8747,-0.797;0.2519,-0.0696,0.4018;3.7335,0.1469,0.1804;4.194,1.1049,-0.0928;3.7203,-0.6891,-0.9853;4.7456,-0.4149,-1.9098;5.7311,-0.3918,-1.4271;4.6988,-1.241,-2.633;4.622,0.8267,-2.5471;3.4788,0.9245,-3.3882;3.5362,1.897,-3.8827;2.5476,0.8596,-2.8137;3.4819,0.1316,-4.1517;4.5734,-0.5298,1.275;4.6548,0.0966,2.17;5.5892,-0.7221,0.913;4.1188,-1.4849,1.5562)|",7.19469020722
"[H]OC([H])([H])[C@@]1([H])O[C@]1([H])C(=C([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C([H])([H])[H] |(2.5922,3.8903,-3.7865;2.9324,3.1391,-4.2997;2.4753,1.9653,-3.6443;1.3778,1.88,-3.6846;2.9018,1.1209,-4.1968;2.9274,1.9322,-2.2052;4.006,1.8986,-2.0435;2.2465,2.8672,-1.3461;2.0541,1.4668,-1.1048;1.0602,1.107,-1.3871;2.5953,0.8955,0.1661;3.2642,1.6494,1.0417;3.66,1.2226,1.957;3.419,2.7074,0.8566;2.3387,-0.5934,0.3626;1.3052,-0.8204,0.0636;2.5171,-0.9936,1.7261;1.406,-0.7844,2.5366;1.7401,-0.9617,3.5694;1.0267,0.2501,2.4494;0.3868,-1.6962,2.1778;-0.8156,-1.4692,2.8848;-1.5419,-2.2018,2.5251;-1.2108,-0.4558,2.7074;-0.6847,-1.6032,3.9706;3.2872,-1.4578,-0.4707;3.2139,-1.2056,-1.534;3.039,-2.5152,-0.341;4.3201,-1.295,-0.1461)|",6.7783560159550005
"[H]OC([H])([H])/C([H])=C(\[H])C(=C([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C([H])([H])[H] |(-0.3818,2.901,-5.3195;-0.9789,3.5732,-4.9547;-0.5031,3.8813,-3.6436;0.4753,4.387,-3.6758;-1.2287,4.6015,-3.2459;-0.4303,2.667,-2.7642;-1.3485,2.0834,-2.6931;0.6755,2.2756,-2.1133;1.5953,2.8456,-2.2492;0.7918,1.1059,-1.2222;-0.2239,0.6276,-0.488;-1.2078,1.088,-0.4968;-0.1007,-0.2441,0.1502;2.1732,0.4583,-1.15;2.1502,-0.3199,-0.3693;3.1796,1.4189,-0.8219;3.1172,1.9093,0.5094;2.2909,2.6202,0.6314;2.9718,1.0553,1.2005;4.2781,2.6064,0.8029;5.4433,1.7934,0.8465;6.2602,2.4373,1.1802;5.6822,1.3781,-0.1387;5.3211,0.9659,1.5631;2.5961,-0.1733,-2.475;1.8874,-0.9547,-2.7678;3.593,-0.6148,-2.3776;2.629,0.5794,-3.2691)|",5.518468888139999
"[H]OC([H])([H])C(=C([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C([H])([H])[H] |(0.958,-0.1915,-3.5042;1.1103,0.7202,-3.2082;1.8774,0.6474,-2.019;2.8809,0.2307,-2.2192;2.0359,1.6894,-1.7136;1.2316,-0.1393,-0.8926;-0.0337,-0.5591,-0.9584;-0.6561,-0.3233,-1.8159;-0.478,-1.129,-0.1493;2.1207,-0.3928,0.3183;3.1324,-0.6499,-0.0292;1.6356,-1.4641,1.1383;1.9728,-2.7514,0.6729;1.3599,-3.4393,1.272;1.7394,-2.8623,-0.3922;3.3368,-3.0565,0.7921;3.7771,-3.1705,2.1388;4.8357,-3.4384,2.1028;3.224,-3.9601,2.6706;3.6565,-2.228,2.6861;2.2115,0.8364,1.2269;2.578,1.7095,0.6769;2.8948,0.6392,2.0588;1.2226,1.0726,1.6323)|",7.23278614629
"[H]O[C@]1([H])[C@]([H])(O[H])[C@@]([H])(C([H])([H])[H])O[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])[C@]1([H])O[H] |(3.9371,2.1184,-2.7589;3.074,1.8856,-3.1465;2.5901,0.828,-2.3469;1.5066,0.779,-2.5129;2.8771,1.0956,-0.8556;2.3394,1.9985,-0.5453;4.2604,1.3987,-0.6873;4.7456,0.6469,-1.0822;2.4343,-0.0841,0.0305;2.9276,0.0425,0.9995;0.9199,-0.1584,0.2556;0.5759,0.732,0.7933;0.6791,-1.0394,0.8585;0.3495,-0.2183,-0.6776;2.9667,-1.327,-0.4607;2.6525,-1.6419,-1.8119;1.5618,-1.7082,-1.9555;3.2659,-2.9892,-2.1429;2.7271,-3.7697,-3.175;1.8324,-3.4316,-3.6941;3.3196,-4.9793,-3.5348;2.8868,-5.5755,-4.3337;4.4621,-5.4264,-2.8661;4.9229,-6.3703,-3.1441;5.0031,-4.6574,-1.8357;5.886,-5.0029,-1.3041;4.4114,-3.4438,-1.4758;4.8106,-2.8568,-0.6553;3.1793,-0.5332,-2.7596;2.8773,-0.7602,-3.7925;4.6044,-0.4284,-2.6823;4.9732,-1.3224,-2.7876)|",6.353858409175
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"[H]O[C@]1([H])[C@]([H])(O[H])[C@@]([H])(C([H])([H])[H])O[C@]([H])(C([H])([H])[C@@]([H])(N([H])[H])C([H])([H])[H])[C@]1([H])O[H] |(2.7895,4.4778,3.4177;1.9423,4.1992,3.8091;1.9844,2.7881,3.8017;0.948,2.4464,3.9079;2.7922,2.2262,4.9865;2.3155,2.5326,5.9291;4.1022,2.8129,4.8984;4.6596,2.4221,5.5893;2.8876,0.6901,4.9111;3.6591,0.368,5.6256;1.5872,-0.0324,5.2855;1.3078,0.1988,6.3197;1.7336,-1.1135,5.2018;0.7443,0.2459,4.6457;3.4121,0.2864,3.646;2.6704,0.7237,2.4946;1.6494,0.3136,2.542;3.4077,0.1387,1.2874;4.4291,0.5353,1.3065;3.4798,-0.9476,1.4368;2.7953,0.4189,-0.1032;2.7297,1.5058,-0.241;3.6415,-0.0736,-1.2035;3.7905,-1.0774,-1.0866;4.566,0.3519,-1.1348;1.3937,-0.1767,-0.2721;0.6767,0.2503,0.4378;1.4111,-1.2637,-0.1133;1.0299,0.0109,-1.2865;2.5565,2.2599,2.4699;1.879,2.5571,1.662;3.7957,2.9063,2.186;4.3678,2.7306,2.9588)|",7.597418705959999
"[H]O[C@]1([H])[C@]([H])(O[H])[C@@]([H])(C([H])([H])[H])O[C@]([H])(C([H])([H])/C(=N\OC([H])([H])[H])C([H])([H])[H])[C@]1([H])O[H] |(1.9691,6.5793,2.1325;1.9418,6.3385,1.1857;1.4523,4.9988,1.1777;0.7478,4.9009,0.3429;0.7408,4.7048,2.5143;-0.1808,5.2949,2.5767;1.5661,5.1805,3.5827;2.4028,4.6786,3.5087;0.4143,3.213,2.6836;0.168,3.0603,3.7395;-0.7645,2.7285,1.8321;-1.6858,3.2312,2.1474;-0.8967,1.6509,1.9694;-0.6328,2.9194,0.7623;1.6029,2.4348,2.4918;2.2177,2.5775,1.2071;1.5122,2.2941,0.4143;3.4029,1.6031,1.1666;3.9308,1.7495,0.2186;4.0842,1.8531,1.9841;2.9616,0.1604,1.2727;2.4905,-0.5278,0.2964;2.4259,0.2303,-0.8959;1.9862,-0.6207,-1.9461;1.9418,0.0106,-2.8378;0.9932,-1.0341,-1.733;2.6907,-1.4447,-2.1106;3.0521,-0.5359,2.6013;4.0913,-0.5602,2.9549;2.6776,-1.5598,2.5276;2.4675,0.0166,3.3459;2.6364,4.0301,0.9606;2.9941,4.1235,-0.0765;3.691,4.37,1.8662;3.8439,5.3197,1.7112)|",6.669510475755
"[H]C1=C([H])C2=C(C([H])=C1[H])N1C([H])([H])C([H])([H])N(C([H])([H])[H])C([H])([H])[C@]1([H])C([H])([H])C([H])([H])O2 |(2.361,-3.3782,6.3707;2.7823,-2.8602,5.5143;3.4353,-3.5676,4.5095;3.5502,-4.6463,4.5588;3.9962,-2.9266,3.4039;3.9113,-1.5166,3.2462;3.2162,-0.8335,4.271;3.0897,0.2388,4.2129;2.675,-1.4794,5.3808;2.161,-0.8887,6.1344;4.5779,-0.812,2.2051;4.5035,0.6565,2.2394;4.6415,1.0164,3.2608;5.3483,1.0355,1.6528;3.1989,1.2013,1.6328;3.2509,2.2967,1.581;2.3435,0.9464,2.2871;2.9988,0.6857,0.2763;1.7553,1.1703,-0.3004;1.7598,2.2661,-0.3181;0.8553,0.8417,0.2564;1.664,0.8139,-1.3327;3.0971,-0.7764,0.2257;2.2851,-1.2715,0.7953;2.9975,-1.0831,-0.8231;4.4705,-1.2018,0.7765;5.1894,-0.5488,0.267;4.9072,-2.6241,0.4183;4.7434,-2.7584,-0.6597;5.9855,-2.7127,0.5949;4.238,-3.7508,1.1815;4.5454,-4.7186,0.7717;3.1409,-3.6958,1.1371;4.6767,-3.7536,2.5377)|",5.396017655414999
"[H]C1=C2OC([H])([H])C([H])([H])[C@]3([H])N(C2=C([H])C(N([H])[H])=C1[H])C([H])([H])C([H])([H])N([H])C3([H])[H] |(-1.2625,-2.711,0.4918;-1.3929,-1.6554,0.2728;-0.2377,-0.8782,0.2158;0.9347,-1.5415,0.5211;1.9501,-1.5159,-0.4769;2.5746,-2.3953,-0.2862;1.5048,-1.6238,-1.4766;2.7754,-0.2516,-0.3403;3.6795,-0.3323,-0.9607;3.1066,-0.1943,0.7034;2.0839,1.0638,-0.7098;2.7738,1.8426,-0.3538;0.8106,1.3769,-0.0066;-0.3199,0.5157,-0.0484;-1.6201,1.031,-0.2448;-1.7584,2.0773,-0.4834;-2.781,0.2503,-0.158;-4.0361,0.833,-0.408;-4.8082,0.3294,0.0129;-4.0882,1.8171,-0.1711;-2.6633,-1.1181,0.1053;-3.5457,-1.7496,0.1679;0.5618,2.8176,-0.1872;-0.3182,3.1194,0.3866;1.417,3.3474,0.254;0.4422,3.2015,-1.6792;0.3641,4.2901,-1.7889;-0.4719,2.7663,-2.099;1.5686,2.7325,-2.4995;2.3862,3.3052,-2.2863;1.9126,1.3294,-2.2217;1.1173,0.6914,-2.6286;2.836,1.093,-2.7654)|",5.13750949744
"[H]C1=C2C(=C([H])\C(C([H])([H])[H])=C/1[H])\OC([H])([H])C([H])([H])[C@@]1([H])N\2C([H])([H])C([H])([H])N([H])C1([H])[H] |(4.9,2.1764,0.0011;4.3399,1.25,-0.0679;4.8941,0.0755,0.4572;4.135,-1.1129,0.3288;2.8876,-1.0847,-0.2962;2.3473,-2.0251,-0.3677;2.3319,0.0996,-0.7934;0.9665,0.0955,-1.4407;0.726,1.0746,-1.8673;0.179,-0.1543,-0.718;0.9069,-0.6453,-2.2475;3.0809,1.2711,-0.6706;2.6875,2.2099,-1.0527;4.5058,-2.3317,0.8298;5.8817,-2.7316,0.8997;6.2278,-2.6211,1.9337;5.8694,-3.8009,0.6605;6.8003,-1.993,-0.07;6.2897,-1.9001,-1.0349;7.6902,-2.6086,-0.2548;7.2506,-0.6137,0.4431;7.5791,0.0009,-0.415;6.1435,0.0582,1.1522;6.5525,1.337,1.7378;6.8588,2.0685,0.9689;5.6995,1.7521,2.2839;7.731,1.1309,2.6845;7.4046,0.4975,3.5308;8.0465,2.1009,3.0858;8.8361,0.547,1.9269;9.6544,0.4446,2.5235;8.4524,-0.7557,1.3901;9.2959,-1.1652,0.8215;8.1931,-1.4839,2.1827)|",5.423229040464999
"[H]C1=C([H])C(=O)[C@@]([H])(OC([H])([H])[H])C(Br)=N1 |(1.7189,2.8984,4.1397;1.994,2.3035,3.2734;1.6898,0.9892,3.2009;1.1232,0.4905,3.9805;2.0074,0.2333,1.9874;1.5945,-0.8891,1.7529;2.9811,0.9373,1.0018;3.9888,0.6045,1.3321;2.7534,0.6084,-0.332;3.3762,-0.5967,-0.7743;3.1602,-0.6718,-1.8419;2.9708,-1.4672,-0.2515;4.4662,-0.5466,-0.6314;3.0102,2.4417,1.217;3.8155,3.4762,-0.1887;2.626,3.0483,2.2635)|",4.111640281055001
"[H]OC([H])([H])C([H])([H])C([H])([H])[C@]1([H])C(=O)N=C([H])C(F)=C1[H] |(2.1294,-2.3222,-1.0322;1.1836,-2.1736,-1.1887;0.977,-0.7633,-1.2746;1.5933,-0.3205,-2.072;-0.0677,-0.6336,-1.5683;1.2421,-0.0329,0.0454;2.3226,-0.0291,0.2535;0.9463,1.0153,-0.0776;0.5435,-0.66,1.2612;0.7956,-0.0947,2.1666;0.9222,-1.6785,1.4025;-1.0136,-0.7723,1.1556;-1.2301,-1.3556,0.2531;-1.6582,0.6013,0.9404;-1.6609,1.1233,-0.1576;-2.2255,1.299,2.0439;-2.5374,0.6015,3.082;-3.0051,1.1124,3.9275;-2.287,-0.8261,3.2395;-2.7838,-1.3849,4.3611;-1.5529,-1.5022,2.351;-1.3041,-2.5493,2.5005)|",4.410965516605
"[H]C1=N[C@]2([H])C(=O)NC(C([H])([H])[H])=NC2=C([H])C1Br |(7.0057,-1.8524,2.1645;6.1495,-1.6742,1.5139;5.959,-0.4868,1.0793;4.8451,-0.2434,0.1958;5.2894,-0.0409,-0.799;4.0928,1.0845,0.4745;4.4707,1.9093,1.2689;2.9719,1.273,-0.3515;2.384,0.2304,-0.8572;1.2098,0.4196,-1.7685;0.3143,-0.0188,-1.3122;1.3777,-0.1205,-2.7082;1.0446,1.479,-1.9675;2.6766,-1.1156,-0.5737;3.8225,-1.3391,-0.0041;4.1523,-2.6644,0.4697;3.4608,-3.4733,0.2615;5.2804,-2.8183,1.1993;5.7917,-4.5097,1.9037)|",3.44496134733
"[H]C1=C([H])C(=O)[C@]2([H])OC([H])([H])C([H])([H])OC2=N1 |(2.7739,-1.943,-0.063;2.0373,-1.1425,-0.0613;2.4255,0.1376,0.1806;3.4494,0.3801,0.4446;1.4172,1.192,0.2391;1.5706,2.3102,0.6937;0.0964,0.7799,-0.47;0.3673,0.8846,-1.5383;-0.973,1.6667,-0.2302;-2.0286,1.0807,0.5133;-1.7036,0.8241,1.5338;-2.8189,1.8331,0.5777;-2.518,-0.1556,-0.2125;-2.8508,0.1035,-1.2242;-3.3322,-0.6578,0.3167;-1.4611,-1.1344,-0.3084;-0.1923,-0.7006,-0.3109;0.7321,-1.5926,-0.2248)|",4.427292347635
"[H]C1=C([H])/C(=C(/[H])NO)[C@]2([H])OC([H])([H])C([H])([H])O\C2=N\1 |(2.6808,-2.0866,-0.2667;1.9719,-1.2697,-0.1638;2.3786,-0.062,0.3134;3.3913,0.0892,0.6667;1.4237,1.0084,0.435;1.631,2.1818,1.0997;0.8765,2.9616,1.1716;2.8699,2.4014,1.7145;2.9432,3.4826,2.3074;0.143,0.7932,-0.3588;0.41,1.0515,-1.4012;-0.9085,1.6737,0.0069;-2.0353,1.0142,0.5676;-1.7869,0.5609,1.5397;-2.7969,1.7826,0.7227;-2.5232,-0.044,-0.399;-2.7784,0.4091,-1.3642;-3.3914,-0.587,-0.0162;-1.5038,-1.042,-0.6074;-0.2196,-0.6871,-0.4427;0.657,-1.624,-0.4493)|",2.78644582912
"[H]C1=NC([H])=C([H])C([C@@]2([H])N([H])N3C(=O)C([H])=C([H])N([H])[C@@]3([H])C2([H])[H])=C1[H] |(-2.7825,1.4824,0.3522;-1.7857,1.0781,0.1832;-1.7142,-0.2472,0.0016;-0.4949,-0.7539,-0.2175;-0.4442,-1.8309,-0.3719;0.6678,0.0142,-0.2619;1.6258,-0.4624,-0.4562;0.5881,1.3971,-0.0595;1.8577,2.2286,-0.0671;2.6192,1.6595,-0.6128;1.7684,3.572,-0.7484;0.8364,3.7663,-1.1182;1.984,4.58,0.25;0.8466,5.2334,0.7585;-0.1905,5.2644,0.0932;1.0335,5.8792,2.0505;0.2543,6.5371,2.4106;2.1473,5.6215,2.7765;2.3226,6.0757,3.7476;3.1483,4.8075,2.3264;3.8798,4.5349,2.9652;2.9674,3.9826,1.1496;3.9228,3.9206,0.6129;2.3977,2.5554,1.3511;3.1421,1.8292,1.6904;1.5845,2.5943,2.0836;-0.6854,1.9361,0.165;-0.8314,3.0039,0.304)|",4.879001339465
"[H]C1=NC([H])=C([H])C([H])=C1[C@@]1([H])N([H])N2C(=O)C([H])=C([H])N([H])[C@@]2([H])C1([H])[H] |(-1.5428,0.4106,-0.522;-0.5801,-0.0356,-0.2691;0.457,0.8068,-0.2297;1.6543,0.2848,0.0629;2.4888,0.9838,0.0885;1.8566,-1.0724,0.3165;2.8523,-1.4477,0.5343;0.7636,-1.9357,0.2783;0.8991,-3.0008,0.4505;-0.5027,-1.4124,-0.0159;-1.7694,-2.2443,-0.0534;-2.5205,-1.673,-0.6111;-1.6588,-3.5819,-0.7492;-0.7146,-3.7683,-1.0908;-1.8995,-4.6008,0.2326;-0.7739,-5.2454,0.7766;0.2886,-5.2573,0.1509;-0.9999,-5.9069,2.0544;-0.2282,-6.5626,2.4343;-2.1412,-5.6671,2.7432;-2.3473,-6.1342,3.7022;-3.1313,-4.8552,2.267;-3.888,-4.5958,2.8814;-2.9122,-4.0141,1.1078;-3.8492,-3.9459,0.5408;-2.3482,-2.591,1.3445;-3.1013,-1.8686,1.6729;-1.5562,-2.6401,2.0996)|",4.821857430860001
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"[H]OC([H])([H])[C@@]([H])(C1=C([H])N([H])C2=NC([H])=C([H])C2=C1[H])N([H])C([H])([H])[H] |(2.8352,3.3805,-0.2073;2.8718,3.5164,-1.178;2.6679,2.2113,-1.6931;1.5911,1.9853,-1.7633;3.0783,2.1756,-2.7072;3.3524,1.1541,-0.7961;2.9687,0.1619,-1.0921;4.8657,1.1096,-0.9272;5.6037,2.2739,-1.1034;5.1332,3.2448,-1.1918;6.9577,2.2288,-1.1888;7.4886,3.0824,-1.3272;7.657,1.0635,-1.0982;8.9698,0.9248,-1.1683;9.1381,-0.4411,-1.0237;10.1435,-0.8487,-1.0422;7.9604,-1.1591,-0.8667;7.8529,-2.2279,-0.7394;6.9254,-0.1835,-0.9093;5.5434,-0.1306,-0.828;4.9584,-1.0394,-0.6952;2.975,1.4636,0.6035;3.7061,1.1303,1.2262;1.6823,0.9172,1.0202;1.5188,1.1476,2.0774;1.5983,-0.1747,0.8825;0.8749,1.389,0.4508)|",4.078986618995
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"[H]OC(=O)[C@@](C1=C(\[H])N([H])C2=NC([H])=C([H])\C2=C\1[H])(N([H])[H])C([H])([H])[H] |(0.8002,-0.6406,-1.347;0.1904,-0.3076,-0.6413;0.9986,0.1898,0.3063;0.5957,0.736,1.305;2.5212,-0.0327,0.0188;2.9467,-1.2709,0.8158;3.6584,-1.1417,1.9991;3.959,-0.1899,2.4129;4.0161,-2.234,2.7229;4.5299,-2.1256,3.5912;3.702,-3.4997,2.3337;4.0103,-4.6179,2.9635;3.4488,-5.5879,2.1491;3.5499,-6.63,2.4333;2.7977,-5.099,1.0257;2.2893,-5.6684,0.2596;2.9411,-3.6854,1.1027;2.5708,-2.5684,0.3755;1.993,-2.6716,-0.5383;2.6496,-0.3418,-1.4285;3.4938,-0.8817,-1.6075;2.7243,0.5227,-1.9639;3.2756,1.2454,0.4139;3.0247,1.5531,1.4314;2.9791,2.0674,-0.2477;4.3582,1.1044,0.3261)|",4.03816954142
"[H]C1=NC([H])=C([H])C(C([H])([H])[C@@]([H])(C2=C([H])N([H])C3=NC([H])=C([H])C3=C2[H])N([H])[H])=C1[H] |(0.5339,-1.5653,1.5355;0.4253,-0.8845,0.6925;-0.2357,-1.364,-0.3684;-0.3717,-0.5366,-1.413;-0.9044,-0.9382,-2.274;0.1271,0.765,-1.4437;-0.0108,1.3789,-2.3308;0.8149,1.2652,-0.3307;1.3476,2.6788,-0.3175;1.7564,2.9362,-1.3011;2.1696,2.7604,0.4057;0.2744,3.7476,0.0156;-0.5581,3.5977,-0.6841;-0.2806,3.5986,1.4307;-1.5809,3.1578,1.6133;-2.2336,2.9018,0.7871;-2.1075,3.0104,2.8586;-3.0619,2.6881,2.9802;-1.3906,3.2866,3.982;-1.8102,3.1817,5.2303;-0.702,3.5821,5.9572;-0.764,3.5926,7.0404;0.3999,3.937,5.1919;1.3645,4.2765,5.5443;-0.0139,3.754,3.8425;0.5082,3.9008,2.5689;1.5285,4.2551,2.4304;0.8409,5.0687,-0.2948;1.6703,5.2345,0.2752;0.1743,5.7945,-0.0335;0.9613,0.4022,0.7605;1.4856,0.7251,1.6558)|",4.03272726441
"[H]C1=C([H])/C2=C([H])/C([C@@]([H])(N([H])C([H])([H])[H])C([H])([H])[H])=C(/[H])N([H])C2=N1 |(6.422,6.14,1.4113;5.7787,5.2983,1.1762;6.1334,3.9572,1.2057;7.0952,3.536,1.4654;4.9557,3.2552,0.8224;4.5456,1.9473,0.6282;5.2406,1.1239,0.7843;3.2165,1.6634,0.2252;2.7434,0.2241,0.0002;1.6936,0.2739,-0.3229;3.4544,-0.5063,-1.0552;4.4285,-0.6304,-0.7828;3.3881,0.102,-2.3804;3.9544,-0.516,-3.0846;3.773,1.1344,-2.4381;2.3442,0.1155,-2.7171;2.7974,-0.6007,1.2939;2.213,-0.124,2.0875;3.828,-0.6995,1.6573;2.4054,-1.6056,1.1113;2.3409,2.7161,0.0205;1.3131,2.5704,-0.2914;2.7288,4.0071,0.2098;2.0785,4.7702,0.0548;3.9922,4.3292,0.5997;4.4676,5.545,0.8054)|",4.03816954142
"[H]OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(C1=C(\[H])N([H])C2=NC([H])=C([H])\C2=C\1[H])N([H])[H] |(0.4778,-0.11,-4.9792;0.5243,-0.6745,-4.1917;1.181,0.0694,-3.1719;0.5853,0.9493,-2.8722;1.2171,-0.6024,-2.3084;2.5894,0.5097,-3.5809;3.1677,-0.3818,-3.8539;2.5088,1.1105,-4.4998;3.332,1.3482,-2.5263;2.7577,2.2657,-2.3357;4.2897,1.6743,-2.9508;3.5838,0.657,-1.1754;2.6379,0.3771,-0.6981;4.0529,1.3856,-0.4967;4.4565,-0.6123,-1.225;3.9597,-1.3381,-1.8805;5.8492,-0.3749,-1.8157;6.7085,0.5569,-1.2485;6.4384,1.172,-0.3981;7.9615,0.7497,-1.7393;8.5803,1.4345,-1.3176;8.4373,0.0504,-2.8054;9.6356,0.168,-3.3528;9.5894,-0.7581,-4.3785;10.4614,-0.8794,-5.0129;8.3902,-1.4519,-4.4844;8.1333,-2.2145,-5.207;7.5742,-0.9392,-3.4391;6.2992,-1.1277,-2.9268;5.6251,-1.859,-3.3697;4.4529,-1.2206,0.1182;4.8929,-0.5854,0.7845;5.0299,-2.0611,0.1127)|",4.0272849874
"[H]C1=C([H])C2=C([H])C([C@@]([H])(N([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])=C([H])N([H])C2=N1 |(6.712,9.4431,2.9817;6.3266,8.5544,2.4926;7.0572,7.4356,2.1212;8.1166,7.2653,2.2575;6.1092,6.5548,1.5259;6.0887,5.3016,0.9386;6.9932,4.7111,0.8217;4.8695,4.7671,0.4495;4.824,3.3823,-0.1776;3.8191,3.255,-0.6236;5.9039,3.2767,-1.1721;5.8639,2.3658,-1.6276;5.7747,3.9772,-1.9008;5.0097,2.2517,0.856;5.0901,1.3007,0.3075;5.9736,2.3965,1.3606;3.8827,2.138,1.888;3.7987,3.0797,2.4474;2.9213,2.0039,1.3685;4.0854,0.9811,2.8752;4.1646,0.0366,2.3181;5.0475,1.1108,3.3903;2.9625,0.8703,3.9115;3.135,0.0354,4.6;1.9908,0.708,3.4285;2.8849,1.7858,4.5106;3.7107,5.5174,0.5526;2.7513,5.1685,0.1888;3.72,6.7509,1.1273;2.8679,7.2971,1.1926;4.8614,7.3056,1.6222;4.9755,8.4923,2.1917)|",4.068102064975
"[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(C1=C([H])N([H])C2=NC([H])=C([H])C2=C1[H])N([H])[H] |(3.1433,4.9481,-1.5139;2.4571,5.264,-2.1254;1.26,4.7018,-1.8158;0.2857,4.984,-2.4726;1.2713,3.7563,-0.618;2.0686,3.0129,-0.7665;1.5667,4.348,0.261;-0.0755,3.0683,-0.3699;-0.0467,2.5906,0.6176;-0.8533,3.8368,-0.3236;-0.4173,2.0175,-1.4351;0.2825,1.1765,-1.3636;-0.2778,2.4455,-2.4359;-1.8307,1.423,-1.3301;-1.9359,0.9847,-0.3278;-2.9749,2.4252,-1.5048;-2.8207,3.608,-2.2162;-1.8728,3.9565,-2.6066;-3.8771,4.44,-2.4221;-3.7455,5.3093,-2.9289;-5.1248,4.1669,-1.9532;-6.2058,4.9145,-2.1008;-7.1711,4.1862,-1.4279;-8.184,4.5737,-1.3877;-6.7186,3.0027,-0.8608;-7.293,2.2821,-0.2945;-5.335,2.9422,-1.19;-4.2528,2.1038,-0.9815;-4.3672,1.184,-0.4083;-1.9046,0.2834,-2.2716;-1.8757,0.6494,-3.2248;-2.8185,-0.1619,-2.1865)|",4.057217510955
"[H]C1=C([H])C2=C([H])C([C@@]3([H])N([H])C([H])([H])C([H])([H])OC3([H])[H])=C([H])N([H])C2=N1 |(-3.4606,-0.3932,0.0198;-2.3922,-0.2035,0.011;-1.7738,1.0301,0.1527;-2.2493,1.9913,0.2937;-0.3787,0.7563,0.0754;0.8163,1.4531,0.125;0.843,2.5327,0.2432;2.0467,0.7591,-0.0046;3.3685,1.4918,0.0859;4.1804,0.814,-0.2425;3.3337,2.7231,-0.7153;3.1084,2.4937,-1.6814;4.6023,3.4537,-0.6453;5.4545,2.8698,-1.0404;4.5161,4.3744,-1.2342;4.8858,3.793,0.8151;4.1085,4.4778,1.1916;5.8628,4.2728,0.9259;4.9251,2.612,1.6076;3.6987,1.9001,1.5349;3.8147,1.0137,2.1652;2.8738,2.5132,1.9327;2.0371,-0.614,-0.1893;2.9428,-1.1979,-0.3049;0.8682,-1.3063,-0.2395;0.8682,-2.3106,-0.3824;-0.3405,-0.6906,-0.1138;-1.526,-1.2717,-0.1555)|",4.062659787965
"[H]C1=C([H])C2=C([H])C([C@]3([H])N([H])C([H])([H])C([H])([H])N([H])C3([H])[H])=C([H])N([H])C2=N1 |(-2.1959,-2.651,-0.8071;-1.3981,-1.9215,-0.7117;-0.0412,-2.1573,-0.8852;0.4375,-3.0932,-1.1402;0.5842,-0.9001,-0.6511;1.8592,-0.358,-0.6446;2.7252,-0.9784,-0.8735;2.0629,1.0141,-0.3513;3.4948,1.5524,-0.3161;4.0754,0.9281,-1.0073;4.1702,1.4348,0.995;4.073,0.4806,1.3371;3.6659,2.3751,1.999;2.5976,2.2314,2.2401;4.2383,2.229,2.9221;3.8695,3.7992,1.4851;4.9543,3.9852,1.3831;3.4628,4.5204,2.2029;3.1546,3.9455,0.2112;3.2592,4.8971,-0.1352;3.6666,3.0049,-0.7935;3.1195,3.155,-1.7303;4.742,3.1578,-0.9951;0.9578,1.8108,-0.075;1.0538,2.8664,0.1456;-0.2972,1.2879,-0.0849;-1.0974,1.8794,0.1135;-0.5414,-0.0239,-0.3541;-1.7237,-0.6165,-0.3864)|",4.0653809264700005
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"[H]C1=C(F)C([H])=C([H])C([C@@]2([H])N=NC(=O)C([H])=C2[H])=N1 |(1.1366,-1.9373,0.7941;0.5473,-1.1352,0.3556;-0.7064,-1.4022,-0.1862;-1.1837,-2.659,-0.162;-1.4581,-0.3789,-0.7455;-2.4364,-0.5862,-1.167;-0.9126,0.9038,-0.7433;-1.4478,1.7417,-1.1725;0.3527,1.0944,-0.1778;1.0466,2.4625,-0.1536;1.8187,2.4295,-0.9433;0.1069,3.5056,-0.6458;0.1,4.6602,-0.1813;1.0259,5.0569,0.9068;1.1266,6.2435,1.1299;1.7063,3.9958,1.6474;2.1892,4.2696,2.5805;1.7202,2.7526,1.1483;2.2149,1.9217,1.6427;1.0655,0.095,0.3581)|",3.5837394110850003
"[H]OC(=O)[C@]1([H])/C([H])=C(/[H])C2=NC([H])=C([H])C(=O)/C2=C/1[H] |(-4.7782,0.5375,0.4211;-4.5695,1.0156,1.2404;-3.2562,1.3547,1.2894;-2.8188,1.9918,2.2078;-2.4056,0.8642,0.0871;-2.9176,1.2659,-0.8097;-2.3958,-0.6418,-0.0051;-3.3533,-1.158,-0.015;-1.2571,-1.3497,-0.0796;-1.2633,-2.4333,-0.147;0.0523,-0.7096,-0.0769;1.1166,-1.4616,-0.1514;2.3515,-0.824,-0.1383;3.1966,-1.5059,-0.2039;2.5609,0.516,-0.0537;3.5611,0.9365,-0.0506;1.4319,1.4377,0.0237;1.5419,2.6606,0.0779;0.0954,0.754,0.0203;-1.0409,1.474,0.1241;-0.9641,2.553,0.2308)|",3.455845901350001
"[H]C1N=C(SC([H])([H])[H])N=C2NC(=O)OC(=O)[C@]12[H] |(5.4131,4.7905,-0.9842;4.7268,4.0061,-1.3008;4.2368,3.2174,-0.4199;3.3791,2.2051,-0.9105;2.5557,1.4207,0.4062;1.5251,0.2094,-0.4877;1.0007,-0.3649,0.2794;2.1552,-0.4459,-1.0908;0.8077,0.7191,-1.1329;3.2163,1.8209,-2.1434;3.836,2.5503,-3.1257;3.8899,2.0761,-4.3273;4.549,2.7956,-5.3063;4.4098,2.6601,-6.4863;5.5597,3.7339,-4.8961;5.5992,4.2655,-3.6544;6.49,4.9935,-3.3043;4.4035,3.9095,-2.7699;3.6365,4.6746,-2.9892)|",4.046332956935
"[H]C#CC([H])([H])N1C(=O)OC(=O)[C@@]2([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])[H])N([H])[C@]12[H] |(7.1485,-1.6683,-0.3411;6.2032,-1.1875,-0.2246;5.1205,-0.6753,-0.07;3.8207,-0.0101,0.0923;3.06,-0.7285,0.4056;3.5057,0.4312,-0.859;3.8351,1.0454,1.1124;3.2176,0.7706,2.3035;2.5703,-0.2181,2.5531;3.3681,1.7269,3.309;3.7816,3.0295,3.1015;3.8774,3.7629,4.0469;4.061,3.3891,1.6622;3.0888,3.5017,1.1586;4.8624,4.6822,1.5041;4.3086,5.5136,1.9501;5.8018,4.606,2.0646;5.1232,4.9283,0.0124;5.7526,5.8149,-0.1249;4.1691,5.1303,-0.4904;5.7885,3.719,-0.6674;5.7399,3.8776,-1.7519;7.2754,3.5667,-0.2927;7.8423,4.449,-0.612;7.7124,2.6938,-0.7927;7.4307,3.4453,0.7846;4.9769,2.5006,-0.4218;5.4701,1.7141,-0.8442;4.7465,2.198,0.9849;5.6816,1.9581,1.5261)|",6.201474652895
"[H]N1[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[C@]2([H])C(=O)OC(=O)N(C([H])([H])C([H])([H])C([H])([H])[H])[C@@]12[H] |(4.2344,0.2296,-2.472;4.8212,1.0262,-2.716;5.5758,0.6633,-3.9418;4.8153,0.5737,-4.7274;6.3054,-0.6913,-3.8555;6.7307,-0.958,-4.83;5.6076,-1.489,-3.5714;7.1237,-0.6907,-3.1278;6.5112,1.8223,-4.3265;7.0982,1.5376,-5.2073;5.8965,2.6849,-4.613;7.4343,2.2247,-3.1699;8.0292,3.1055,-3.4279;8.1517,1.4272,-2.9416;6.5839,2.5274,-1.9346;5.9257,3.3833,-2.149;7.4084,2.8799,-0.7191;8.5638,3.2108,-0.7238;6.7411,2.8027,0.4839;5.3873,2.4619,0.6203;4.84,2.7431,1.6608;4.8127,1.8241,-0.4403;3.4152,1.3975,-0.2817;2.9428,2.1317,0.3736;2.9384,1.4722,-1.2626;3.2539,-0.0056,0.3182;3.7566,-0.7494,-0.3175;3.7625,-0.0282,1.2893;1.7797,-0.3841,0.4892;1.6748,-1.3867,0.9169;1.2475,-0.3747,-0.4705;1.2687,0.3183,1.1581;5.6505,1.3681,-1.567;6.2646,0.5135,-1.2222)|",6.261339700005
"[H]N1[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[C@]2([H])C(=O)OC(=O)N(C([H])(C([H])([H])[H])C([H])([H])[H])[C@@]12[H] |(3.9985,2.521,0.0091;3.4047,2.8386,0.7737;3.8513,4.2076,1.1381;3.6013,4.8269,0.2676;5.3701,4.3298,1.367;5.6521,5.3798,1.5066;5.9244,3.953,0.4984;5.713,3.7754,2.2468;3.0239,4.7163,2.3299;3.3778,5.7124,2.6191;1.9804,4.8236,2.0081;3.0892,3.7548,3.5217;2.4362,4.084,4.335;4.102,3.7155,3.9401;2.6668,2.3569,3.0619;1.6136,2.3819,2.7443;2.7957,1.3232,4.1579;2.8199,1.5529,5.3372;2.9181,0.0196,3.7292;2.7534,-0.4027,2.3979;2.536,-1.5786,2.2202;2.8636,0.5646,1.4429;2.6829,0.1819,0.0151;2.6659,1.1339,-0.5166;3.8529,-0.6699,-0.498;3.7177,-0.8934,-1.5622;3.9092,-1.614,0.05;4.8095,-0.1449,-0.3833;1.3183,-0.4745,-0.2357;1.1707,-0.5853,-1.3159;0.5126,0.157,0.1534;1.2517,-1.4581,0.2311;3.4653,1.8613,1.8497;4.5048,1.6613,2.1732)|",6.3021567775800005
"[H]N1C([H])([H])C([H])([H])C([H])([H])[C@]2([H])C(=O)OC(=O)N(C([H])(C([H])([H])[H])C([H])([H])[H])[C@@]12[H] |(2.0472,0.8996,2.7339;1.62,0.0005,2.9487;1.3581,-0.0275,4.4004;2.2851,0.1064,4.9894;0.7035,0.8207,4.6257;0.7012,-1.3459,4.8067;0.5172,-1.3473,5.8872;-0.2714,-1.4326,4.3061;1.6033,-2.5205,4.4139;1.133,-3.4858,4.6208;2.5306,-2.4943,5.0018;1.9442,-2.427,2.9224;1.0267,-2.5557,2.3293;2.9272,-3.4922,2.4915;3.1341,-4.5279,3.0648;3.6471,-3.207,1.3516;3.4091,-2.1069,0.51;3.8596,-2.1658,-0.6104;2.6973,-1.0769,1.0517;2.4127,0.1132,0.2021;1.7266,0.7143,0.7993;3.6815,0.9269,-0.0881;3.4243,1.8356,-0.6439;4.3849,0.3419,-0.6862;4.1831,1.2291,0.8394;1.6445,-0.2641,-1.0722;1.3245,0.6528,-1.58;0.7488,-0.8419,-0.8208;2.2602,-0.8478,-1.7576;2.5203,-1.0607,2.5235;3.5189,-0.954,2.9952)|",6.370185240205
"[H]C1=NC(=O)C(=C([H])[H])N=C1Cl |(6.0433,0.0416,-0.0539;4.9546,0.1155,-0.0539;4.412,1.2795,-0.0521;2.9853,1.3425,-0.0504;2.4308,2.4232,-0.0459;2.2284,0.0426,-0.054;0.8801,0.0382,-0.055;0.3342,0.9748,-0.0545;0.3369,-0.9006,-0.0563;2.9034,-1.1862,-0.0559;4.1764,-1.1358,-0.0561;5.1098,-2.6216,-0.06)|",3.54292233351
"[H]C1C(C2N([H])N([H])N([H])N2[H])=NC([H])=C1[H] |(2.2683,0.5693,0.253;1.2275,0.3023,0.1082;0.0824,1.1677,0.2063;0.0254,2.5221,0.4493;-1.1255,3.2752,0.4763;-1.8611,3.0368,-0.1787;-0.7631,4.6736,0.5675;-0.8165,4.8914,1.5646;0.6287,4.7164,0.2662;0.6717,4.7094,-0.7614;1.0606,3.4006,0.72;2.0074,3.1538,0.4615;-1.1011,0.4693,-0.0343;-0.7115,-0.7696,-0.2715;-1.4354,-1.5451,-0.5062;0.7171,-0.9379,-0.191;1.2688,-1.858,-0.341)|",4.381032993050001
"[H]OB(O[H])[C@]1([H])C([H])C2=C([H])C([H])=C([H])C([H])=C2NC1=O |(1.7175,1.6444,1.9709;1.6682,2.6103,2.0384;2.0372,3.2541,0.9055;2.0527,4.6155,0.9413;2.364,5.0208,0.1223;2.4103,2.4456,-0.4806;3.1078,3.0711,-1.0495;1.1236,2.2456,-1.1822;0.702,3.0651,-1.7632;0.4298,1.0807,-1.0381;-0.8784,0.8627,-1.6117;-1.2944,1.6368,-2.2521;-1.5743,-0.2702,-1.3359;-2.5598,-0.4322,-1.7615;-1.0095,-1.2698,-0.4617;-1.5944,-2.16,-0.2448;0.2305,-1.1325,0.0847;0.6667,-1.8936,0.723;1.0424,0.0283,-0.1983;2.2688,0.0755,0.2875;3.0965,1.1421,-0.0937;4.3095,1.055,-0.0411)|",3.0694442336400005
"[H]C1=C([H])C(=O)[C@]2([H])C(=N1)/C([H])=C(C#N)\C([H])=C/2[H] |(-2.1974,-4.4216,-0.5693;-1.5435,-3.5925,-0.3073;-2.0498,-2.5208,0.369;-3.0779,-2.5092,0.7164;-1.1827,-1.4189,0.7624;-1.5,-0.4941,1.4938;0.1941,-1.4578,0.0688;-0.0716,-0.9506,-0.8883;0.6251,-2.8319,-0.3902;-0.2204,-3.794,-0.6534;2.031,-3.0642,-0.5826;2.3296,-4.0035,-1.035;2.9539,-2.1603,-0.1269;4.3523,-2.4305,-0.286;5.4922,-2.6294,-0.4084;2.5586,-0.9391,0.5684;3.3385,-0.3161,0.9937;1.2575,-0.6192,0.6948;0.93,0.2663,1.2294)|",3.31434669909
"[H]C1=C([H])C(=O)[C@@]([H])(C(=O)OC([H])([H])[H])C(OC([H])([H])[H])=N1 |(6.0978,-0.3868,1.4031;5.3189,-1.1457,1.4214;5.5292,-2.3204,2.0635;6.4614,-2.5276,2.5773;4.4854,-3.3322,2.1215;4.5719,-4.3895,2.7263;3.1889,-2.9971,1.3323;3.1223,-3.7241,0.5134;1.9821,-3.1971,2.25;1.639,-2.4032,3.0955;1.3857,-4.3747,2.0126;0.2916,-4.6951,2.8925;-0.0678,-5.6703,2.5643;-0.4966,-3.9427,2.8092;0.6404,-4.7394,3.927;3.1884,-1.6104,0.7285;2.0423,-1.3359,0.1056;1.9193,-0.0371,-0.5029;0.9228,-0.0238,-0.9447;2.6881,0.0997,-1.2671;2.0217,0.7446,0.2534;4.1598,-0.7677,0.7473)|",4.58239724242
"[H]OC(=O)/C(C#N)=C(\[H])C1=C(C([H])([H])[H])ON=C1C([H])([H])[H] |(2.2093,-4.8741,2.3894;1.5372,-5.0303,1.7013;1.5642,-4.067,0.7569;0.7903,-4.0853,-0.1709;2.6003,-2.9806,0.9239;3.4384,-3.0385,2.0745;4.0556,-3.1531,3.0577;2.6051,-1.9669,0.0144;1.7977,-2.0404,-0.7131;3.4748,-0.8313,-0.118;4.8048,-0.6338,0.2172;5.8798,-1.4856,0.7965;5.7682,-2.5227,0.4684;5.8478,-1.4855,1.8914;6.8541,-1.1094,0.4741;5.175,0.5923,-0.1668;4.091,1.2628,-0.7747;3.1027,0.4076,-0.7615;1.7745,0.7822,-1.3405;1.4685,0.0709,-2.1164;1.8324,1.7781,-1.7853;0.9962,0.787,-0.5693)|",4.438176901655
"[H]OC1=NN([H])[C@]([H])(C2=C(Cl)C([H])=C([H])C([H])=C2[H])[C@@]1([H])C#N |(2.6253,5.6305,1.8499;2.0566,5.8983,1.1081;1.6465,4.7988,0.4462;1.1989,4.8358,-0.7463;0.6967,3.5489,-1.0605;0.877,3.3746,-2.0465;1.3745,2.555,-0.1862;2.3818,2.3268,-0.5582;0.5736,1.2802,-0.0269;1.1722,0.0239,0.1539;2.9275,-0.1464,0.1881;0.4101,-1.1341,0.3139;0.9104,-2.0866,0.4505;-0.9807,-1.0513,0.2965;-1.5727,-1.9535,0.4207;-1.6016,0.1851,0.1155;-2.6851,0.2565,0.0937;-0.8286,1.3321,-0.0454;-1.2985,2.2959,-0.2127;1.5354,3.4168,1.101;0.6123,3.3753,1.6991;2.6658,3.1074,1.971;3.5732,2.9662,2.6824)|",5.298056669235
"[H]OC1=NN([H])[C@]([H])(C2=C([H])C([H])=C(Cl)C([H])=C2[H])[C@@]1([H])C#N |(2.6059,5.6145,1.8868;2.0575,5.888,1.1326;1.649,4.7942,0.4599;1.2172,4.8423,-0.7387;0.7131,3.5592,-1.0681;0.8922,3.394,-2.0557;1.3783,2.5599,-0.1984;2.4016,2.3475,-0.5495;0.612,1.2649,-0.0598;-0.7869,1.2383,-0.127;-1.3249,2.1595,-0.3264;-1.4852,0.0432,0.0374;-2.5683,0.0226,-0.0212;-0.776,-1.1339,0.2757;-1.6508,-2.6432,0.4846;0.6165,-1.1331,0.3445;1.1545,-2.0576,0.5244;1.3015,0.0688,0.1716;2.388,0.0708,0.2198;1.5137,3.4088,1.1008;0.5773,3.3663,1.6778;2.624,3.0507,1.9762;3.5254,2.8276,2.6742)|",5.385133101395001
"[H]OC([H])([H])[C@@]1([H])N([H])N([H])[C@]([H])(C2=C([H])C([H])=C(F)C([H])=C2[H])C1([H])[H] |(3.2334,4.5648,3.6417;3.9427,3.9626,3.9238;3.949,2.8945,2.9922;4.5391,3.1489,2.0908;4.4484,2.0471,3.4742;2.537,2.5068,2.5768;1.9567,2.2833,3.4779;1.8701,3.6586,1.9282;2.5194,3.9724,1.1867;0.757,2.9971,1.2982;0.2731,3.6602,0.6964;1.3236,1.8474,0.5566;1.8428,2.1818,-0.3605;0.2648,0.8392,0.1649;-0.7876,0.5243,1.0371;-0.8569,1.0452,1.9866;-1.7468,-0.4251,0.689;-2.5687,-0.674,1.3526;-1.6426,-1.0623,-0.5429;-2.5688,-1.9837,-0.8853;-0.6164,-0.774,-1.4335;-0.5723,-1.2879,-2.3882;0.3314,0.1838,-1.0704;1.1354,0.4229,-1.7628;2.4051,1.3369,1.5628;2.0906,0.4122,2.0537;3.3485,1.1353,1.0423)|",5.93752421791
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"[H]SC1=NC([H])([H])N([H])[C@@]2([H])ON([H])C([H])([H])[C@]12[H] |(-0.1564,3.475,-0.2374;1.1125,3.1643,-0.5749;1.0236,1.426,-0.1443;2.1026,0.7605,-0.2413;2.1504,-0.6612,0.0449;2.4407,-1.1449,-0.9045;2.9706,-0.8291,0.7523;0.919,-1.2692,0.6183;0.9445,-2.2763,0.4615;-0.2183,-0.6788,-0.0281;-0.1996,-0.7857,-1.1379;-1.4709,-1.1434,0.4456;-2.4185,-0.1496,-0.0483;-2.5931,-0.4571,-1.0089;-1.675,1.1588,-0.1274;-1.695,1.549,-1.1515;-2.1593,1.8844,0.532;-0.2599,0.7975,0.3434;-0.2402,0.8242,1.4396)|",6.15521529831
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"[H]OC(=N/[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[H])/C([H])=C(\[H])C1=C([H])SC(C([H])([H])[H])=N1 |(7.3859,-0.8733,-1.5628;6.6703,-0.2187,-1.571;5.6103,-0.7543,-2.2659;4.4696,-0.2492,-2.0188;3.2761,-0.6077,-2.7709;3.3972,-1.5389,-3.3502;2.9689,0.523,-3.7669;2.0461,0.3101,-4.3191;3.7852,0.638,-4.4882;2.848,1.4734,-3.236;2.1116,-0.8151,-1.7867;1.9692,0.1143,-1.2204;1.1954,-0.9801,-2.3703;2.3286,-1.9825,-0.8209;1.4788,-2.0959,-0.138;3.2284,-1.8244,-0.217;2.4473,-2.9301,-1.3625;5.9501,-1.8276,-3.2224;5.1718,-2.5345,-3.4884;7.1625,-1.9662,-3.7959;7.9503,-1.2426,-3.5936;7.5091,-3.0246,-4.733;8.7263,-3.1504,-5.357;9.5922,-2.5089,-5.2642;8.7376,-4.5361,-6.3923;7.067,-4.8658,-5.9046;6.3134,-6.0443,-6.4409;5.2952,-6.0154,-6.0467;6.7805,-6.9889,-6.1388;6.2703,-6.0297,-7.5356;6.5865,-4.0088,-5.0589)|",4.5170899183
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"[H]OC(=N/C([H])(C([H])([H])[H])C([H])([H])[H])/C([H])=C(\[H])C1=C([H])SC(C([H])([H])[H])=N1 |(5.2542,0.021,-2.2722;4.8846,-0.8357,-2.0071;4.3941,-0.7108,-0.7269;3.5059,-1.554,-0.3864;2.977,-1.6298,0.9674;3.3969,-0.8635,1.6389;1.4558,-1.4333,0.9047;1.0107,-1.5302,1.9019;1.005,-2.1821,0.2444;1.2058,-0.4422,0.5104;3.3382,-3.0046,1.5508;2.9268,-3.1158,2.5611;4.4249,-3.1305,1.6045;2.936,-3.8019,0.9166;4.9929,0.373,0.0807;4.4124,0.7716,0.9055;6.2226,0.8791,-0.1404;6.8495,0.4662,-0.9292;6.8231,1.9501,0.6411;8.0931,2.4381,0.4519;8.8285,2.1197,-0.2744;8.4306,3.7045,1.5808;6.8032,3.4911,2.2481;6.306,4.3332,3.383;5.2991,4.0024,3.6474;6.2678,5.3934,3.1075;6.9515,4.2419,4.2638;6.1125,2.5626,1.6635)|",4.52797447232
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"[H]C1=NC(=O)/C(=N/C(=O)C([H])([H])[H])C([H])=C1C(F)(F)F |(0.6815,3.731,4.3863;1.2158,3.1032,3.6707;2.4413,2.8218,3.942;3.1682,2.0152,3.0251;4.3441,1.7983,3.2072;2.441,1.4365,1.8032;3.1,0.6288,1.0579;2.5382,-0.0355,-0.0531;1.5985,-0.7996,0.0618;3.2981,0.2072,-1.337;3.3724,1.2807,-1.5467;2.7918,-0.3017,-2.1589;4.3218,-0.1695,-1.2354;1.0548,1.8537,1.5774;0.5193,1.483,0.7107;0.47,2.6576,2.4846;-0.9499,3.1392,2.345;-0.9874,4.4897,2.3622;-1.5258,2.7223,1.2048;-1.7021,2.707,3.3785)|",3.072165372145
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"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C(C([H])([H])[H])C([H])=C(C([H])([H])[H])C([H])=C1[H] |(8.7569,-0.0446,0.4232;9.2488,-0.5011,-0.278;8.5952,-0.2707,-1.4595;9.3679,-0.2285,-2.4913;8.6218,-0.1352,-3.6777;9.5265,0.0407,-4.7599;8.9039,0.0673,-5.658;10.2341,-0.7943,-4.8186;10.0846,0.9803,-4.6653;7.1447,-0.1158,-1.4489;6.7184,0.2904,-2.359;6.3681,-0.4724,-0.4021;6.8478,-0.976,0.4352;4.9179,-0.2917,-0.2937;4.161,-1.0686,0.6218;4.8085,-2.1158,1.5011;4.0579,-2.6238,2.1136;5.547,-1.6793,2.1862;5.3294,-2.8797,0.9117;2.7809,-0.8719,0.6941;2.2059,-1.4772,1.3928;2.1078,0.0681,-0.0975;0.6129,0.2462,0.0208;0.2469,1.0169,-0.6646;0.3236,0.5384,1.0381;0.0803,-0.6853,-0.2082;2.8669,0.8334,-0.9893;2.3783,1.581,-1.6096;4.2433,0.6584,-1.0797;4.8127,1.2852,-1.7598)|",4.013679294875001
"[H]OC(=N/C([H])([H])C([H])([H])C([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1C#N |(4.3203,1.2671,-2.786;4.74,1.0797,-1.9319;5.2831,2.2347,-1.4353;6.1954,2.105,-0.5594;6.7748,3.2702,0.0982;7.3276,3.8878,-0.6294;5.9947,3.9155,0.5336;7.7368,2.829,1.207;8.5123,2.1959,0.7578;8.2423,3.7195,1.6054;7.0372,2.069,2.3362;7.7551,1.7278,3.0905;6.5145,1.1938,1.9378;6.2974,2.7036,2.8414;4.7273,3.5063,-1.9455;5.3705,4.374,-1.8323;3.5039,3.6295,-2.4968;2.8781,2.7428,-2.5797;2.896,4.8649,-3.0027;3.4092,6.1399,-2.7104;4.2781,6.2279,-2.0663;2.8147,7.2943,-3.209;3.2363,8.2641,-2.9606;1.6767,7.213,-4.0175;1.2106,8.1143,-4.4036;1.138,5.9677,-4.3203;0.2546,5.8825,-4.9446;1.7364,4.8005,-3.8195;1.1614,3.5325,-4.1639;0.6985,2.5026,-4.4459)|",4.258581760325001
"[H]C1=C([H])C([H])=C(/C([H])=C(\[H])C(=O)N(C([H])([H])[H])C([H])([H])[H])C(C#N)=C1[H] |(-1.1775,6.6457,-3.7621;-0.6311,5.8297,-3.2991;-0.0731,5.982,-2.0261;-0.1875,6.9209,-1.4917;0.6192,4.9332,-1.4286;1.02,5.0604,-0.428;0.7875,3.7009,-2.0811;1.521,2.58,-1.478;1.3155,1.6023,-1.9083;2.4068,2.6689,-0.4714;2.6315,3.6221,0.0002;2.9891,1.5039,0.2686;2.9438,1.5366,1.4983;3.4943,0.4349,-0.4284;3.8687,0.4437,-1.8362;3.2543,-0.254,-2.4206;4.9186,0.1379,-1.9369;3.7607,1.4419,-2.257;3.9107,-0.7423,0.3221;3.5373,-1.6468,-0.1727;3.5061,-0.6765,1.331;5.0062,-0.8056,0.3802;0.2078,3.5592,-3.369;0.3439,2.3282,-4.0924;0.4571,1.329,-4.6787;-0.492,4.619,-3.9682;-0.9205,4.4786,-4.9553)|",4.5170899183
"[H]C1=C([H])C(C#N)=C(/C([H])=C(\[H])C(=O)N2C([H])([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H])C([H])=C1[H] |(-1.8822,-9.0471,3.375;-1.7758,-8.059,2.9379;-2.3854,-6.9633,3.5403;-2.9651,-7.0868,4.4496;-2.2575,-5.6774,2.9886;-2.8942,-4.6073,3.6985;-3.4236,-3.766,4.3048;-1.5053,-5.4685,1.7998;-1.3023,-4.1872,1.1176;-0.5387,-4.196,0.3416;-1.9468,-3.0197,1.2879;-2.7516,-2.9219,2.0007;-1.5663,-1.8562,0.429;-0.8199,-2.0098,-0.5411;-2.1043,-0.6278,0.7426;-1.8031,0.5103,-0.1312;-1.2885,0.1205,-1.0086;-2.7579,0.9541,-0.4527;-0.9626,1.5679,0.5969;0.027,1.1446,0.8136;-0.8086,2.4282,-0.0665;-1.6434,2.0041,1.9027;-2.571,2.5457,1.6641;-1.0058,2.7028,2.4576;-1.9816,0.7839,2.7714;-2.5521,1.0827,3.6597;-1.056,0.3102,3.125;-2.7936,-0.2567,1.9813;-3.7752,0.1659,1.7178;-2.9746,-1.1326,2.6023;-0.8897,-6.5986,1.2305;-0.3013,-6.4616,0.3276;-1.0218,-7.8711,1.7781;-0.5339,-8.7159,1.3003)|",4.176947605175
"[H]OC(=N/C1=C([H])C(Cl)=C([H])C([H])=C1F)/C([H])=C([H])/C([H])=C(\[H])C([H])([H])[H] |(8.1632,-0.2918,3.2049;8.0108,-0.3704,2.2502;6.7473,-0.8338,2.0459;6.2674,-0.6332,0.8761;5.0525,-1.1758,0.4592;4.0904,-0.3313,-0.123;4.2934,0.7324,-0.1671;2.905,-0.8556,-0.6275;1.7127,0.2391,-1.3232;2.6421,-2.2253,-0.6073;1.717,-2.6162,-1.015;3.599,-3.0782,-0.0572;3.4455,-4.1521,-0.0269;4.7734,-2.5544,0.4634;5.6874,-3.4028,0.9861;6.0675,-1.4707,3.1828;4.9849,-1.5286,3.1083;6.6942,-1.9909,4.2602;7.7849,-1.9927,4.3027;6.0191,-2.608,5.3837;4.9297,-2.6304,5.3536;6.6632,-3.1437,6.4369;7.7541,-3.1147,6.4496;6.0033,-3.7903,7.6145;6.2741,-3.2788,8.5482;6.3304,-4.8331,7.7252;4.9125,-3.7817,7.5237)|",4.0218427103900005
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C(OC([H])([H])[H])=C([H])C([H])=C1[H] |(4.1254,4.1498,-4.9198;5.0236,4.3576,-5.2231;5.5686,3.2092,-5.7313;6.4084,3.4013,-6.6909;7.0303,2.1994,-7.0671;7.842,2.4553,-8.2059;8.329,1.5039,-8.435;8.5967,3.2189,-7.9853;7.238,2.7802,-9.0619;5.1802,1.9236,-5.1601;5.4478,1.0591,-5.7564;4.5501,1.7987,-3.9731;4.3753,2.6984,-3.3829;4.0904,0.5587,-3.342;3.5239,0.6342,-2.0636;3.4319,1.5895,-1.5546;3.0677,-0.5127,-1.4013;2.5373,-0.3025,-0.1614;2.062,-1.4252,0.5645;1.6911,-1.0324,1.5129;2.8645,-2.1484,0.7612;1.2425,-1.9304,0.0364;3.1739,-1.761,-2.0227;2.8286,-2.6638,-1.5326;3.7373,-1.8378,-3.3029;3.8167,-2.8083,-3.7854;4.1904,-0.7047,-3.9625;4.6179,-0.7947,-4.9555)|",4.057217510955
"[H]OC(=N/OC([H])([H])[H])/C1=N/C(=O)[C@@]2([H])C([H])=C([H])C([H])=C([H])\C2=C\1[H] |(2.9524,0.0887,-0.2332;2.9699,1.0712,-0.1246;4.2336,1.3429,0.2486;4.4787,2.5889,0.4863;5.785,2.8547,0.8993;5.9034,4.2595,1.1121;6.9254,4.414,1.467;5.1867,4.5999,1.867;5.741,4.8105,0.1793;5.1165,0.1284,0.3299;4.4262,-0.9775,0.1136;5.0561,-2.2162,0.0943;4.4842,-3.2277,-0.2654;6.489,-2.2798,0.6583;6.2712,-2.395,1.7444;7.2597,-3.5061,0.2839;6.6849,-4.4109,0.118;8.6056,-3.4731,0.2125;9.1702,-4.3717,-0.0194;9.3257,-2.2331,0.4192;10.412,-2.2535,0.3885;8.682,-1.0435,0.5865;9.2414,-0.1149,0.6624;7.2478,-0.9814,0.5747;6.5358,0.1878,0.5228;7.0264,1.1488,0.5551)|",3.0095791865299995
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C(OC([H])([H])[H])C([H])=C([H])C(C([H])([H])[H])=C1[H] |(6.6434,-3.9725,1.1094;6.5369,-4.7601,0.5531;5.2169,-4.8457,0.2025;4.8092,-6.0483,-0.0253;3.488,-6.0521,-0.5056;3.0705,-7.4031,-0.643;2.0479,-7.3549,-1.0269;3.082,-7.9246,0.3219;3.7106,-7.9426,-1.3511;4.4243,-3.6245,0.1082;3.3524,-3.7786,0.0582;4.9725,-2.3908,0.0587;6.0546,-2.3021,0.0099;4.2547,-1.1172,0.0108;4.9684,0.0732,-0.2893;6.3074,-0.0703,-0.5208;7.0692,1.0837,-0.8369;8.0921,0.7343,-0.9878;7.0518,1.8149,-0.018;6.7096,1.5621,-1.757;4.3008,1.2978,-0.3393;4.8373,2.2111,-0.5689;2.9277,1.3558,-0.0922;2.4259,2.3196,-0.1344;2.1924,0.2068,0.2131;0.7068,0.2683,0.4863;0.3385,1.2986,0.4528;0.4613,-0.1403,1.4745;0.1374,-0.3109,-0.2515;2.8792,-1.0095,0.2622;2.3276,-1.9105,0.5179)|",3.932045139725001
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C(OC([H])([H])C([H])([H])[H])C([H])=C([H])C([H])=C1[H] |(2.3315,-4.4722,-2.2747;2.5179,-5.3462,-1.8972;3.852,-5.5967,-2.069;4.1446,-6.8494,-2.1663;5.5373,-7.0431,-2.2043;5.7875,-8.4168,-2.4672;6.8753,-8.5241,-2.4476;5.4022,-8.7123,-3.451;5.3347,-9.0527,-1.6976;4.7871,-4.478,-2.1173;5.7756,-4.7298,-2.4837;4.4625,-3.2235,-1.7364;3.4688,-3.0366,-1.3387;5.3293,-2.0455,-1.774;4.7721,-0.7631,-1.5223;3.4245,-0.7399,-1.2958;2.765,0.4743,-0.9213;3.0822,1.2946,-1.5771;1.7098,0.2777,-1.1311;2.9574,0.8183,0.5517;2.3748,1.7113,0.8053;4.0072,1.0146,0.7893;2.6139,-0.0087,1.1812;5.5848,0.3765,-1.5411;5.1669,1.3565,-1.3444;6.9477,0.2596,-1.8156;7.5651,1.1537,-1.8256;7.514,-0.9903,-2.0665;8.5761,-1.083,-2.2719;6.7061,-2.1221,-2.0415;7.1517,-3.0959,-2.2195)|",3.95109310926
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(OC([H])([H])[H])C(F)=C1[H] |(4.178,-0.6092,-7.4611;4.8269,-0.0099,-7.8628;4.6536,1.2276,-7.3026;4.9238,2.209,-8.095;4.8592,3.4394,-7.4207;5.0612,4.4778,-8.3702;5.0201,5.408,-7.7976;4.2752,4.4759,-9.1351;6.0387,4.3802,-8.8567;4.2139,1.3244,-5.9157;3.8712,2.3073,-5.6125;4.2455,0.2855,-5.0527;4.6774,-0.6555,-5.394;3.8003,0.2733,-3.6618;4.0154,-0.8742,-2.8753;4.5092,-1.7367,-3.3151;3.6147,-0.9279,-1.5479;3.7832,-1.8148,-0.9455;2.9772,0.1595,-0.9318;2.6452,-0.0107,0.3721;1.8512,0.948,1.0756;1.6905,0.5081,2.0615;0.8867,1.1074,0.5834;2.3699,1.9048,1.1789;2.7656,1.303,-1.7237;2.1582,2.3986,-1.1993;3.1579,1.3627,-3.0488;2.9506,2.2799,-3.5896)|",3.90755489318
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(C([H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(4.7444,0.6025,7.6504;5.6403,0.9203,7.8458;5.9384,1.9064,6.9436;6.7387,2.8105,7.3966;7.1135,3.7079,6.3822;7.8667,4.7575,6.9757;8.1589,5.4102,6.1489;8.7592,4.3646,7.4763;7.2651,5.3205,7.6998;5.3617,1.8442,5.6054;5.4447,2.7591,5.0293;4.7827,0.7356,5.0966;4.8013,-0.1769,5.6933;4.1488,0.5871,3.7865;3.6877,-0.6797,3.3907;3.8113,-1.5244,4.065;3.0848,-0.8737,2.1504;2.7424,-1.8684,1.8735;2.9127,0.1859,1.2526;2.2529,-0.0384,-0.0998;2.0222,-1.1101,-0.1704;0.9217,0.7286,-0.2234;0.436,0.5018,-1.1796;1.0811,1.8124,-0.1786;0.2315,0.4591,0.5834;3.1998,0.3077,-1.2645;2.7307,0.0681,-2.2259;4.1379,-0.253,-1.1919;3.4483,1.3754,-1.2732;3.3677,1.4555,1.6501;3.2478,2.3044,0.9816;3.9698,1.6549,2.8853;4.303,2.6524,3.1564)|",4.01095815637
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(C#N)C([H])=C1[H] |(3.3322,1.007,1.7755;2.6118,0.5849,1.2803;3.1675,-0.2769,0.374;2.4738,-1.3428,0.1583;3.02,-2.1045,-0.8808;2.2835,-3.3191,-0.9846;2.7233,-3.8521,-1.8311;1.2243,-3.1165,-1.1772;2.3801,-3.9209,-0.0734;4.4311,0.0752,-0.264;4.9224,-0.7402,-0.7828;4.9545,1.3194,-0.2378;4.3743,2.115,0.2287;6.2261,1.7491,-0.8205;6.5379,3.1222,-0.8318;5.8299,3.8305,-0.4095;7.7263,3.5909,-1.3767;7.9495,4.6528,-1.3805;8.6469,2.6845,-1.9268;9.8773,3.1555,-2.4893;10.8777,3.5372,-2.9448;8.354,1.3079,-1.918;9.0683,0.6068,-2.3373;7.164,0.8524,-1.3715;6.962,-0.2138,-1.3672)|",3.687142674275
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(N(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(5.9978,-1.8784,-6.4519;5.5509,-2.4725,-7.0754;4.9537,-3.4636,-6.3411;4.8605,-4.5921,-6.9592;4.1206,-5.5199,-6.2013;4.1236,-6.7593,-6.8937;3.5252,-7.4382,-6.28;5.1409,-7.1555,-7.0027;3.6701,-6.6569,-7.8869;4.4871,-3.1618,-4.994;4.2555,-4.0274,-4.3841;4.3194,-1.902,-4.5299;4.4851,-1.0755,-5.2228;3.8905,-1.4923,-3.1994;3.6516,-0.1312,-2.9355;3.7975,0.5946,-3.7331;3.2309,0.3213,-1.6936;3.065,1.3828,-1.5547;3.0203,-0.5828,-0.6261;2.5823,-0.1491,0.6107;2.4404,1.2737,0.8663;2.0687,1.421,1.8818;1.7191,1.7322,0.1776;3.3942,1.8145,0.7692;2.4939,-1.0828,1.7202;2.1087,-0.5597,2.5972;3.4703,-1.5177,1.9831;1.8049,-1.9071,1.4958;3.2769,-1.9542,-0.8805;3.151,-2.6878,-0.0932;3.6951,-2.3862,-2.1281;3.8825,-3.4466,-2.2707)|",3.76605569092
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(OC([H])([H])C([H])([H])[H])C([H])=C1[H] |(11.9198,-2.7156,-0.4554;12.348,-2.612,-1.3201;11.8088,-1.5045,-1.9195;12.6252,-0.8752,-2.6949;11.9703,0.1637,-3.3796;12.9446,0.8953,-4.1103;12.39,1.6794,-4.6328;13.4557,0.2519,-4.8361;13.6885,1.3475,-3.4429;10.4184,-1.1565,-1.6507;10.1307,-0.1574,-1.9577;9.5351,-2.0079,-1.0844;9.8661,-3.0261,-0.8771;8.1405,-1.7449,-0.7394;7.5344,-0.4826,-0.864;8.1137,0.3609,-1.2276;6.2005,-0.2748,-0.5273;5.7745,0.7154,-0.6393;5.4259,-1.3417,-0.0458;4.117,-1.2465,0.3118;3.4606,0.017,0.1907;3.9807,0.7656,0.8048;3.4948,0.3531,-0.8552;2.0263,-0.1645,0.6562;1.4836,0.7839,0.5795;1.9975,-0.4979,1.6982;1.5126,-0.9098,0.0411;6.0121,-2.6094,0.0911;5.4023,-3.4257,0.4651;7.3421,-2.7991,-0.249;7.7804,-3.7885,-0.1392)|",3.967419940290001
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C(Cl)=C(C([H])([H])[H])C([H])=C1[H] |(8.3545,-0.087,-0.0074;8.7976,-0.1994,-0.8635;8.0631,-1.085,-1.604;8.7613,-1.8082,-2.412;7.9339,-2.5781,-3.2438;8.7488,-3.4923,-3.9671;8.0697,-4.0089,-4.6501;9.2265,-4.2183,-3.2975;9.5213,-2.9619,-4.535;6.6147,-1.1273,-1.434;6.1309,-1.9978,-1.8623;5.9109,-0.1622,-0.8053;6.441,0.7275,-0.4651;4.4697,-0.1472,-0.5492;3.9029,0.9938,0.0434;4.5306,1.8424,0.2973;2.5387,1.0528,0.3069;1.9026,2.518,1.0532;1.6698,-0.0047,0.0023;0.1923,0.0538,0.2874;-0.2965,-0.8779,-0.0112;-0.2857,0.8803,-0.2516;-0.0029,0.2184,1.3535;2.2529,-1.1381,-0.5871;1.611,-1.9799,-0.8342;3.6124,-1.2187,-0.8579;4.0099,-2.1228,-1.3079)|",3.96741994029
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1Cl |(3.3697,-2.0559,0.9661;2.6697,-1.3843,0.9984;3.2486,-0.1582,0.8212;2.6499,0.8056,1.4342;3.2078,2.0478,1.0952;2.5988,3.0465,1.9049;3.0322,3.9934,1.5729;2.8193,2.8872,2.9673;1.5128,3.0586,1.759;4.4344,-0.0412,-0.0229;4.9743,0.893,0.0796;4.8432,-1.0145,-0.8629;4.2473,-1.9195,-0.9414;6.0298,-0.9704,-1.7216;6.6849,0.2416,-2.0225;6.28,1.1652,-1.6214;7.8095,0.2884,-2.8366;8.2833,1.2427,-3.0469;8.32,-0.8886,-3.3897;9.1962,-0.8631,-4.0308;7.6959,-2.105,-3.1225;8.0751,-3.0301,-3.543;6.5695,-2.1369,-2.3023;5.8434,-3.7161,-1.9932)|",3.9837467713200008
"[H]OC(=N/OC([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C(N(=O)=O)C([H])=C1[H] |(2.6369,0.1145,-1.8176;2.1236,-0.394,-1.1697;2.8168,-0.4037,0.0094;2.0736,-0.3887,1.0642;2.8311,-0.5454,2.2287;1.969,-0.3934,3.3527;2.6038,-0.5548,4.2273;1.1651,-1.137,3.3285;1.5348,0.6125,3.386;4.274,-0.4517,-0.0192;4.7555,-0.2384,0.9283;4.9833,-0.7544,-1.1275;4.4399,-1.0366,-2.0287;6.4405,-0.7932,-1.2555;7.3172,-0.3924,-0.2256;6.9192,-0.0249,0.7143;8.6929,-0.4514,-0.3932;9.3737,-0.1453,0.3914;9.2076,-0.9126,-1.6063;10.6636,-0.9738,-1.7891;11.0875,-1.3856,-2.8695;11.3738,-0.6097,-0.8513;8.376,-1.3131,-2.6495;8.8113,-1.6648,-3.5765;7.0002,-1.2486,-2.4657;6.3409,-1.5606,-3.2713)|",3.3415580841399994
"[H]OC(=N/C([H])([H])C([H])([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=C([C@]2([H])C([H])([H])[C@@]2([H])C([H])([H])[H])O1 |(3.1593,-0.5959,0.1657;2.9975,-0.6412,-0.7895;3.2936,0.5891,-1.3311;2.7353,0.8531,-2.4439;3.037,2.0577,-3.1924;3.5294,2.8483,-2.6031;3.7425,1.7912,-3.9935;1.7574,2.6135,-3.8223;1.9773,3.49,-4.4429;1.042,2.9097,-3.0473;1.2792,1.8516,-4.446;4.2417,1.4142,-0.5627;4.2237,2.4849,-0.7347;5.1488,0.9063,0.3034;5.2238,-0.171,0.4415;6.1004,1.6614,1.0652;7.093,1.2847,1.9378;7.3371,0.2684,2.2162;7.7253,2.478,2.3889;8.5489,2.5625,3.084;7.0846,3.5194,1.7683;7.2726,4.9722,1.8179;8.0697,5.269,2.4951;6.0907,5.9261,1.7046;5.1087,5.4838,1.5648;6.1072,6.8071,2.3415;7.082,5.8404,0.5802;6.7406,5.2964,-0.2985;8.0423,6.9753,0.297;7.5714,7.7367,-0.3366;8.9418,6.6182,-0.2184;8.3597,7.4668,1.2244;6.0942,3.0359,0.962)|",3.989189048330001
"[H]NC(O[H])/C([H])=C(\[H])C1=C(C([H])([H])[H])N(C2=C([H])C([H])=C([H])C([H])=C2[H])C(C([H])([H])[H])=C1[H] |(6.3036,5.2197,-0.4663;7.2328,4.8552,-0.2458;7.1395,3.6181,0.0559;8.3122,2.9539,0.3015;8.1073,2.1652,0.8268;5.8983,2.8272,0.1395;4.9838,3.4064,0.2442;5.8384,1.4806,0.0376;6.7706,0.9418,-0.1328;4.6461,0.664,0.0962;4.6023,-0.7169,-0.0909;5.6866,-1.6925,-0.4222;6.6372,-1.1723,-0.5612;5.4716,-2.2385,-1.3494;5.8313,-2.4432,0.3649;3.2829,-1.1147,0.0391;2.8106,-2.4598,-0.0842;2.1216,-2.8552,-1.2354;1.9633,-2.1364,-2.0339;1.656,-4.1656,-1.3498;1.1201,-4.4697,-2.2446;1.8848,-5.0838,-0.3231;1.5249,-6.1046,-0.4161;2.5765,-4.6883,0.8235;2.7542,-5.3986,1.626;3.0354,-3.3765,0.9478;3.5598,-3.0524,1.8417;2.4792,-0.0032,0.3086;1.0062,-0.1174,0.5387;0.5981,0.8727,0.7599;0.7699,-0.7749,1.3852;0.4714,-0.5124,-0.3338;3.2995,1.0925,0.3489;2.9735,2.1036,0.5526)|",4.41912893212
"[H]OC1=NC(=O)N(C([H])([H])C(=O)N2C([H])([H])C([H])([H])C([H])([H])C2([H])[H])C([H])([H])C1([H])[H] |(-6.8785,1.7768,2.1026;-6.2071,2.477,2.0915;-5.2035,2.13,1.2575;-4.2222,2.939,1.1603;-3.1681,2.6571,0.2607;-2.344,3.5232,-0.0034;-3.115,1.3986,-0.3197;-1.9304,1.0673,-1.1062;-2.2001,0.2682,-1.8057;-1.6484,1.9512,-1.6757;-0.8012,0.517,-0.2194;-0.9968,-0.51,0.4372;0.3835,1.1732,-0.1948;1.4762,0.6603,0.6491;1.1832,0.7129,1.7057;1.6706,-0.3913,0.4192;2.6498,1.5954,0.3283;3.3509,1.6847,1.1636;3.2075,1.2176,-0.5379;1.9569,2.9223,-0.0257;2.5879,3.6023,-0.6055;1.6501,3.4441,0.8886;0.711,2.4768,-0.806;0.9442,2.3534,-1.8738;-0.1256,3.1699,-0.7006;-3.9314,0.3071,0.2036;-3.963,-0.4916,-0.5425;-3.4827,-0.1205,1.1093;-5.3363,0.8348,0.4887;-5.9146,0.0928,1.0528;-5.8746,1.0357,-0.4482)|",5.36064285485
"[H]C#CC([H])([H])/N=C(/O[H])C([H])([H])N1C(=O)N=C(O[H])C([H])([H])C1([H])[H] |(9.6535,1.5669,-2.8614;8.5926,1.6313,-2.774;7.3894,1.7063,-2.6937;5.9245,1.7726,-2.5814;5.5317,2.3018,-3.4582;5.6634,2.4067,-1.716;5.2621,0.4692,-2.5349;5.3739,-0.2679,-1.5026;4.7367,-1.4527,-1.5022;4.9279,-1.9276,-0.657;6.1762,0.0048,-0.2244;6.7231,0.9455,-0.293;6.9115,-0.7982,-0.1056;5.3393,0.0403,0.9831;5.0433,-1.1427,1.6209;5.2525,-2.2267,1.0707;4.5204,-1.0975,2.9226;3.9275,-0.0357,3.3107;3.4113,-0.0312,4.5528;2.9812,0.8199,4.7309;3.7671,1.1963,2.4474;2.8827,1.0713,1.8088;3.6236,2.1008,3.0493;5.0271,1.329,1.5892;5.8687,1.6863,2.2033;4.8588,2.0628,0.7955)|",5.091250142854999
"[H]C1=C([H])/C2=C([H])/C(C(=O)N(C([H])([H])[H])C([H])([H])[H])=N\C(=O)[C@@]2([H])C([H])=C1[H] |(3.1383,7.8413,-1.1953;2.4537,7.0002,-1.2671;2.8962,5.7471,-0.9706;3.9165,5.5815,-0.6352;1.9953,4.6266,-1.0054;2.2839,3.3864,-0.5155;3.2467,3.1453,-0.081;1.2525,2.381,-0.4879;1.669,1.0483,0.1253;2.5032,1.0912,1.0322;1.1045,-0.1123,-0.3135;0.1858,-0.2657,-1.4363;0.4736,-1.1625,-1.9983;0.2241,0.5952,-2.098;-0.8484,-0.383,-1.0912;1.4114,-1.3419,0.4082;1.9028,-2.063,-0.2574;0.4868,-1.7949,0.7883;2.0725,-1.1052,1.24;-0.01,2.5596,-0.7895;-0.4306,3.83,-1.2055;-1.6113,4.1081,-1.2974;0.6497,4.8283,-1.6596;0.8076,4.498,-2.7118;0.1915,6.2507,-1.7599;-0.8551,6.4108,-1.9955;1.0681,7.2631,-1.6102;0.7464,8.2947,-1.7222)|",3.213664574405
"[H]C1=C([H])/C2=N/C(C(=O)N(C([H])([H])[H])C([H])([H])[H])=C(/[H])C(=O)[C@@]2([H])C([H])=C1[H] |(-2.978,3.2176,0.4244;-1.9427,2.886,0.4331;-1.2181,2.9255,-0.72;-1.6357,3.3026,-1.6482;0.1714,2.5494,-0.7266;0.9007,2.7754,-1.7895;2.2614,2.5233,-1.7145;3.0676,2.637,-2.9983;3.9126,1.7709,-3.2219;2.8149,3.6634,-3.8685;2.0742,4.8796,-3.5657;2.7583,5.7398,-3.5345;1.5552,4.7936,-2.6154;1.3267,5.0667,-4.3467;3.5562,3.6931,-5.1225;2.8708,3.9199,-5.948;4.0221,2.7219,-5.2802;4.3382,4.465,-5.0993;2.9381,2.1477,-0.5861;4.0181,2.0576,-0.5964;2.237,1.9628,0.6734;2.7695,1.8077,1.7632;0.7108,1.8593,0.5044;0.6337,0.7809,0.2278;-0.0964,2.0113,1.7493;0.3789,1.7095,2.677;-1.3648,2.4666,1.6938;-1.9669,2.543,2.5947)|",3.44496134733
"[H]/N=C(O[H])\C([H])=C([H])\C1=C(/C([H])([H])C([H])([H])[H])OC2=C([H])C([H])=C([H])C([H])=C21 |(4.8085,4.0382,-1.3327;4.3904,4.7233,-1.9638;3.8702,4.1234,-2.9608;3.3616,4.9095,-3.958;2.8359,4.3589,-4.558;3.7535,2.6654,-3.1992;3.7742,2.3379,-4.2363;3.5875,1.7741,-2.2015;3.5091,2.1751,-1.1914;3.4717,0.3348,-2.3123;3.0612,-0.491,-1.2896;2.6621,-0.245,0.1267;2.6083,0.8344,0.2988;1.6458,-0.6344,0.2766;3.6134,-0.8993,1.1466;3.2618,-0.714,2.1668;4.6265,-0.4941,1.0541;3.665,-1.9812,0.9924;3.0275,-1.8036,-1.6821;3.4338,-1.8396,-2.9928;3.5507,-2.9863,-3.765;3.3081,-3.9637,-3.3613;4.0003,-2.8105,-5.0733;4.1102,-3.6753,-5.721;4.3212,-1.5324,-5.561;4.6813,-1.4281,-6.5806;4.1937,-0.3963,-4.764;4.4712,0.5741,-5.1608;3.7288,-0.5438,-3.4481)|",4.478993979229999
"[H]OC(=N/C([H])([H])C(F)(F)F)/C([H])=C(\[H])C1=C([H])N(C([H])([H])[H])N=C1[H] |(6.3834,3.3863,0.7662;7.1378,3.522,0.1719;7.6293,2.2991,-0.1908;8.8342,2.3008,-0.6144;9.4297,1.1079,-1.1575;10.4902,1.0805,-0.8842;8.9814,0.1545,-0.8421;9.379,1.1055,-2.6802;10.0049,2.1641,-3.2195;9.9795,-0.0128,-3.1578;8.1065,1.1035,-3.134;6.7046,1.16,-0.0471;7.1518,0.1734,0.027;5.3595,1.282,-0.0405;4.9256,2.2718,-0.1836;4.3863,0.2195,0.1;4.5655,-1.1517,0.2867;5.4598,-1.7534,0.366;3.3356,-1.706,0.3669;2.9927,-3.1053,0.5474;3.9107,-3.6825,0.6724;2.4465,-3.4717,-0.3261;2.3648,-3.2189,1.4347;2.3415,-0.7924,0.2459;2.9722,0.3648,0.0859;2.401,1.2766,-0.0377)|",4.655867982055
"[H]OC(=N/C([H])([H])/C(=N\C([H])([H])C([H])([H])C([H])([H])[H])O[H])/C([H])=C(\[H])C1=C([H])C([H])=C([H])O1 |(0.8514,5.202,2.5653;1.0927,4.2768,2.4019;2.4549,4.1763,2.389;2.9371,3.1581,1.7915;4.3607,2.8737,1.8024;4.9369,3.6503,1.275;4.7697,2.8236,2.8218;4.6189,1.5309,1.102;5.7485,0.9706,0.9535;6.9402,1.6188,1.4824;7.0438,2.6557,1.1122;6.8976,1.6891,2.5834;8.1942,0.8311,1.0855;9.0657,1.2786,1.5838;8.0934,-0.1909,1.4725;8.4152,0.7874,-0.4282;9.286,0.1743,-0.6878;8.583,1.7942,-0.8334;7.5354,0.3667,-0.9248;3.5097,0.9375,0.6203;2.7671,1.5396,0.8578;3.2143,5.2248,3.0837;4.2499,5.3577,2.7882;2.7057,5.9792,4.0846;1.6876,5.8097,4.4312;3.4072,7.0054,4.8009;3.0308,7.8228,5.8415;2.0676,7.8209,6.3331;4.1498,8.6543,6.1377;4.2185,9.4189,6.8984;5.1301,8.2901,5.261;6.1429,8.6263,5.0979;4.701,7.2941,4.4432)|",3.804151629989999
"[H]/N=C(O[H])\C([H])=C(/[H])C1=C(N(=O)=O)C([H])=C2OC([H])([H])OC2=C1[H] |(-1.1052,2.3023,-1.4526;-1.7518,1.5502,-1.2064;-1.0994,0.4819,-0.9653;-1.8063,-0.6081,-0.5551;-1.2767,-1.4073,-0.7069;0.3719,0.3291,-1.0538;0.8783,1.0298,-1.7143;1.0681,-0.5978,-0.3724;0.5371,-1.2927,0.2674;2.5337,-0.7327,-0.4095;3.2119,-1.97,-0.3304;2.4849,-3.2439,-0.3021;3.1179,-4.2546,0.0048;1.2854,-3.2513,-0.5982;4.6172,-2.0803,-0.3117;5.0875,-3.0515,-0.2449;5.3294,-0.9098,-0.3936;6.6848,-0.7324,-0.3972;6.9037,0.6811,-0.4933;7.4501,1.0285,0.3896;7.4521,0.9064,-1.4139;5.6172,1.324,-0.5383;4.6902,0.329,-0.4815;3.3173,0.4444,-0.4784;2.8417,1.4174,-0.4906)|",3.779661383445001
"[H]OC(=N/C([H])([H])C([H])([H])[H])/C([H])=C(\[H])C1=C(OC([H])([H])[H])C([H])=C([H])C(C([H])([H])[H])=C1[H] |(3.2329,0.3707,-0.0542;3.3372,0.7487,-0.941;4.6411,1.1533,-1.0833;4.873,1.9937,-2.0102;6.2408,2.4063,-2.2991;6.6632,2.9719,-1.4512;6.9005,1.5378,-2.4517;6.2665,3.2887,-3.5466;7.2872,3.6198,-3.7719;5.6361,4.1725,-3.4027;5.8789,2.7399,-4.4112;5.6217,0.5493,-0.1595;6.5493,1.1,-0.0337;5.4404,-0.6347,0.4622;4.5445,-1.2084,0.2383;6.3609,-1.2832,1.3981;6.1789,-2.6548,1.7145;5.1382,-3.2818,1.0916;4.8991,-4.6515,1.3765;4.0262,-4.931,0.7842;4.6827,-4.81,2.441;5.7519,-5.277,1.0832;7.045,-3.2882,2.608;6.9201,-4.3374,2.8492;8.0889,-2.5719,3.196;8.7525,-3.0836,3.889;8.2932,-1.2172,2.9179;9.4022,-0.4347,3.5836;10.1037,-1.1006,4.0965;9.0086,0.2663,4.331;9.9722,0.1557,2.8568;7.4168,-0.6012,2.0203;7.5466,0.457,1.8071)|",4.231370375275
"[H]OC(=N/C([H])([H])[H])/C([H])=C(\[H])C1=C(OC([H])([H])[H])C([H])=C([H])C(C([H])([H])[H])=C1[H] |(6.2745,-3.6994,1.3403;6.2437,-4.5206,0.8254;4.9932,-4.6308,0.2701;4.6457,-5.7951,-0.1089;3.3762,-5.9779,-0.7926;3.3258,-7.0038,-1.1702;3.238,-5.3004,-1.6479;2.5178,-5.8404,-0.116;4.2105,-3.3839,0.1602;3.1367,-3.5112,0.0586;4.7587,-2.1509,0.1305;5.8423,-2.064,0.1382;4.0464,-0.8749,0.0385;4.7713,0.3041,-0.2756;6.1119,0.1471,-0.4818;6.8962,1.2927,-0.7769;7.9214,0.9328,-0.8794;6.8491,2.0328,0.0325;6.5815,1.7629,-1.7173;4.1086,1.5298,-0.3698;4.6516,2.434,-0.6183;2.7319,1.5984,-0.1495;2.2346,2.5622,-0.2292;1.9862,0.4608,0.1744;0.5018,0.5421,0.4474;0.0856,1.4941,0.1025;0.2843,0.4588,1.5204;-0.0452,-0.2636,-0.0556;2.6671,-0.7564,0.2623;2.1067,-1.6481,0.5319)|",4.225928098264999
"[H]O/C(=N/C([H])([H])C([H])([H])[H])C1=C([H])C(=O)[C@]2([H])C(=N1)C([H])=C([H])C([H])=C2[H] |(2.9869,1.3193,-0.4224;2.812,0.6331,-1.1001;4.0119,0.0549,-1.3635;4.0146,-1.0051,-2.0632;5.1927,-1.7821,-2.3928;5.6382,-1.393,-3.3221;5.9775,-1.7249,-1.6214;4.7985,-3.2445,-2.6117;5.6717,-3.8432,-2.8965;4.0449,-3.3226,-3.4015;4.3715,-3.6682,-1.6965;5.1627,0.8048,-0.7378;6.4404,0.7863,-1.2305;6.6838,0.3089,-2.1702;7.4987,1.5503,-0.5871;8.624,1.7031,-1.0376;7.1168,2.0816,0.8091;7.3229,1.1709,1.4181;5.6344,2.288,0.9985;4.7489,1.6042,0.3224;5.2102,3.2465,1.9823;4.1479,3.3095,2.1957;6.1112,4.0932,2.5553;5.7633,4.8397,3.2647;7.5181,4.0724,2.2127;8.1703,4.8288,2.6395;8.0032,3.1467,1.36;9.0461,3.1144,1.0619)|",3.44496134733
"[H]C1=C([H])C(C([H])([H])[H])=C([H])C(/C([H])=C(\C#N)C(=O)OC([H])([H])[H])=C1OC([H])([H])[H] |(3.2423,2.648,-1.1232;3.1839,1.7613,-0.5037;1.9912,1.4635,0.154;1.1426,2.1319,0.0274;1.8621,0.3331,0.9704;0.5553,0.0029,1.6541;-0.0586,0.8987,1.7955;0.7219,-0.4522,2.6358;-0.0358,-0.7092,1.0633;2.9759,-0.4931,1.1163;2.8912,-1.3676,1.7501;4.2033,-0.2347,0.4741;5.3847,-1.0656,0.5973;6.2423,-0.7307,0.0219;5.6308,-2.204,1.3078;4.6948,-2.8712,2.1549;3.9282,-3.4129,2.8449;6.999,-2.7961,1.184;7.8905,-2.3377,0.4976;7.1246,-3.9086,1.9333;8.4089,-4.5491,1.8772;8.3284,-5.4167,2.5319;9.1904,-3.8704,2.2289;8.6383,-4.8569,0.8536;4.2917,0.9236,-0.3543;5.4885,1.1473,-0.9584;5.636,2.2793,-1.8048;6.6609,2.2374,-2.1762;5.4866,3.2147,-1.2512;4.9388,2.2399,-2.651)|",3.8504109845750008
"[H]C1=C([H])C([H])=C([C@@]2([H])N([H])N([H])C([H])([H])[C@@]2([H])C([H])([H])N([H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(-0.5837,-3.4582,-2.872;0.1425,-3.6111,-2.0782;1.3238,-4.3112,-2.3314;1.5228,-4.7024,-3.3258;2.2542,-4.5118,-1.3114;3.1688,-5.0581,-1.5288;2.025,-4.017,-0.0173;3.0208,-4.2107,1.1156;2.5109,-3.9253,2.044;3.4759,-5.6279,1.244;3.148,-6.1653,0.4457;4.9154,-5.6892,1.2213;5.2075,-5.6881,2.1987;5.3736,-4.4395,0.5999;6.3992,-4.2215,0.9179;5.3763,-4.56,-0.4931;4.3474,-3.3715,1.0259;4.6,-3.0482,2.044;4.2958,-2.1109,0.1594;3.4125,-1.5231,0.4334;4.1617,-2.3899,-0.9028;5.4864,-1.2871,0.3798;6.3106,-1.8574,0.1936;5.5726,-0.0815,-0.4588;5.3528,-0.3192,-1.5192;4.5625,0.9755,0.0052;4.6674,1.8903,-0.5893;4.7337,1.2217,1.0594;3.5283,0.6321,-0.101;7.0012,0.4651,-0.3798;7.1098,1.3675,-0.9909;7.7307,-0.2714,-0.741;7.256,0.7155,0.6565;0.8321,-3.3226,0.2212;0.6298,-2.9447,1.2209;-0.1007,-3.1166,-0.7967;-1.0206,-2.578,-0.5847)|",5.670852644420001
"[H]C1=C([H])C([H])=C([C@@]2([H])N([H])N([H])C([H])([H])[C@@]2([H])C([H])([H])N([H])C([H])([H])C([H])([H])OC([H])([H])[H])C([H])=C1[H] |(6.9479,-4.4379,-1.4541;6.9171,-4.1644,-0.4028;8.081,-3.7589,0.2529;9.0229,-3.7119,-0.2876;8.043,-3.4135,1.6043;8.9604,-3.1026,2.0976;6.8412,-3.4623,2.3287;6.7627,-3.0819,3.7998;5.7883,-3.4269,4.1684;7.835,-3.7192,4.6218;8.4678,-4.2337,4.0152;8.6269,-2.7065,5.2835;8.2472,-2.6413,6.2281;8.353,-1.4402,4.5905;8.5297,-0.59,5.254;9.03,-1.3369,3.7314;6.8945,-1.5515,4.1165;6.2472,-1.3545,4.9848;6.4878,-0.5878,3.0013;5.4751,-0.8486,2.6426;7.1588,-0.7132,2.1423;6.6134,0.7946,3.4669;5.9492,0.955,4.2236;6.3817,1.8159,2.4492;6.6437,2.7881,2.885;7.0685,1.6409,1.6109;4.9524,1.9072,1.9076;4.66,0.9774,1.3911;4.8799,2.7271,1.1717;4.0911,2.1466,3.0083;2.7304,2.2164,2.6421;2.1566,2.3921,3.5562;2.3841,1.2792,2.1769;2.5394,3.0418,1.9373;5.6836,-3.8781,1.657;4.7462,-3.9393,2.2057;5.7163,-4.2239,0.3053;4.8051,-4.5478,-0.1911)|",5.483094087575
"[H]OC(=N/C([H])([H])C([H])([H])[H])/C1=N/C(=O)[C@@]2([H])C([H])=C([H])C([H])=C([H])\C2=C\1[H] |(9.5328,1.5827,-0.6802;9.1676,2.2467,-0.054;7.9817,1.7463,0.3581;7.2182,2.4996,1.0391;5.8749,2.171,1.477;5.368,1.4372,0.8323;5.92,1.7329,2.4871;5.0345,3.4496,1.5378;4.0248,3.2326,1.9061;4.9517,3.9014,0.5438;5.5035,4.1821,2.2017;7.7798,0.3126,-0.1034;8.5389,-0.0094,-1.1311;8.5167,-1.3039,-1.645;9.3214,-1.6864,-2.4715;7.3479,-2.2116,-1.2027;6.556,-1.8717,-1.9078;7.5388,-3.6656,-1.4931;8.1198,-3.9136,-2.3748;6.9686,-4.6044,-0.7107;7.077,-5.6597,-0.9436;6.223,-4.2255,0.4719;5.7537,-5.0084,1.0618;6.1451,-2.9311,0.8931;5.6413,-2.679,1.8224;6.7994,-1.8847,0.1615;6.9402,-0.5997,0.6169;6.5159,-0.305,1.5678)|",3.13475155776
"[H]C1=C([H])C([H])=C2OC(/C([H])=C(\[H])C(=O)N(OC([H])([H])[H])C([H])([H])[H])=N\C2=C1[H] |(12.4706,0.9697,-0.2074;11.4772,0.7383,-0.5805;11.324,0.3767,-1.9334;12.2007,0.3367,-2.5733;10.0741,0.0672,-2.4745;9.9462,-0.2126,-3.5146;9.0048,0.1399,-1.5941;7.6794,-0.1047,-1.837;7.0667,0.1164,-0.6164;5.6377,-0.0582,-0.5219;5.2069,0.1289,0.4569;4.8438,-0.4279,-1.5426;5.2479,-0.6344,-2.5251;3.3843,-0.5759,-1.305;2.8637,-0.4071,-0.206;2.6027,-0.8564,-2.4172;3.2596,-1.3787,-3.5506;3.1902,-0.4447,-4.6328;2.1509,-0.2108,-4.8929;3.6739,-0.9471,-5.4742;3.7235,0.4821,-4.3913;1.2465,-1.3585,-2.27;0.8446,-0.9482,-1.3441;1.2383,-2.4545,-2.2223;0.6363,-1.0287,-3.1157;7.8707,0.4709,0.3461;9.129,0.4975,-0.2418;10.388,0.8051,0.2854;10.4997,1.0832,1.3283)|",4.14157280461
"[H]OC(=N/C([H])([H])C([H])([H])[H])/C([H])=C([H])/C1=N/C2=C([H])C([H])=C([H])C([H])=C2O1 |(4.2199,1.3781,1.8044;3.3508,0.9478,1.8252;3.4199,-0.2131,1.0939;2.5532,-1.1015,1.3682;2.4314,-2.3355,0.6164;1.4625,-2.3032,0.0987;3.1936,-2.4735,-0.1663;2.4443,-3.5341,1.5698;2.2738,-4.4657,1.0185;1.6636,-3.426,2.3291;3.4081,-3.6107,2.0853;4.4718,-0.2572,0.0577;4.8138,-1.2307,-0.2782;5.0172,0.8397,-0.5046;4.6788,1.8401,-0.2476;6.0515,0.7731,-1.5145;6.6451,-0.2751,-2.0079;7.5468,0.2387,-2.9315;8.4639,-0.3944,-3.7776;8.5582,-1.4753,-3.7851;9.2392,0.4182,-4.6015;9.9624,-0.0375,-5.2716;9.1114,1.8213,-4.5891;9.7368,2.4162,-5.2483;8.2009,2.4679,-3.7507;8.0934,3.5471,-3.7339;7.4393,1.6391,-2.9401;6.4768,1.9836,-2.0272)|",4.040890679925
"[H]OC(=N/[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[H])/C([H])=C(\[H])C1=C([H])N(C([H])([H])[H])N=C1[H] |(7.1767,2.0476,1.0906;6.5019,1.5818,0.5724;5.268,1.9177,1.0822;4.3266,1.0949,0.8501;2.948,1.3668,1.2336;2.7818,2.424,1.5029;2.5912,0.492,2.4451;1.5409,0.6288,2.7283;3.2161,0.7424,3.3101;2.7549,-0.5651,2.209;2.0303,1.0525,0.0361;2.1621,-0.0048,-0.2275;0.9877,1.1764,0.3604;2.2997,1.929,-1.189;1.6263,1.6718,-2.0146;3.3295,1.8004,-1.5376;2.152,2.9921,-0.9583;5.2081,3.2001,1.8145;4.4058,3.3073,2.5379;6.0674,4.2218,1.6125;6.8292,4.1147,0.84;6.0651,5.4958,2.3035;6.9081,6.5748,2.0472;7.6994,6.6973,1.3205;6.5762,7.5618,2.9113;7.1631,8.8826,3.038;7.9584,8.9885,2.2975;7.579,9.0124,4.041;6.4012,9.6479,2.8664;5.5598,7.2044,3.7343;5.2505,5.9653,3.3721;4.4556,5.4437,3.8891)|",4.71845416767
"[H]C([H])([H])/C1=N/C(=O)[C@@]2([H])C(N1)SC1=C2C([H])([H])C([H])([H])C([H])([H])S1 |(0.7199,-0.4564,-2.1366;1.0815,-0.3097,-1.1121;0.6307,0.5852,-0.682;0.7783,-1.1975,-0.5438;2.5754,-0.198,-1.1165;3.1365,0.903,-0.7119;4.5372,0.9744,-0.6921;5.1438,2.0241,-0.7156;5.227,-0.3935,-0.5366;4.9827,-0.6799,0.5062;4.4925,-1.3689,-1.424;3.23,-1.3218,-1.6601;5.5902,-2.5031,-2.193;7.0062,-1.6035,-1.5432;6.7049,-0.5508,-0.759;7.7104,0.3384,-0.0738;7.6877,1.327,-0.55;7.39,0.5142,0.9622;9.1346,-0.2371,-0.0717;9.2074,-1.0645,0.6438;9.8343,0.5391,0.2624;9.5771,-0.731,-1.4511;10.6143,-1.0781,-1.4385;9.4882,0.0594,-2.2032;8.602,-2.187,-2.0218)|",3.4993841174300004
"[H]C([H])([H])C1=NC(=O)[C@]2([H])C(N1)SC(C([H])([H])[H])=C2C([H])([H])C([H])([H])[H] |(8.0509,1.5661,-1.2382;8.0257,0.6325,-0.675;8.4587,-0.1762,-1.2758;8.6422,0.7158,0.228;6.6184,0.2811,-0.2986;5.6377,1.007,-0.7506;4.3286,0.6859,-0.3671;3.3565,1.0411,-1.0008;4.2362,-0.0599,0.977;4.5093,0.724,1.7104;5.3584,-1.0668,1.0176;6.5097,-0.8932,0.4681;4.8636,-2.5375,1.8234;3.1557,-1.9612,1.9302;2.1843,-2.9238,2.544;1.1778,-2.5009,2.5799;2.4786,-3.183,3.5688;2.1373,-3.8592,1.9724;2.9603,-0.7251,1.4323;1.6524,0.0238,1.3877;1.4423,0.2765,0.3426;0.8414,-0.6231,1.7365;1.6594,1.3245,2.2114;0.6766,1.8056,2.1664;2.3894,2.0403,1.8176;1.8959,1.1336,3.2646)|",3.883064646635
"[H]C1=C([H])C(/C([H])=C(\[H])C(=O)N(OC([H])([H])[H])C([H])([H])[H])=C([H])C2=C1OC([H])([H])O2 |(7.0347,-4.0374,-1.4413;7.3177,-2.9929,-1.3682;6.3597,-1.9754,-1.2762;5.3107,-2.2527,-1.2765;6.7153,-0.6172,-1.1788;5.7305,0.4587,-1.0824;6.1335,1.4647,-0.9732;4.3873,0.3612,-1.1139;3.8723,-0.5861,-1.2099;3.5756,1.593,-0.9856;4.0567,2.7123,-0.8224;2.1954,1.4509,-1.1275;1.6674,0.1517,-0.9492;1.1006,-0.3138,-2.1758;0.3026,0.3519,-2.5268;0.6793,-1.2955,-1.9431;1.8648,-0.4092,-2.9563;1.296,2.4806,-0.6328;1.7999,3.4392,-0.7507;1.0557,2.319,0.426;0.371,2.4744,-1.2168;8.0885,-0.2587,-1.1695;8.393,0.7798,-1.093;9.0188,-1.2695,-1.2616;8.6467,-2.6078,-1.3595;9.7627,-3.3985,-1.4369;10.8862,-2.5072,-1.3754;11.4813,-2.6105,-2.2901;11.488,-2.7432,-0.4897;10.3896,-1.1684,-1.2753)|",4.095313450025
"[H]OC(=N/C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])/C([H])=C(\[H])C1=C(C([H])([H])[H])N(C([H])([H])[H])N=C1C([H])([H])[H] |(4.2133,-2.5445,-0.1309;4.1414,-2.7391,-1.0786;2.8596,-3.1969,-1.3177;2.7273,-3.8939,-2.3707;1.467,-4.4132,-2.9221;0.9325,-5.5778,-2.0606;0.0787,-6.0574,-2.5536;0.6009,-5.2493,-1.0702;1.7145,-6.3318,-1.9204;0.3921,-3.3215,-3.1125;-0.4668,-3.7284,-3.659;0.8003,-2.4885,-3.6957;0.0259,-2.9197,-2.1636;1.8382,-4.9713,-4.3124;0.9656,-5.4112,-4.8104;2.6134,-5.7384,-4.2173;2.2367,-4.1724,-4.9466;1.8593,-2.7701,-0.3118;0.9935,-3.4065,-0.1895;1.9758,-1.6279,0.4048;2.8381,-0.9969,0.1891;1.0771,-1.1003,1.409;1.213,0.1478,2.0312;2.2154,1.2424,1.8478;2.9775,0.9531,1.1207;2.7283,1.4868,2.7868;1.7465,2.1647,1.4813;0.189,0.2487,2.915;-0.134,1.3623,3.7857;0.7246,1.6313,4.4098;-0.9581,1.0416,4.4227;-0.4436,2.2412,3.2084;-0.6133,-0.8504,2.9152;-0.09,-1.672,2.0131;-0.7372,-3.0002,1.7694;-1.1268,-3.0817,0.7468;-1.5728,-3.1286,2.4624;-0.0323,-3.8274,1.9183)|",4.6803582286
"[H]C1=C([H])C(F)=C(/C([H])=C(\C#N)C(=C(C#N)C#N)N([H])[H])C([H])=C1[H] |(-3.512,-1.0873,0.6931;-2.7871,-0.3023,0.499;-1.9062,0.0836,1.5084;-1.909,-0.387,2.4856;-0.9864,1.0913,1.253;-0.1052,1.4052,2.2195;-0.9108,1.7457,0.0124;0.0287,2.8085,-0.3272;0.3434,2.79,-1.3649;0.5127,3.8326,0.4258;0.1359,4.0612,1.7904;-0.1215,4.3416,2.8903;1.471,4.8353,-0.1176;1.4916,5.2616,-1.4466;2.4998,6.1903,-1.8347;3.3521,6.9519,-2.0659;0.5334,4.8776,-2.4231;-0.2403,4.5598,-3.2355;2.3505,5.3417,0.7709;2.3025,5.0885,1.7474;2.9682,6.0985,0.5054;-1.7866,1.2991,-1.0019;-1.7313,1.7738,-1.9779;-2.7252,0.3036,-0.7602;-3.4018,-0.0052,-1.5505)|",3.681700397265
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"[H]OC(NC1=C([H])C([H])=C([H])N=C1[H])N1C([H])([H])C([H])([H])C([H])([H])[C@@]1([H])C([H])([H])O[H] |(-1.459,-0.2529,0.3918;-2.395,0.0107,0.4465;-2.9746,-0.6073,-0.6223;-2.1829,-1.2418,-1.4174;-2.5089,-2.1847,-2.3849;-3.2683,-3.3415,-2.1322;-3.6906,-3.503,-1.1434;-3.4494,-4.2738,-3.1513;-4.032,-5.1752,-2.981;-2.8658,-4.0371,-4.396;-2.9914,-4.7452,-5.2131;-2.1213,-2.9538,-4.657;-1.9477,-2.0692,-3.6743;-1.3229,-1.204,-3.893;-4.3169,-0.3762,-0.6488;-5.0132,0.3021,0.4676;-5.099,-0.3822,1.3223;-4.4524,1.1776,0.8005;-6.388,0.6544,-0.1157;-7.1694,0.6543,0.65;-6.3653,1.6552,-0.5616;-6.6076,-0.4122,-1.1997;-7.3276,-0.1219,-1.9681;-6.9559,-1.3471,-0.7447;-5.207,-0.624,-1.7995;-5.0913,-1.643,-2.1742;-4.9011,0.3322,-2.9609;-3.8634,0.1824,-3.2869;-5.0008,1.3772,-2.6236;-5.824,0.0292,-3.9995;-5.5015,0.431,-4.8192)|",5.3334314698
"[H]C1C2=C(/N=C(/C([H])([H])OC([H])([H])C([H])([H])C([H])([H])[H])NC2=O)C([H])=C([H])[C@@]1([H])F |(11.6119,0.7938,3.2168;11.0033,1.5727,2.7637;9.6593,1.4942,2.8133;8.8263,2.569,2.2689;7.5257,2.5236,2.306;6.9564,1.3742,2.8703;5.4345,1.3711,2.8716;5.1025,2.0089,3.7015;5.0976,0.3447,3.0737;4.8468,1.9072,1.7118;4.9915,1.0837,0.5593;6.0584,0.9404,0.3206;4.5595,0.0885,0.7565;4.2801,1.7596,-0.6063;4.3084,1.0747,-1.4644;3.2235,1.8873,-0.3385;4.8926,3.1114,-0.9857;4.3443,3.5745,-1.8136;5.9379,2.9953,-1.2986;4.874,3.7955,-0.1326;7.5513,0.3571,3.4177;8.9544,0.3493,3.4754;9.568,-0.5521,4.0207;9.4871,3.7529,1.7238;8.8412,4.5645,1.4039;10.8251,3.8285,1.6563;11.3309,4.71,1.2721;11.7065,2.6772,2.0409;12.134,2.2477,1.1162;12.7918,3.1304,2.7954)|",3.229991405435
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"[H]C1S[C@]2([H])C(=O)N/C(C([H])([H])OC([H])([H])C([H])([H])C([H])([H])[H])=N\C2=C1[H] |(10.0294,2.404,-4.1279;10.6144,2.6767,-3.2561;11.5108,4.1747,-3.3064;12.0091,3.9315,-1.5594;11.409,4.633,-0.9581;13.464,4.1964,-1.1685;14.2167,4.8696,-1.8377;13.8001,3.6668,0.0831;13.093,2.677,0.5549;13.4694,2.1391,1.9159;14.1964,2.8198,2.3797;13.9599,1.1592,1.7724;12.3006,2.0031,2.6927;12.5395,1.372,3.9415;12.9698,0.3681,3.7793;13.2756,1.9504,4.5269;11.2212,1.2709,4.6975;10.8075,2.2803,4.8129;10.5094,0.7084,4.0811;11.3848,0.6035,6.0665;10.4253,0.5442,6.591;12.0776,1.1641,6.706;11.7736,-0.4174,5.9693;12.0851,1.9638,-0.0898;11.6088,2.5259,-1.16;10.7335,1.928,-2.1273;10.2769,0.9567,-1.9812)|",3.744286582880001
"[H]C([H])([H])/C1=C(\C([H])([H])C([H])([H])[H])[C@@]2([H])C(NC(C([H])([H])C([H])([H])[H])=NC2=O)S1 |(0.9415,-3.2141,2.1753;1.7355,-3.8204,1.7331;2.0819,-4.5295,2.4958;1.2963,-4.4092,0.9178;2.8604,-2.963,1.2362;2.9984,-1.6229,1.2352;2.0091,-0.6212,1.7747;1.7423,0.0612,0.96;1.0929,-1.1326,2.0866;2.5527,0.213,2.9493;1.7791,0.8964,3.3154;3.4075,0.8256,2.6414;2.8666,-0.4236,3.7847;4.3319,-1.1641,0.6972;4.9558,-0.8149,1.5433;5.0594,-2.3475,0.1101;6.0993,-2.259,-0.6436;6.4571,-0.9462,-0.9987;7.8242,-0.8259,-1.6162;7.8272,-1.4276,-2.5356;7.9813,0.2187,-1.8959;8.9409,-1.3319,-0.6871;9.9078,-1.2833,-1.1987;8.7594,-2.3698,-0.3923;9.009,-0.7216,0.2205;5.7049,0.1125,-0.9088;4.4452,0,-0.306;3.555,0.803,-0.4969;4.2506,-3.8475,0.5006)|",3.85857440009
"[H]O/C(=N/C([H])([H])C([H])([H])[H])[C@]1([H])C(=O)N=C(C(F)(F)F)C([H])=C1[H] |(3.9055,0.9329,2.6399;3.4628,0.0559,2.6265;4.01,-0.6847,1.6422;3.888,-1.9428,1.6956;4.4072,-2.8586,0.7001;3.5773,-3.1779,0.0514;5.1732,-2.4226,0.0337;4.9965,-4.0924,1.3894;5.3343,-4.8269,0.6492;4.2454,-4.5589,2.0337;5.8511,-3.8139,2.0151;4.6666,0.1015,0.4716;5.628,-0.4046,0.2638;5.0408,1.5588,0.7409;5.0494,2.0555,1.8552;5.4181,2.3443,-0.3647;4.8602,2.0676,-1.4921;5.2246,2.9341,-2.6936;5.8484,2.1752,-3.6219;4.0956,3.4171,-3.2581;6.016,3.955,-2.3781;3.9353,0.9641,-1.7345;3.3905,0.916,-2.6705;3.8561,0.0023,-0.8004;3.2452,-0.8829,-0.9479)|",3.60278738062
"[H]OC1=NC([H])([H])C([H])([H])C([H])([H])[C@@]1([H])/C(=N\C([H])([H])C1=C([H])C([H])=NC([H])=C1[H])O[H] |(1.5306,1.1809,1.3855;2.1407,1.6082,2.0088;2.9295,0.667,2.6067;3.7068,1.0623,3.5241;4.631,0.1258,4.1565;5.5766,0.6584,4.3127;4.2475,-0.1004,5.1629;4.8737,-1.1738,3.3807;5.494,-0.9665,2.498;5.4294,-1.8848,4.003;3.5383,-1.7747,2.9407;2.9372,-2.0225,3.8256;3.6721,-2.7075,2.3828;2.7707,-0.759,2.0622;3.2052,-0.7608,1.0559;1.2889,-1.1225,1.9566;0.631,-1.563,0.9673;1.2213,-1.8734,-0.3148;0.7548,-1.2141,-1.0626;2.3064,-1.6917,-0.3834;0.9525,-3.3121,-0.7334;-0.0182,-4.1015,-0.1138;-0.5966,-3.7028,0.7121;-0.2207,-5.4039,-0.5738;-0.9723,-6.0352,-0.1018;0.4596,-5.9596,-1.5839;1.3875,-5.1933,-2.1725;1.9385,-5.6536,-2.9916;1.67,-3.8832,-1.7899;2.4423,-3.3203,-2.3097;0.5834,-0.9528,3.1142;1.1295,-0.4901,3.7711)|",6.20963806841
"[H]OC([H])([H])C([H])([H])OC(=O)[C@]1([H])N=NC(=O)C2=C1C([H])=C([H])C([H])=C2[H] |(-0.5039,2.1732,3.6336;-0.0377,2.9971,3.4148;-0.9911,4.0323,3.2872;-1.3153,4.428,4.2656;-0.4852,4.8453,2.7556;-2.2465,3.6202,2.5261;-2.8781,4.4881,2.3212;-2.8235,2.8717,3.0761;-1.9114,3.079,1.2232;-1.6669,1.7633,1.1727;-1.724,0.9937,2.1077;-1.3167,1.3173,-0.2554;-1.401,2.1782,-0.9203;-2.4305,0.3799,-0.6626;-2.332,-0.8346,-0.4281;-1.1195,-1.4051,0.2094;-1.2336,-2.479,0.753;0.1366,-0.6654,0.0053;0.0513,0.7018,-0.2842;1.2153,1.4302,-0.539;1.1564,2.493,-0.7575;2.4513,0.7851,-0.5029;3.3568,1.3506,-0.7041;2.5356,-0.5818,-0.2072;3.5052,-1.0698,-0.1731;1.3788,-1.3115,0.0447;1.4127,-2.3718,0.2746)|",3.578297134075
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"[H]OC(=O)[C@]1([H])N([H])C([H])([H])C([H])([H])[C@@]([H])(C(F)(F)F)C1([H])[H] |(-1.8414,-0.6545,2.5413;-0.994,-0.6902,3.0149;0.0335,-0.8594,2.1409;1.163,-0.9544,2.5529;-0.3372,-0.9966,0.6411;-0.8563,-1.9647,0.5636;0.8316,-1.0561,-0.2252;1.4664,-1.7633,0.1389;1.5588,0.2187,-0.33;1.9619,0.5543,0.6389;2.4094,0.0567,-1.0006;0.6449,1.3004,-0.9066;0.3436,1.0253,-1.9241;1.1801,2.2539,-0.9606;-0.611,1.4471,-0.0303;-0.315,1.817,0.961;-1.5656,2.4894,-0.5813;-0.9701,3.6971,-0.6892;-2.0308,2.1584,-1.809;-2.6481,2.6475,0.2199;-1.3087,0.0856,0.1238;-1.6747,-0.2403,-0.8551;-2.1894,0.1872,0.7695)|",6.41644459479
"[H]OC(=O)[C@@]1([H])N([H])[C@@]2([H])[C@@]([H])(OC1([H])[H])[C@]2([H])C(F)(F)F |(1.1342,2.9726,-0.8222;0.3996,3.6185,-0.7306;-0.699,2.9259,-0.3667;-1.762,3.4544,-0.1554;-0.4706,1.4146,-0.2814;-0.5969,1.0262,-1.3023;0.9305,1.1597,0.1201;1.0833,1.656,1.0012;1.174,-0.2442,0.4154;2.0689,-0.3887,1.0132;0.0196,-1.1853,0.6685;0.1079,-1.9436,1.4437;-1.2917,-0.728,0.4855;-1.4658,0.6862,0.6188;-2.4939,0.8947,0.321;-1.3326,1.0044,1.6645;0.8729,-1.3307,-0.5845;0.3899,-1.0661,-1.5198;1.828,-2.4729,-0.7228;2.3697,-2.81,0.4757;1.2209,-3.5728,-1.2143;2.8486,-2.1716,-1.555)|",6.8001251239950005
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"[H]C1=C([H])C(C(F)(F)F)=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])C2([H])[H])C([H])=C1[H] |(-1.2564,-2.2443,0.5704;-0.7033,-1.3343,0.3575;0.6863,-1.3684,0.2711;1.2165,-2.3016,0.4204;1.4128,-0.2061,-0.0074;2.9179,-0.3047,-0.0738;3.3614,-1.5503,0.1965;3.4045,0.0274,-1.293;3.5203,0.5238,0.8203;0.7525,1.0246,-0.2039;1.4828,2.3196,-0.5094;2.544,2.1188,-0.6674;0.9914,2.9781,-1.7428;-0.0263,2.9204,-1.7608;1.3046,4.3816,-1.6135;2.0916,4.5834,-2.2277;1.7372,4.6888,-0.2258;1.1908,5.5637,0.1471;3.1534,5.053,-0.2256;3.7393,4.2196,-0.2863;3.3998,5.5272,0.6414;1.3514,3.419,0.567;2.0004,3.2592,1.4339;0.3158,3.4784,0.9233;-0.645,1.0342,-0.0961;-1.1829,1.9706,-0.2197;-1.3702,-0.1247,0.1749;-2.4533,-0.0784,0.2466)|",4.94158752508
"[H]C1=C([H])N2C(=S)N=NC(=O)C2=C1[H] |(-3.5132,-0.0985,-0.0674;-2.4339,-0.0544,-0.0281;-1.5956,-1.1401,0.0239;-1.7892,-2.2012,0.0355;-0.2901,-0.6719,0.0625;0.8897,-1.3825,0.1198;1.0124,-3.0159,0.1568;2.1331,-0.6218,0.1494;2.1678,0.6306,0.1283;0.933,1.4454,0.0694;1.0658,2.6511,0.0539;-0.3161,0.7299,0.0355;-1.6344,1.1262,-0.0211;-1.9726,2.1524,-0.054)|",2.533379948155
"[H]OC1=NC(=O)N(C([H])([H])C([H])([H])[H])[C@@]([H])(N([H])[H])[C@@]1([H])N([H])C([H])([H])C([H])([H])[H] |(3.586,-1.1138,-1.0503;4.4477,-0.7821,-1.4221;5.066,-0.2676,-0.3637;6.1804,0.3503,-0.4822;6.7762,0.8829,0.6822;7.9651,1.1596,0.7124;5.9588,1.1265,1.7879;6.5352,1.8384,2.932;7.2842,2.5341,2.5474;5.7356,2.4305,3.3963;7.1781,0.904,3.9593;7.5841,1.4791,4.7999;7.9973,0.3501,3.4924;6.4534,0.1848,4.3603;4.5335,0.8207,1.7858;4.2282,0.6297,2.8199;3.6419,1.8666,1.2735;4.0255,2.2507,0.4095;3.5849,2.6389,1.9339;4.3306,-0.4679,0.964;4.8211,-1.2948,1.4921;2.9378,-0.8255,0.6975;2.4122,0.0528,0.674;2.3201,-1.7446,1.6666;2.9076,-2.6706,1.6687;2.3522,-1.343,2.6936;0.8761,-2.0454,1.2762;0.4264,-2.7516,1.9819;0.8284,-2.4791,0.272;0.266,-1.1341,1.2823)|",5.864053478275
"[H]C([H])([H])OC(=O)[C@]1([H])N=NC(=O)C2=NOC(C([H])([H])[H])=C21 |(0.8031,-4.8397,2.5541;1.7472,-4.2985,2.6052;1.6872,-3.4747,3.3203;2.5635,-4.9657,2.8908;1.9676,-3.7876,1.2719;3.0879,-3.0706,1.1132;3.892,-2.821,1.9798;3.2351,-2.5785,-0.3387;2.4322,-2.999,-0.9456;4.5086,-3.2454,-0.8451;5.6106,-2.6845,-0.7557;5.7926,-1.309,-0.1969;6.8872,-1.0044,0.1964;4.5861,-0.474,-0.2752;4.5204,0.8392,-0.3334;3.1586,1.1005,-0.4788;2.4458,-0.0534,-0.5035;0.969,0.0649,-0.6431;0.5128,-0.9276,-0.683;0.7052,0.6094,-1.5563;0.5391,0.6098,0.205;3.3185,-1.0976,-0.3862)|",3.534758917995
"[H]C1=C([H])C(/C([H])=C(\[H])C2=C(N([H])[H])C(C([H])([H])[H])=NO2)=C([H])C([H])=C1F |(9.184,-6.3204,0.3974;8.8869,-5.2782,0.4501;7.564,-4.8856,0.2629;6.8099,-5.6412,0.0586;7.1756,-3.5333,0.3324;5.7695,-3.1944,0.1234;5.1181,-4.0332,-0.1137;5.2044,-1.9676,0.1923;5.8116,-1.0956,0.4281;3.8112,-1.6915,-0.0289;3.081,-0.5237,0.0143;3.5028,0.7926,0.2246;2.8252,1.3548,0.7286;4.409,0.8667,0.6731;1.7487,-0.9297,-0.3081;0.5314,-0.0661,-0.4125;0.2658,0.373,0.5582;-0.318,-0.6586,-0.7604;0.697,0.7589,-1.1154;1.6791,-2.2263,-0.5206;2.9692,-2.7169,-0.3544;8.1786,-2.5781,0.5971;7.9247,-1.5237,0.6497;9.5036,-2.9524,0.7865;10.2776,-2.2189,0.9878;9.8402,-4.3015,0.7102;11.1261,-4.6657,0.8932)|",3.736123167365
"[H]OC1=N[C@]([H])(C2=NC([H])=C([H])C([H])=N2)N([H])O1 |(2.8592,3.4722,-3.1411;2.8667,3.9,-2.2677;2.8041,2.9184,-1.3703;2.8573,1.6635,-1.5879;2.6009,1.0676,-0.2836;3.3077,0.2686,-0.0416;1.1966,0.4819,-0.1928;1.1009,-0.7619,0.291;-0.1373,-1.2639,0.3874;-0.2188,-2.2777,0.7764;-1.2656,-0.5397,0.0152;-2.2651,-0.9534,0.0983;-1.0405,0.7482,-0.4715;-1.8671,1.3857,-0.7819;0.1847,1.2654,-0.5852;2.7595,2.1754,0.7225;3.7465,2.1928,0.9964;2.6685,3.3786,-0.1034)|",5.439555871495
"[H]OC(=O)C([H])([H])[C@@]1([H])C(C([H])([H])[H])N=C(N2C([H])([H])C([H])([H])C([H])([H])C2([H])[H])NC1=O |(5.3725,2.3804,-1.0174;4.7796,3.1623,-1.2186;3.5384,2.75,-1.4822;2.6547,3.5136,-1.8048;3.2754,1.2404,-1.3603;2.1974,1.1236,-1.4707;3.751,0.7289,-2.2062;3.7727,0.592,-0.0273;3.7519,1.375,0.7487;2.8524,-0.4996,0.4787;1.3836,-0.2074,0.6202;1.2136,0.811,0.987;0.9273,-0.9311,1.2982;0.8787,-0.2848,-0.3508;3.2697,-1.6631,0.8379;4.6494,-1.9197,0.7169;5.0015,-3.1459,1.1215;6.4014,-3.6049,1.1034;6.7242,-3.7536,0.0655;7.0501,-2.841,1.5375;6.3532,-4.9194,1.8942;7.1409,-5.6142,1.5904;6.48,-4.7176,2.9645;4.9358,-5.4499,1.6204;4.5943,-6.1784,2.3611;4.8962,-5.9296,0.6351;4.0726,-4.1796,1.6187;3.7293,-3.9197,2.6278;3.1938,-4.2429,0.9756;5.5961,-1.1127,0.2631;5.2349,0.1314,-0.1412;6.0642,0.9202,-0.6153)|",4.438176901655
"[H]OC(=O)C([H])([H])C([H])([H])C1=C(C([H])([H])[H])C2=C(N=NC2=O)N=C1O[H] |(2.2015,-3.0012,0.2231;1.3591,-3.4934,0.2701;0.3502,-2.6643,0.6394;-0.7705,-3.0812,0.7908;0.7593,-1.2062,0.835;-0.1156,-0.6628,1.1957;1.5165,-1.1727,1.6281;1.3233,-0.5557,-0.4697;0.5437,0.0529,-0.9322;1.5627,-1.3414,-1.1952;2.5656,0.2729,-0.2418;2.5896,1.6818,-0.2766;1.3632,2.5132,-0.5508;0.9775,2.3213,-1.5594;1.5934,3.5769,-0.471;0.5587,2.2759,0.1544;3.8354,2.2784,-0.0454;4.9465,1.4905,0.2002;6.1369,2.3188,0.415;5.8354,3.5277,0.3216;4.3406,3.6601,0.0186;3.8126,4.7314,-0.116;4.9724,0.1731,0.2477;3.7814,-0.394,0.028;3.7595,-1.754,0.0755;4.6738,-2.0423,0.2579)|",3.159241804305
"[H]OC(=O)C([H])([H])C1=C(C([H])([H])[H])C2=C(N=NC2=O)N=C1O[H] |(4.1407,-3.0884,1.4104;3.3347,-3.273,1.9309;2.2352,-2.8947,1.2445;1.132,-2.9963,1.7194;2.497,-2.312,-0.1559;3.1493,-2.9924,-0.7151;1.5345,-2.2709,-0.6653;3.1303,-0.9384,-0.0895;2.3699,0.2484,-0.1468;0.8694,0.2397,-0.2696;0.5625,-0.1781,-1.2372;0.4684,1.2517,-0.1948;0.4201,-0.3873,0.5081;3.1008,1.4394,-0.0791;4.4791,1.4022,0.0495;5.0353,2.7572,0.0963;4.1144,3.5977,0.0112;2.7709,2.8746,-0.1075;1.7333,3.4731,-0.1989;5.2257,0.3187,0.1341;4.5258,-0.8189,0.0726;5.2445,-1.9696,0.1926;6.1784,-1.7084,0.3011)|",3.1565206658
"[H]OC(=O)/C1=C(\[H])C(=C([H])[H])N([H])C2=C1C(=O)N=N2 |(6.4686,2.2885,0.6112;5.9269,3.0978,0.4651;4.6319,2.8776,0.1984;3.8715,3.8005,0.0046;4.1468,1.4459,0.1456;2.8303,1.2323,-0.1202;2.191,2.0931,-0.2813;2.2126,-0.0885,-0.2059;0.9098,-0.319,-0.468;0.2333,0.5096,-0.6341;0.499,-1.3221,-0.5213;3.1079,-1.1766,0.0138;2.7645,-2.1307,-0.0284;4.3913,-0.9565,0.2717;4.9776,0.2725,0.3537;6.3523,-0.016,0.649;7.3278,0.6874,0.8288;6.4611,-1.5558,0.7203;5.3391,-2.0506,0.5028)|",2.0082002166900006
"[H]OC1=NC(=S)N(C2([H])C([H])([H])C2([H])[H])C([H])([H])C1([H])[H] |(-3.8123,-1.3742,-5.0998;-3.425,-2.1416,-4.6501;-2.8003,-1.7402,-3.5226;-2.2056,-2.6398,-2.8368;-1.5991,-2.3243,-1.6135;-1.0343,-3.5679,-0.6594;-1.5057,-0.9927,-1.2539;-0.7803,-0.6157,-0.0635;0.2948,-0.7855,-0.123;-1.4132,-0.811,1.2829;-2.4121,-1.234,1.2989;-0.7705,-1.1426,2.0922;-1.2544,0.5819,0.7273;-0.499,1.2408,1.1464;-2.157,1.0911,0.3989;-1.6313,0.0219,-2.304;-1.7112,1.0018,-1.8317;-0.7329,0.0306,-2.9418;-2.8626,-0.2789,-3.1525;-2.8809,0.3618,-4.0431;-3.7834,-0.0876,-2.5838)|",3.708911782315001
"[H]C([H])=C([H])/C1=N/C(=O)[C@@]2([H])C(N1)SC1=C2C([H])([H])C([H])([H])C([H])([H])C1([H])[H] |(3.1214,2.5451,0.965;3.7001,1.7263,0.5479;4.7347,1.9203,0.2847;3.1577,0.5147,0.3693;2.1281,0.3019,0.6415;3.8838,-0.6231,-0.2016;3.2596,-1.7714,-0.2571;3.9078,-2.8625,-0.8418;3.5814,-4.0135,-0.6353;5.0029,-2.4753,-1.854;4.4226,-2.091,-2.7161;5.7553,-1.2932,-1.2905;5.2212,-0.3779,-0.5635;7.4712,-1.3889,-1.6521;7.2696,-3.043,-2.3301;6.0003,-3.483,-2.3475;5.6101,-4.829,-2.9016;5.3025,-5.4769,-2.0704;4.7153,-4.7268,-3.5305;6.7583,-5.4616,-3.7066;6.5462,-6.5222,-3.8838;6.8186,-4.9822,-4.6937;8.106,-5.2991,-2.9891;8.9036,-5.8042,-3.5458;8.052,-5.7781,-2.0026;8.4691,-3.8128,-2.8071;8.828,-3.387,-3.7563;9.298,-3.709,-2.0948)|",3.57013371856
"[H]OC1=NC(=O)N(C([H])([H])[H])C([H])([H])[C@@]1([H])C1=NC(C([H])([H])[H])=C([H])S1 |(3.4745,-2.7888,0.3016;4.02,-3.5199,-0.0918;5.1365,-3.0128,-0.6048;5.9291,-3.7942,-1.237;7.1507,-3.336,-1.7671;8.0071,-4.1236,-2.1348;7.3465,-1.9561,-1.851;8.4713,-1.4553,-2.6258;8.901,-0.5729,-2.1368;8.1738,-1.1785,-3.6487;9.2179,-2.2463,-2.6796;6.246,-1.0499,-1.6024;6.6563,-0.0489,-1.4242;5.5633,-0.9728,-2.4668;5.4645,-1.5266,-0.3691;6.1571,-1.505,0.4877;4.2903,-0.6387,-0.0369;3.1858,-1.0689,0.5118;2.2749,-0.0587,0.7629;0.9531,-0.3982,1.384;0.3427,0.4976,1.5263;1.0955,-0.8787,2.3586;0.3961,-1.0997,0.7522;2.7143,1.1812,0.3967;2.1963,2.1262,0.4819;4.3059,1.096,-0.2878)|",4.868116785445
"[H]O/C(=N/N([H])[H])C([H])([H])N1C(=O)N=C2C([H])=C([H])C([H])=C([H])[C@]2([H])C1=O |(2.6429,-4.3445,-1.313;2.3724,-4.8839,-2.0802;1.5406,-4.0888,-2.824;0.4672,-4.6062,-3.2782;-0.4193,-3.8259,-3.9975;-0.0325,-3.0113,-4.4793;-0.9241,-4.4224,-4.643;2.0267,-2.6662,-3.0569;1.919,-2.372,-4.102;3.0829,-2.5986,-2.7865;1.2753,-1.6639,-2.2718;0.5031,-0.6754,-2.9607;0.3427,-0.7719,-4.1631;0.0271,0.4177,-2.2389;0.0588,0.4024,-0.9424;-0.3046,1.591,-0.2032;-0.7008,2.425,-0.7729;-0.0627,1.6737,1.1306;-0.2978,2.5939,1.6593;0.5555,0.5905,1.8783;0.8034,0.7537,2.9228;0.8295,-0.5848,1.2848;1.3006,-1.4109,1.8063;0.4241,-0.8285,-0.1393;-0.5199,-1.4105,-0.0835;1.356,-1.7602,-0.9037;2.0641,-2.5843,-0.3384)|",2.555149056195
"[H]OC1=NN([H])[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])C([H])([H])[C@@]1([H])/N=C(/O[H])C([H])([H])[H] |(2.9985,2.2066,0.3691;2.7205,3.0925,0.0576;2.9059,3.0578,-1.2889;3.068,4.1606,-1.9104;3.3596,4.1302,-3.2801;2.9901,4.9797,-3.6928;3.0572,2.9364,-4.071;3.6765,3.0093,-4.9754;1.6024,2.7851,-4.5259;1.3097,1.9512,-5.6155;2.1206,1.4361,-6.1282;-0.0009,1.7864,-6.0626;-0.2049,1.1431,-6.9149;-1.0465,2.4578,-5.4243;-2.0686,2.3351,-5.7726;-0.7672,3.2937,-4.343;-1.5729,3.8237,-3.8416;0.547,3.4589,-3.8979;0.7533,4.1123,-3.055;3.5571,1.7166,-3.2688;4.6341,1.8186,-3.097;3.3884,0.7952,-3.836;2.8345,1.6711,-1.9163;1.7666,1.4604,-2.0923;3.397,0.7188,-0.9656;3.1324,-0.5204,-1.0283;3.7103,-1.3055,-0.0743;3.4425,-2.2278,-0.2081;2.2689,-1.2671,-2.0222;1.7091,-0.5927,-2.6715;1.5511,-1.9082,-1.4953;2.8913,-1.9118,-2.6552)|",4.821857430860001
"[H]C([H])=C1N(C([H])([H])C([H])([H])[H])C(=O)/N=C2/C([H])=C(C([H])([H])[H])C(C([H])([H])[H])=C([H])[C@]12[H] |(6.9153,3.2395,0.2376;7.6088,2.4628,-0.0541;8.6385,2.5908,0.2544;7.1879,1.402,-0.7611;8.0118,0.3434,-1.1786;9.3513,0.1587,-0.6024;9.8884,-0.4994,-1.2849;9.8634,1.1268,-0.5972;9.3149,-0.4547,0.7982;10.3323,-0.5503,1.1943;8.8671,-1.4526,0.7598;8.7357,0.1633,1.4922;7.6101,-0.5689,-2.1754;8.2284,-1.604,-2.3653;6.5428,-0.1894,-3.0069;5.6762,0.6594,-2.5588;4.6274,1.1441,-3.4257;4.5966,0.7192,-4.4247;3.7524,2.1102,-3.0429;2.6972,2.6015,-3.9988;2.7976,3.6777,-4.1886;1.6882,2.4465,-3.5955;2.7626,2.0806,-4.9574;3.8311,2.7021,-1.6899;2.8512,3.782,-1.2973;3.0345,4.1177,-0.2726;1.8134,3.4304,-1.3539;2.9263,4.6573,-1.9544;4.7699,2.2729,-0.8247;4.8045,2.6883,0.1791;5.7279,1.1597,-1.1262;5.412,0.2971,-0.5065)|",3.703469505305
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"[H]C1=NC([H])=C([H])C(=O)/C1=N/C(=O)C([H])([H])[C@@]1([H])C([H])=C([H])C([H])([H])C1([H])[H] |(-2.7281,-3.9376,1.7006;-3.0379,-4.6605,0.9455;-3.3969,-5.83,1.3424;-3.8184,-6.7651,0.3789;-4.1042,-7.7248,0.801;-3.8822,-6.5445,-0.952;-4.2149,-7.3109,-1.6448;-3.4874,-5.2473,-1.5124;-3.5094,-4.988,-2.7043;-3.031,-4.2253,-0.4726;-2.6623,-3.0671,-0.8711;-2.2807,-2.0113,-0.0211;-3.1026,-1.3999,0.6357;-0.8049,-1.6608,-0.1189;-0.2388,-2.51,0.2951;-0.5286,-1.6136,-1.1801;-0.4408,-0.3573,0.6064;-0.8249,-0.4374,1.6345;1.0504,-0.0859,0.6403;1.7763,-0.8489,0.9122;1.3402,1.1856,0.3552;2.3393,1.6137,0.3618;0.0993,2.0031,0.0793;0.1917,2.6357,-0.8125;-0.1058,2.6841,0.9192;-1.0058,0.9248,-0.0697;-1.1735,0.7233,-1.1353;-1.9654,1.2256,0.3564)|",2.67215801191
"[H]OC(=N/C1=C([H])C([H])=C([H])C(SC([H])([H])[H])=C1[H])/C(=C(\[H])C([H])([H])C([H])([H])[H])C([H])([H])[H] |(5.1155,-1.1356,2.9226;4.3569,-0.8729,2.3779;4.4596,-1.4861,1.1595;3.4106,-1.468,0.442;3.3757,-1.9034,-0.8907;4.1348,-1.2844,-1.9005;4.8043,-0.4693,-1.6425;3.9972,-1.7005,-3.2207;4.5837,-1.2169,-3.9984;3.1209,-2.731,-3.5695;3.0415,-3.0365,-4.6065;2.3512,-3.3417,-2.571;1.1926,-4.67,-2.8667;1.169,-4.8202,-4.6828;0.4249,-5.589,-4.9063;2.1368,-5.1418,-5.0774;0.8634,-3.8838,-5.1574;2.4719,-2.9143,-1.2421;1.8603,-3.3583,-0.4617;5.803,-2.089,0.866;5.8554,-3.3884,0.5274;4.9085,-3.925,0.4716;7.0612,-4.2227,0.1992;6.9976,-4.5083,-0.8616;7.9886,-3.6506,0.3063;7.1362,-5.5027,1.05;7.9915,-6.1189,0.7522;6.2296,-6.1077,0.9333;7.2434,-5.2641,2.1141;6.9919,-1.1565,0.9709;7.9418,-1.6952,0.9804;6.9497,-0.534,1.8727;7.0094,-0.4615,0.1208)|",4.840905400395
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"[H]OC(=O)[C@@]12C([H])([H])N([H])C([H])([H])[C@]1([H])C([H])([H])N(/C(=N\C([H])([H])C([H])=C([H])[H])O[H])C2([H])[H] |(4.3787,-6.8233,4.7296;4.3604,-6.3842,5.5957;4.5784,-5.0442,5.4593;4.5907,-4.3442,6.4412;4.8194,-4.5499,4.0357;6.1099,-5.2008,3.4053;6.3131,-6.1712,3.8785;6.9967,-4.579,3.5519;5.848,-5.4213,1.9844;5.9442,-4.5289,1.5009;4.4367,-5.8105,1.9468;4.0269,-5.6994,0.9383;4.3629,-6.8774,2.2072;3.687,-4.9555,3.0092;2.8685,-5.5157,3.4729;3.1619,-3.6114,2.4721;2.2905,-3.2765,3.0577;2.8584,-3.6721,1.4258;4.296,-2.6925,2.6598;4.192,-1.3629,2.2564;3.8448,-0.8749,1.1308;3.6329,-1.7126,-0.0371;4.2464,-2.6286,-0.0189;2.5781,-2.0372,-0.0815;3.9416,-0.9313,-1.2898;3.4693,0.0488,-1.3482;4.7182,-1.364,-2.2817;4.8922,-0.7701,-3.1749;5.213,-2.3329,-2.2408;4.5719,-0.5073,3.2476;4.576,0.3705,2.8247;4.8532,-3.0083,3.9866;5.8606,-2.5997,4.0861;4.2443,-2.5909,4.7973)|",6.185147821865
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"[H]OC(=O)[C@@]([H])(O[C@]1([H])C([H])([H])C([H])([H])C([H])([H])O[C@]1([H])C([H])=C([H])[H])C([H])([H])[H] |(0.1138,-0.841,-0.3231;-0.6522,-0.6455,-0.9049;-0.3249,-1.0598,-2.1387;-1.0723,-0.9635,-3.0827;1.0976,-1.6363,-2.2749;1.6946,-0.8588,-2.7746;1.6122,-1.8711,-0.9583;3.0093,-1.6283,-0.7628;3.5377,-1.748,-1.719;3.5559,-2.655,0.2277;2.9744,-2.6084,1.1578;3.4148,-3.662,-0.1806;5.0393,-2.3655,0.5054;5.6279,-2.5584,-0.401;5.4245,-3.0287,1.2899;5.2294,-0.9053,0.9181;6.2899,-0.6418,0.9674;4.7998,-0.7314,1.9171;4.6672,-0.0045,-0.0364;3.2633,-0.176,-0.2781;3.0482,0.5113,-1.1044;2.407,0.2287,0.8986;2.5329,-0.3254,1.827;1.5155,1.2205,0.8532;0.9121,1.49,1.716;1.3529,1.8054,-0.0498;1.1009,-2.9104,-3.1154;0.6224,-2.719,-4.0788;0.5482,-3.7017,-2.5995;2.1257,-3.2554,-3.2881)|",6.718490968845001
"[H]C1=C([H])[C@]([H])([C@]([H])(C([H])([H])C(=O)C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])OC1=O |(10.2479,5.6761,5.4608;9.6077,4.8315,5.2431;9.7912,3.842,4.3659;10.6371,3.6784,3.7091;8.6335,2.8829,4.4472;8.9883,1.9158,4.8244;7.8214,2.6481,3.1472;7.0195,1.9496,3.4258;8.6948,1.9798,2.0488;8.0546,1.7531,1.1899;9.4626,2.6879,1.7175;9.4079,0.7138,2.5133;10.5036,0.7739,3.0443;8.6937,-0.6072,2.301;9.2434,-1.4144,2.7888;8.6169,-0.8174,1.226;7.6678,-0.5657,2.688;7.1664,3.944,2.6279;6.7205,4.4659,3.4818;7.9464,4.6079,2.2283;6.0771,3.7339,1.5666;6.4861,3.2174,0.6871;5.2973,3.0737,1.9749;5.4364,5.0507,1.1067;5.0243,5.5783,1.9789;6.2176,5.708,0.6959;4.3317,4.8613,0.0593;4.7378,4.3123,-0.8025;3.541,4.2235,0.4795;3.7217,6.1826,-0.4197;2.9303,6.013,-1.1585;4.4807,6.8234,-0.8852;3.2841,6.7427,0.4156;7.7761,3.4427,5.4568;8.3277,4.6061,5.9571;7.7955,5.2545,6.8215)|",5.798746154154999
"[H]C1=C([H])C([H])=C([C@]([H])(C([H])([H])C(=O)C([H])([H])[H])[C@]2([H])OC(=O)C([H])=C2[H])O1 |(0.5543,-0.3488,5.7081;0.538,0.1728,4.7632;-0.4161,0.3836,3.8186;-1.4366,0.0277,3.8437;0.2076,1.1629,2.7882;-0.2379,1.5072,1.8656;1.497,1.3736,3.1773;2.6492,2.0968,2.5526;2.278,2.5006,1.6056;3.8265,1.1644,2.2197;4.7219,1.7644,2.0032;4.0856,0.523,3.0704;3.5704,0.3014,0.9841;2.6731,0.5478,0.2014;4.5108,-0.8721,0.7817;4.2983,-1.6445,1.5327;5.5565,-0.572,0.9178;4.369,-1.2951,-0.2148;3.0789,3.3212,3.4051;2.184,3.9341,3.5806;4.0132,4.1202,2.6621;5.1569,4.3352,3.4082;6.0819,4.9894,3.0006;4.9804,3.6268,4.6987;5.7407,3.6335,5.4685;3.7846,3.033,4.7039;3.3386,2.4319,5.4864;1.7128,0.7672,4.3945)|",4.9742411871400005
"[H]C1=C([H])C([H])=C(/C(=C(\[H])C([H])([H])OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(3.6529,7.4323,-5.9082;4.5038,6.7823,-5.723;5.7576,7.0943,-6.2498;5.8908,7.9952,-6.8433;6.8475,6.2554,-6.0188;7.8143,6.5242,-6.4332;6.7181,5.0895,-5.2433;7.8876,4.2016,-4.9806;7.9904,3.5584,-3.803;7.2319,3.734,-3.0425;9.0436,2.5857,-3.3614;9.9464,2.6251,-3.9897;8.655,1.551,-3.4284;9.3746,2.8771,-2.0114;10.3084,1.9648,-1.4591;9.9198,0.9328,-1.5344;11.2534,1.9974,-2.0288;10.5559,2.3335,-0.0008;11.3524,1.682,0.3862;10.9425,3.3608,0.037;9.3083,2.2165,0.8836;8.5134,2.838,0.4559;8.9413,1.1802,0.853;9.5662,2.625,2.3371;8.6599,2.529,2.9457;9.9011,3.6677,2.4015;10.3432,2.0005,2.796;8.9079,4.0857,-6.0932;9.6666,3.3282,-5.8887;9.4322,5.0363,-6.2545;8.4216,3.8282,-7.0419;5.4426,4.7833,-4.7346;5.3022,3.8647,-4.1727;4.3521,5.6194,-4.9655;3.3777,5.3535,-4.5638)|",5.417786763454999
"[H]C1=C([H])C([H])=C(C([H])([H])/C(=C(\[H])C([H])([H])OC([H])(C([H])([H])[H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(6.7198,4.821,2.6725;6.434,3.8589,2.2554;7.4001,3.0159,1.7068;8.4443,3.3178,1.6933;7.0282,1.7807,1.1702;7.788,1.1267,0.7472;5.6912,1.3665,1.1744;5.2847,0.0089,0.6166;4.9237,-0.6255,1.4362;6.1817,-0.4841,0.2147;4.221,0.0783,-0.4703;3.0237,-0.4822,-0.237;2.8606,-0.9538,0.7325;1.8236,-0.5869,-1.1337;1.6807,-1.6431,-1.4216;1.9375,-0.0148,-2.0634;0.6959,-0.1144,-0.4043;-0.5533,-0.2256,-1.091;-0.4059,0.0582,-2.1476;-1.1056,-1.6539,-1.0308;-2.0632,-1.7206,-1.5603;-1.2648,-1.9515,0.0118;-0.4221,-2.3752,-1.4912;-1.4931,0.7811,-0.434;-2.4767,0.7697,-0.9165;-1.0794,1.7919,-0.5032;-1.6247,0.5376,0.6263;4.6661,0.7842,-1.7555;5.7013,1.106,-1.5798;4.7096,-0.1639,-2.9702;5.1665,0.3391,-3.8312;3.7087,-0.4913,-3.2709;5.2992,-1.0618,-2.7515;3.8746,2.0728,-2.0573;4.3025,2.5812,-2.9302;3.918,2.762,-1.2082;2.8197,1.8751,-2.2723;4.7285,2.2248,1.7264;3.6847,1.9201,1.7281;5.0952,3.4583,2.2632;4.3351,4.1082,2.6894)|",6.291272223560001
"[H]C1=C([H])C([H])=C(C([H])([H])C([H])([H])OC([H])([H])/C([H])=C(\C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(1.4659,7.1363,8.2349;1.8813,6.5424,7.4251;1.5778,6.8527,6.0982;0.9246,7.6913,5.8704;2.1165,6.0914,5.0601;1.8804,6.3429,4.0281;2.967,5.0092,5.3256;3.5193,4.1583,4.2025;3.6632,4.7602,3.2976;4.4995,3.7504,4.4753;2.5951,2.9875,3.8565;1.6037,3.3639,3.5501;2.4373,2.3522,4.7457;3.1913,2.2428,2.8128;2.4217,1.1123,2.4263;2.2421,0.4713,3.3106;1.4308,1.435,2.068;3.1989,0.3637,1.3797;4.2711,0.4526,1.5268;2.7175,-0.3805,0.3723;1.2276,-0.5704,0.1628;0.6416,0.2311,0.621;0.8802,-1.5141,0.6044;0.9628,-0.6049,-0.899;3.6454,-1.114,-0.6156;3.5038,-0.4833,-2.0233;4.145,-1.01,-2.7407;3.803,0.5708,-2.01;2.4781,-0.5369,-2.4033;5.1294,-1.0344,-0.2118;5.7395,-1.5884,-0.9345;5.3031,-1.4717,0.7776;5.4949,-0.0017,-0.1979;3.2502,-2.609,-0.69;3.9094,-3.1414,-1.3861;2.2223,-2.7469,-1.0408;3.3399,-3.0879,0.2922;3.2637,4.7103,6.6625;3.9277,3.878,6.8872;2.7276,5.4678,7.7043;2.9749,5.2217,8.7339)|",6.326647024125
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"[H]C([H])([H])C([H])([H])OC(=O)C1=NO[C@@]([H])(OC([H])([H])C([H])([H])[H])[C@@]2([H])O[C@@]12[H] |(4.0829,-4.0162,4.0744;4.6827,-3.3717,3.4216;4.5647,-2.3382,3.7591;5.7333,-3.6622,3.5224;4.2154,-3.5243,1.9822;3.1802,-3.1962,1.8624;4.3134,-4.5551,1.6347;5.0543,-2.7683,1.0725;4.7414,-1.4782,0.9046;3.8237,-0.9023,1.4611;5.6457,-0.762,-0.0503;6.6059,-1.4293,-0.5941;7.4362,-0.7722,-1.4703;7.6242,0.6785,-1.4004;8.3462,0.8538,-0.5846;8.1135,1.0774,-2.6239;9.4876,0.7271,-2.8877;9.7105,-0.2509,-2.4478;9.5472,0.6254,-3.9747;10.4466,1.8028,-2.3928;11.4781,1.5351,-2.6494;10.3952,1.9223,-1.3044;10.214,2.7674,-2.8552;6.3462,1.4115,-1.0916;6.4464,2.4906,-0.9676;5.1774,0.9592,-1.7547;5.3338,0.6592,-0.3429;4.6277,1.1531,0.3162)|",5.287172115214999
"[H]C([H])=C([H])/C([H])=C(\[H])[Ge](C([H])([H])[H])(C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(6.2737,8.5242,-0.9207;6.6423,7.5083,-1.0261;7.2238,7.1117,-0.1965;6.3943,6.7757,-2.1218;5.8067,7.2062,-2.9333;6.8609,5.4083,-2.321;7.4449,4.9892,-1.4981;6.6157,4.6691,-3.4207;6.0252,5.1351,-4.2139;7.2265,2.8261,-3.7194;9.2026,2.7421,-3.6652;9.6383,3.4223,-4.4034;9.5465,1.7254,-3.8837;9.5735,3.024,-2.6747;6.5801,2.2573,-5.4982;7.0458,2.8571,-6.2868;5.4942,2.3739,-5.5728;6.8243,1.2053,-5.679;6.5035,1.6227,-2.3105;6.8438,0.6035,-2.5407;6.9768,1.8967,-1.3575;4.9747,1.6555,-2.1693;4.6474,2.687,-1.9786;4.5094,1.3624,-3.1219;4.4374,0.7448,-1.055;4.7658,-0.2887,-1.2414;4.8913,1.039,-0.0969;2.9089,0.7778,-0.9243;2.5828,1.8134,-0.7456;2.4561,0.4794,-1.8819;2.3646,-0.1223,0.1923;2.6907,-1.157,0.014;2.814,0.1764,1.1501;0.8374,-0.0819,0.3111;0.4803,-0.734,1.1166;0.3587,-0.4096,-0.62;0.4835,0.9344,0.524)|",5.295335530729999
"[H]C([H])=C([H])/C([H])=C(\[H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(5.5769,-6.1843,6.6031;6.0779,-5.7718,5.7325;6.7897,-4.9694,5.9145;5.8346,-6.2259,4.494;5.1141,-7.0319,4.3503;6.4756,-5.7129,3.2894;7.191,-4.9028,3.4533;6.2416,-6.1648,2.0392;5.5139,-6.9768,1.946;7.064,-5.5176,0.4674;6.3662,-6.493,-1.001;6.5698,-7.5652,-0.8944;6.8143,-6.1635,-1.9461;5.2804,-6.3695,-1.0883;8.9425,-5.7711,0.5633;9.1942,-6.8339,0.6564;9.3731,-5.2523,1.428;9.439,-5.3866,-0.3362;6.7142,-3.6556,0.26;7.1659,-3.1267,1.1134;7.2617,-3.3018,-0.6272;5.2294,-3.2681,0.1442;4.7852,-3.7657,-0.7298;4.6854,-3.651,1.019;4.9918,-1.7555,0.0267;5.4269,-1.2544,0.9042;5.5393,-1.3684,-0.846;3.51,-1.3779,-0.0947;3.0783,-1.8757,-0.976;2.9627,-1.7733,0.7744;3.2633,0.1327,-0.2007;3.6913,0.6311,0.6807;3.8092,0.5299,-1.0685;1.7794,0.4936,-0.3239;1.6332,1.5776,-0.3953;1.2108,0.1384,0.5446;1.3351,0.0377,-1.2174)|",5.254518453155001
"[H]C([H])=C([H])/C([H])=C(\[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(6.3778,-8.8756,2.3029;6.9418,-7.9525,2.3966;8.0131,-8.0155,2.2175;6.35,-6.7919,2.7147;5.2729,-6.7737,2.8859;7.0459,-5.5195,2.8572;8.1214,-5.5408,2.688;6.4312,-4.3674,3.1756;5.3528,-4.4035,3.3406;7.0529,-2.9961,3.3502;8.5898,-3.0415,3.2744;9.007,-3.7096,4.0363;9.0069,-2.0415,3.4406;8.9362,-3.3859,2.2932;6.6449,-2.4527,4.7406;7.0733,-3.0717,5.537;5.5579,-2.4479,4.8765;7.0064,-1.4256,4.877;6.5504,-2.0363,2.2252;7.0212,-1.0559,2.3927;6.9433,-2.4059,1.2677;5.0344,-1.8353,2.0851;4.5486,-2.7937,1.8578;4.6061,-1.489,3.0357;4.678,-0.8254,0.9846;5.1549,0.1404,1.209;5.1094,-1.1611,0.0299;3.1682,-0.6166,0.8112;2.6923,-1.5813,0.5795;2.7337,-0.2867,1.7669;2.8114,0.398,-0.2829;3.2851,1.3624,-0.05;3.2473,0.0692,-1.2372;1.3021,0.5987,-0.4533;1.0811,1.3298,-1.2394;0.8429,0.9592,0.4755;0.8053,-0.3413,-0.7235)|",5.488536364585
"[H]OC([H])([H])[C@@]1([H])N(C2=C([H])C([H])=C([H])C([H])=C2[H])[C@]([H])(C([H])([H])N(=O)=O)C([H])([H])C1([H])[H] |(2.2373,2.9144,-3.931;2.6952,2.8955,-3.0777;2.3863,1.6634,-2.4357;1.3074,1.5627,-2.2533;2.7024,0.7991,-3.0417;3.1447,1.6551,-1.0998;2.9145,2.5935,-0.5803;2.782,0.4923,-0.2776;1.5599,0.3513,0.3797;0.6257,1.4098,0.4214;0.851,2.3564,-0.0566;-0.593,1.259,1.0781;-1.2885,2.0945,1.0896;-0.9257,0.0633,1.7165;-1.8774,-0.0465,2.2275;-0.008,-0.9863,1.6846;-0.2405,-1.9293,2.1729;1.2153,-0.8555,1.0295;1.8965,-1.6993,1.0238;3.8621,-0.4793,-0.1636;3.5069,-1.4972,-0.3611;4.4616,-0.468,1.2701;3.671,-0.5405,2.018;5.088,0.4055,1.4472;5.3284,-1.6738,1.4564;6.546,-1.5115,1.5046;4.7526,-2.7614,1.4986;4.8807,-0.0222,-1.2218;5.9049,-0.3011,-0.9567;4.6476,-0.4942,-2.1821;4.6623,1.4977,-1.2943;4.9911,1.9433,-2.2362;5.2,2.0036,-0.4843)|",3.5510857490250003
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"[H]OC(=N/C([H])([H])C([H])([H])C([H])=C([H])[H])/C([H])=C(\[H])[C@]([H])(N([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(4.8708,-0.4169,-1.066;4.9556,0.2788,-1.7371;6.2917,0.543,-1.907;6.6165,1.1178,-2.9926;7.9761,1.5446,-3.2538;7.957,2.6343,-3.4011;8.6878,1.358,-2.4346;8.5011,0.8856,-4.5457;8.5645,-0.2001,-4.3999;7.7522,1.057,-5.3312;9.8364,1.4319,-4.9681;9.8648,2.5025,-5.1817;10.9593,0.7216,-5.0838;11.897,1.1759,-5.3931;10.9814,-0.3479,-4.8824;7.1659,0.1346,-0.7777;8.1965,-0.1112,-1.0198;6.7569,0.0643,0.4964;5.7323,0.3497,0.7463;7.599,-0.3758,1.6692;8.555,-0.7584,1.2856;6.9038,-1.5229,2.2854;6.0961,-1.1908,2.8141;7.515,-1.978,2.9605;7.9111,0.8083,2.6349;6.9419,1.2122,2.9713;8.6829,0.3325,3.8751;8.887,1.172,4.5488;9.6497,-0.1052,3.5918;8.1267,-0.418,4.4468;8.6799,1.9358,1.9295;8.8737,2.7638,2.6207;8.1282,2.3329,1.0724;9.6514,1.5765,1.5647)|",5.58377621226
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"[H]O[C@@]([H])(C([H])=C([H])[H])C([H])([H])C([H])([H])[C@@]1([H])OC(C([H])([H])[H])(C([H])([H])[H])OC1([H])[H] |(-1.7561,-2.0381,-2.5557;-0.7931,-2.0853,-2.6634;-0.2532,-0.844,-2.1825;-0.5888,-0.0132,-2.8231;-0.7139,-0.5857,-0.772;-0.4627,-1.3678,-0.054;-1.3973,0.4912,-0.3829;-1.7108,0.6326,0.6481;-1.6621,1.2821,-1.0826;1.2727,-0.9576,-2.2772;1.6996,0.0153,-2.011;1.6186,-1.6826,-1.5289;1.794,-1.3926,-3.657;1.4593,-2.4156,-3.8639;2.8914,-1.4018,-3.6275;1.3455,-0.5094,-4.8137;0.2537,-0.5775,-4.936;1.7234,0.8524,-4.5707;1.9157,1.5041,-5.8326;0.7517,2.4446,-6.1356;0.8644,2.8816,-7.1331;0.7141,3.2529,-5.3985;-0.1917,1.8918,-6.1044;3.2662,2.216,-5.7971;3.4524,2.7274,-6.7468;4.0659,1.4899,-5.6242;3.2869,2.9539,-4.9884;1.9178,0.4702,-6.8275;2.0228,-0.7793,-6.1569;1.52,-1.5384,-6.7631;3.0756,-1.0716,-6.0158)|",6.87359586363
"[H]C#C/C([H])=C(\[H])C([H])=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(0.2336,0.0712,0.9532;1.277,-0.1178,1.0616;2.4622,-0.3348,1.1954;3.8536,-0.5774,1.3184;4.4615,-0.3836,0.4338;4.4507,-1.0272,2.4506;3.8125,-1.1982,3.3082;5.8867,-1.2357,2.5284;6.3692,-0.9186,1.6112;6.6885,-1.72,3.5166;6.0634,-2.2258,4.8606;5.4158,-1.0229,5.6004;4.8953,-1.367,6.5031;4.6974,-0.4759,4.9883;6.1855,-0.3082,5.9141;5.013,-3.3354,4.5713;4.5684,-3.6771,5.5142;5.4996,-4.1983,4.102;4.1999,-3.0271,3.915;7.0123,-2.8845,5.8958;6.4301,-3.0782,6.8043;7.8573,-2.2579,6.1862;7.3924,-3.8499,5.5544;8.2277,-1.6672,3.2712;8.871,-0.5993,4.1966;9.9585,-0.5847,4.0511;8.6839,-0.7757,5.2576;8.4866,0.3978,3.9529;8.9211,-3.0416,3.4814;9.9225,-3.0175,3.0364;8.3619,-3.8457,2.9889;9.0479,-3.3059,4.5301;8.5949,-1.2424,1.8273;9.6857,-1.2399,1.7277;8.2504,-0.2327,1.5814;8.1976,-1.9377,1.0794)|",4.345658192485001
"[H]C(=O)/C([H])=C([H])/C([H])=C(\[H])C([H])=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(-2.9933,0.2879,-0.8565;-2.4842,1.2284,-1.172;-3.0953,2.0972,-1.7726;-1.0724,1.3056,-0.8043;-0.5458,2.2101,-1.0996;-0.4738,0.2892,-0.135;-1.1002,-0.5738,0.0974;0.8997,0.1893,0.3143;1.1735,-0.7507,0.7925;1.8539,1.1518,0.2;1.5729,2.0841,-0.2739;3.2147,0.9445,0.6518;3.3541,-0.0707,1.0037;4.305,1.7651,0.6724;4.1774,3.2605,0.2267;3.0549,3.9655,1.0407;2.984,5.0165,0.7351;3.2955,3.946,2.1098;2.0671,3.5223,0.9232;3.8756,3.3087,-1.2972;3.7086,4.3451,-1.6158;2.9996,2.7239,-1.584;4.7266,2.9201,-1.868;5.4083,4.1807,0.4368;5.179,5.1502,-0.0205;6.324,3.819,-0.0339;5.6088,4.3702,1.4939;5.6659,1.1187,1.0745;6.6007,1.0673,-0.1643;7.5685,0.6328,0.116;6.7977,2.0482,-0.6002;6.1646,0.4364,-0.9472;6.3666,1.8584,2.247;7.182,1.2356,2.6333;5.6684,2.0377,3.0727;6.8078,2.8112,1.9589;5.5241,-0.3497,1.5476;6.5196,-0.7355,1.7921;5.1092,-1.0048,0.775;4.9076,-0.4391,2.4494)|",3.5592491645400006
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"[H]C([H])=C([H])/C(C1=C([H])SC([H])=C1[H])=C(/[H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.2315,2.5647,0.8463;4.2203,1.6405,0.2758;5.0607,1.4666,-0.3899;3.2032,0.7728,0.3896;2.3817,1.0218,1.0578;3.0855,-0.5186,-0.3037;4.3168,-1.1614,-0.8253;4.3939,-1.7742,-2.0545;3.6245,-1.8199,-2.813;5.9506,-2.4759,-2.3474;6.5248,-1.9457,-0.7957;7.5326,-2.1805,-0.4806;5.5628,-1.2633,-0.1105;5.7114,-0.8547,0.8822;1.8995,-1.1744,-0.4437;1.9525,-2.1921,-0.8271;0.5421,-0.7196,-0.1195;0.1208,0.6207,-0.2271;0.8149,1.3721,-0.5888;-1.1884,0.9867,0.0823;-1.4908,2.0262,-0.0144;-2.1124,0.0257,0.4965;-3.1328,0.3135,0.7348;-1.7196,-1.3125,0.5843;-2.4338,-2.0713,0.8933;-0.414,-1.6791,0.2714;-0.1152,-2.7227,0.3389)|",4.2096012672350005
"[H]OC1=N[C@@]([H])(C([H])([H])C([H])([H])[H])C2=C(C([H])=C(OC([H])([H])[H])C(OC([H])([H])[H])=C2[H])C1([H])[H] |(2.3026,5.4018,-1.3176;1.7977,4.8427,-0.7074;2.4402,3.6428,-0.5658;1.9369,2.8118,0.2427;2.5303,1.4928,0.4136;2.5911,1.3296,1.4999;1.5343,0.4253,-0.1163;1.9129,-0.5669,0.162;0.5893,0.57,0.4206;1.2844,0.4791,-1.6247;0.5716,-0.2967,-1.9261;2.2094,0.3199,-2.1912;0.864,1.4469,-1.9181;3.9325,1.3169,-0.1533;4.4747,2.2499,-1.0349;5.7692,2.0537,-1.5364;6.2159,2.7706,-2.2208;6.5314,0.9539,-1.1693;7.8179,0.8505,-1.6401;8.0741,-0.2458,-2.5206;9.1143,-0.1402,-2.8375;7.4207,-0.1994,-3.4025;7.9405,-1.2071,-2.0154;5.9938,0.0149,-0.2565;6.8138,-1.0213,0.0918;6.3342,-1.9642,1.0373;7.1428,-2.6838,1.1776;5.4415,-2.4877,0.6699;6.1024,-1.4869,1.9982;4.7035,0.2078,0.2332;4.2822,-0.4982,0.9415;3.6694,3.4638,-1.4329;3.3419,3.3962,-2.4828;4.2928,4.369,-1.3722)|",5.6817371984400005
"[H]OC1=N[C@@]([H])(C([H])([H])[H])C2=C([H])C(OC([H])([H])[H])=C(OC([H])([H])[H])C([H])=C2C1([H])[H] |(4.9103,2.0813,-3.2084;4.3869,2.3947,-2.4551;3.7893,1.3223,-1.8511;3.1052,1.5516,-0.8132;2.3814,0.4655,-0.1633;2.5201,0.6093,0.918;0.8761,0.6483,-0.4513;0.2757,-0.0597,0.1304;0.6662,0.4822,-1.514;0.5731,1.6679,-0.1948;2.8519,-0.9392,-0.511;2.5187,-2.0156,0.3271;1.9466,-1.8163,1.2275;2.9252,-3.3155,0.031;2.6655,-4.411,0.8052;1.9591,-4.2182,2.0208;1.883,-5.2049,2.4809;0.9509,-3.8204,1.8441;2.4998,-3.5426,2.6961;3.6819,-3.5499,-1.1419;4.1632,-4.7976,-1.4568;3.185,-5.7748,-1.8177;3.7437,-6.6769,-2.0774;2.6054,-5.4445,-2.6907;2.5051,-5.9909,-0.9881;4.0191,-2.4785,-1.9571;4.6216,-2.6864,-2.8376;3.6157,-1.1714,-1.6528;4.0076,-0.0114,-2.5371;3.4328,-0.0135,-3.4773;5.0636,-0.1016,-2.8334)|",5.69806402947
"[H]OC([H])([H])[C@]([H])(/C([H])=C(\[H])C([H])([H])[H])[Si](C1=C([H])C([H])=C([H])C([H])=C1[H])(C([H])([H])[H])C([H])([H])[H] |(4.8212,2.4127,1.0855;5.7515,2.155,0.9968;5.7938,0.9309,0.2576;5.3341,1.0695,-0.734;6.8573,0.7281,0.1061;5.1319,-0.2551,0.9873;5.3876,-1.1614,0.4162;3.6326,-0.1283,1.0286;3.2402,0.7998,1.4551;2.7462,-1.0438,0.6192;3.1175,-1.9805,0.1982;1.2526,-0.8977,0.69;0.7955,-0.976,-0.3058;0.963,0.0676,1.12;0.8012,-1.6891,1.304;5.8404,-0.5517,2.7688;7.7352,-0.4423,2.7406;8.3874,0.796,2.5721;7.7989,1.6998,2.4369;9.7797,0.8881,2.5611;10.256,1.8566,2.4293;10.5595,-0.2595,2.7187;11.6444,-0.1891,2.7107;9.938,-1.4973,2.8859;10.5376,-2.396,3.0102;8.5439,-1.5833,2.8967;8.0823,-2.5583,3.0342;5.1449,0.6961,4.0129;5.4987,0.4619,5.0237;4.0489,0.6702,4.0301;5.4593,1.7152,3.7681;5.3061,-2.2955,3.2864;5.665,-2.5431,4.2923;5.6754,-3.0662,2.5994;4.2123,-2.362,3.2962)|",5.95929332595
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"[H]C([H])=C([H])[C@@]1([H])/C(=C(/[H])C([H])([H])[H])C2=C([H])C(Cl)=C([H])C([H])=C2OC1([H])[H] |(0.973,-0.6842,1.6464;1.1668,-0.0631,0.7763;0.3382,0.5533,0.4383;2.3561,-0.0799,0.1708;3.1363,-0.7246,0.5746;2.7905,0.7723,-0.9962;3.7071,1.2896,-0.6646;3.1504,0.0857,-2.3171;3.0537,-1.2132,-2.6585;3.3119,-1.4462,-3.6915;2.6469,-2.4269,-1.8741;1.8812,-2.9864,-2.4277;2.25,-2.2035,-0.8857;3.5006,-3.1105,-1.7579;3.6014,1.0927,-3.3191;4.4777,0.7807,-4.3735;4.8731,-0.2247,-4.4631;4.8735,1.7501,-5.2854;5.9739,1.3133,-6.5913;4.431,3.0693,-5.1721;4.751,3.8196,-5.8868;3.5833,3.4027,-4.1236;3.2201,4.4178,-3.999;3.1741,2.4317,-3.2027;2.3562,2.8701,-2.2019;1.7883,1.887,-1.3347;1.4928,2.4351,-0.437;0.8892,1.4656,-1.8051)|",4.91437614003
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"[H]O/C(=N/[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])[C@@]1([H])/N=C(/O[H])C([H])([H])[H])OC([H])([H])C([H])=C([H])[H] |(-0.196,0.6966,3.0806;0.5216,1.0555,2.5293;1.6295,0.3812,2.936;1.564,-0.472,3.8682;2.7512,-1.2135,4.2635;3.6742,-0.6673,4.0283;2.6501,-1.5798,5.7543;3.6382,-1.894,6.1144;2.3353,-0.7251,6.3606;1.6512,-2.7661,5.8061;1.9338,-3.4968,6.5719;0.649,-2.4091,6.0609;1.6551,-3.3806,4.378;0.7023,-3.1952,3.8742;1.8273,-4.4626,4.3698;2.7651,-2.6261,3.6069;3.7379,-3.0709,3.873;2.5356,-2.5965,2.183;3.278,-3.193,1.3437;2.9612,-3.0974,0.0193;2.1631,-2.5366,-0.0053;4.5098,-4.0446,1.5144;4.8025,-4.1517,2.5592;5.3405,-3.6031,0.9536;4.3256,-5.0387,1.0927;2.7413,0.7387,2.2651;2.6629,1.7876,1.272;1.9885,1.4866,0.4642;2.2505,2.6892,1.7415;4.0511,2.0302,0.7658;4.7993,2.2501,1.5262;4.3826,2.0189,-0.5244;5.393,2.2391,-0.8571;3.6543,1.794,-1.3008)|",6.160657575319999
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"[H]OC(=O)[C@@]([H])(N([H])[H])C([H])([H])C([H])([H])SC([H])([H])C([H])([H])C([H])([H])F |(3.462,3.0548,1.0475;3.7439,3.1773,1.9794;5.021,3.6191,2.0228;5.5762,3.8111,3.0795;5.7209,3.8356,0.6658;4.9829,4.2208,-0.0481;6.8152,4.7996,0.7471;7.3557,4.5786,1.5851;6.4285,5.7258,0.9274;6.287,2.5049,0.1174;6.766,2.7415,-0.8385;7.0931,2.1753,0.7877;5.3147,1.3278,-0.0402;4.9755,0.9622,0.9344;5.8255,0.4939,-0.531;3.7365,1.6751,-0.9319;4.3086,2.3596,-2.5359;3.3927,2.6853,-3.0372;4.9185,3.2532,-2.3615;5.0466,1.3563,-3.4269;5.9897,1.0411,-2.9617;4.4323,0.4579,-3.5553;5.3656,1.9456,-4.7939;5.8684,1.2085,-5.4314;6.0073,2.8323,-4.7084;4.1835,2.3312,-5.4242)|",6.936182049245
"[H]C1=NN([H])C2=C3C(=O)C([H])=C([H])C([H])=C3OC(C([H])([H])[H])(C([H])([H])[H])[C@]12[H] |(2.8412,-3.6171,-0.3393;3.6571,-2.9071,-0.276;4.7372,-3.2239,0.355;5.6151,-2.1501,0.2185;6.4858,-2.0742,0.7382;5.0706,-1.1077,-0.4319;5.5024,0.1907,-0.5277;6.8155,0.5897,0.0038;7.5529,-0.2506,0.5664;7.1539,1.9837,-0.1999;8.1247,2.323,0.1463;6.263,2.836,-0.7977;6.5417,3.8796,-0.9286;4.9564,2.4456,-1.2376;4.2647,3.1737,-1.646;4.5814,1.138,-1.0966;3.3275,0.7374,-1.4977;2.7144,-0.4338,-0.8744;2.329,-0.1101,0.5726;1.8202,-0.9636,1.0339;1.6496,0.7473,0.5857;3.204,0.1369,1.1799;1.4811,-0.7191,-1.7264;0.9492,-1.5994,-1.3494;1.7623,-0.896,-2.7693;0.799,0.1357,-1.6954;3.7585,-1.5732,-0.9833;3.8834,-1.7737,-2.0618)|",3.126588142245
"[H]O[C@@]([H])(C([H])([H])S[H])[C@]([H])(O[H])C1([H])SS1 |(-0.5195,-0.7609,-0.8594;-0.4912,0.1922,-1.0529;0.8187,0.6157,-0.7414;0.8955,1.6448,-1.1122;1.1222,0.5962,0.7685;2.1169,1.0116,0.96;1.1224,-0.4354,1.1344;-0.1031,1.4696,1.8215;-0.0386,2.6611,1.1875;1.8072,-0.2582,-1.5506;1.5272,-0.1582,-2.6068;1.6007,-1.5959,-1.0938;1.9422,-2.1996,-1.7721;3.2731,0.1197,-1.4056;3.6761,0.081,-0.3957;3.9041,1.4922,-2.4109;4.4275,-0.5501,-2.648)|",3.8803435081300006
"[H]C1=C([H])C([H])=C2C(=N1)[C@@]1([H])C(=O)[C@@]([H])(C2([H])[H])C([H])([H])C([H])([H])C1([H])[H] |(3.2231,2.1661,-0.4609;2.5716,1.2978,-0.3747;3.0699,0.0789,0.0877;4.1136,-0.0213,0.3701;2.189,-0.9939,0.1793;2.5335,-1.9607,0.541;0.8513,-0.8324,-0.1965;0.4548,0.4413,-0.6483;1.2982,1.4816,-0.7316;-0.9848,0.7393,-1.0641;-0.9573,1.3315,-1.9818;-1.7264,-0.5624,-1.3117;-2.3323,-0.8301,-2.3304;-1.5978,-1.5018,-0.1191;-2.2685,-2.3505,-0.2813;-0.1297,-1.9886,-0.1295;0.0672,-2.5952,0.7642;0.0217,-2.6516,-0.993;-1.9513,-0.7476,1.2028;-2.5979,-1.3801,1.8211;-1.0385,-0.5718,1.7866;-2.6373,0.5981,0.9184;-2.9,1.0997,1.8572;-3.5839,0.4049,0.3948;-1.7582,1.5266,0.0552;-1.025,2.0568,0.6723;-2.3859,2.2897,-0.4165)|",5.42595017897
"[H]C(=O)C([H])([H])[C@@]([H])(/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])C([H])([H])C([H])=C([H])[H] |(8.3567,0.589,2.6261;7.2452,0.5847,2.7298;6.7101,1.1988,3.6258;6.5247,-0.2199,1.6718;6.6914,0.3032,0.7182;5.4516,-0.2109,1.8892;7.0627,-1.672,1.5506;8.1525,-1.5958,1.4005;6.8318,-2.415,2.8513;7.2958,-1.9482,3.7206;6.1238,-3.5341,3.0287;5.6503,-4.0067,2.1681;5.892,-4.2544,4.3349;4.8048,-4.3597,4.4816;6.2591,-5.2861,4.2234;6.5049,-3.6126,5.5849;7.5938,-3.5392,5.4641;6.1395,-2.5806,5.669;6.1724,-4.351,6.8938;6.5544,-3.7536,7.7338;5.08,-4.3801,7.0152;6.7238,-5.7826,7.0234;6.351,-6.2076,7.965;6.3123,-6.4179,6.2274;8.2542,-5.8732,7.0082;8.5892,-6.9037,7.1733;8.6942,-5.25,7.797;8.6752,-5.5413,6.0522;6.4924,-2.3976,0.3086;6.8701,-3.4269,0.3108;5.3989,-2.461,0.4081;6.8425,-1.7506,-1.0054;6.3486,-0.806,-1.2357;7.6993,-2.2557,-1.8944;7.9204,-1.7487,-2.8299;8.2105,-3.2011,-1.7223)|",6.040927481100001
"[H]C(=O)C([H])([H])[C@]([H])(C([H])([H])C([H])=C([H])[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(9.9563,0.2799,1.6034;9.8426,-0.8256,1.5245;10.7064,-1.485,0.987;8.5758,-1.399,2.1163;8.6391,-1.3053,3.2113;8.5596,-2.4666,1.8728;7.2814,-0.6969,1.6295;7.37,-0.5397,0.5435;7.1073,0.7121,2.2646;7.9902,1.3235,2.041;6.2626,1.2075,1.7666;6.8715,0.7136,3.7522;5.9478,0.2476,4.0975;7.6953,1.2444,4.6582;7.4688,1.2249,5.721;8.6258,1.7308,4.3705;6.0715,-1.629,1.8539;6.2816,-2.5791,1.3432;5.9923,-1.8768,2.9222;4.7255,-1.0897,1.3502;4.4423,-0.1893,1.9126;4.8291,-0.7736,0.3013;3.591,-2.1176,1.4596;3.8565,-3.0159,0.8828;3.4982,-2.4463,2.5056;2.2354,-1.5872,0.9751;1.9697,-0.6911,1.554;2.3283,-1.2574,-0.0694;1.1086,-2.6193,1.0862;0.155,-2.21,0.7338;0.9686,-2.9433,2.1249;1.3287,-3.5125,0.4887)|",6.141609605785001
"[H]/C(C(=O)OC([H])([H])[H])=C(/OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])C(=O)OC([H])([H])[H] |(8.2248,1.7485,-0.6363;7.1456,1.7586,-0.7305;6.4188,0.5067,-0.4343;5.2562,0.2556,-0.6869;7.2554,-0.3759,0.1662;6.6689,-1.642,0.5014;7.469,-2.2216,0.9628;6.2977,-2.1441,-0.3963;5.8385,-1.5087,1.2002;6.5518,2.9158,-1.0812;5.2147,3.1109,-1.1199;4.5539,2.8724,-2.3839;4.9419,3.5838,-3.1231;4.7781,1.8506,-2.7096;3.0616,3.0469,-2.1487;2.7755,2.3605,-1.3434;2.8765,4.0661,-1.7845;2.2063,2.7676,-3.3952;2.4016,1.7455,-3.749;1.151,2.7849,-3.0943;2.4103,3.7588,-4.5484;1.7219,3.5464,-5.3742;3.428,3.7173,-4.9536;2.2273,4.7894,-4.2195;7.4354,4.1053,-1.3757;8.6457,4.1012,-1.2799;6.7135,5.1704,-1.7706;7.4824,6.3456,-2.0772;8.1821,6.1439,-2.8927;8.0453,6.6723,-1.199;6.7539,7.1013,-2.3711)|",5.107576973885
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"[H]O[C@@]1([H])C([H])=C(C([H])([H])[H])[C@]2([H])O[C@]2([H])[C@]1([H])Br |(4.6916,-2.9726,0.823;4.941,-2.8068,-0.101;4.5542,-1.4798,-0.4238;4.7688,-1.377,-1.4923;3.0737,-1.2645,-0.1872;2.4615,-2.1607,-0.2726;2.5145,-0.0684,0.04;1.0372,0.1417,0.2371;0.4889,-0.8039,0.1957;0.6282,0.8073,-0.5347;0.8269,0.6154,1.2055;3.3925,1.1389,0.0876;2.9432,2.0604,0.4581;4.291,1.3464,-1.0317;4.8547,0.9692,0.221;5.4367,1.7433,0.7211;5.3897,-0.4321,0.3405;5.4418,-0.703,1.3995;7.294,-0.4882,-0.2548)|",6.05725431213
"[H]O[C@@]([H])(C([H])=C([H])[H])C([H])([H])C([H])([H])N([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])=C([H])[H] |(5.0613,-1.4277,-0.7891;4.7418,-1.5142,0.124;3.3131,-1.389,0.0895;3.001,-1.5475,1.1284;2.9057,-0.0011,-0.339;3.2051,0.2818,-1.3515;2.2308,0.8671,0.414;1.9663,1.8575,0.0533;1.9177,0.6202,1.4265;2.702,-2.4863,-0.7978;2.9934,-2.3184,-1.845;3.1521,-3.4403,-0.4912;1.1762,-2.5805,-0.7245;0.7321,-1.6237,-1.0263;0.8675,-2.7445,0.3279;0.6745,-3.6124,-1.6293;1.0943,-4.5068,-1.3742;-0.7802,-3.7522,-1.6007;-1.1717,-3.9507,-0.5824;-1.2136,-2.7943,-1.9123;-1.2361,-4.8717,-2.5422;-0.8179,-5.8269,-2.1983;-0.8244,-4.6917,-3.5439;-2.7562,-5.0653,-2.6104;-3.1453,-5.1168,-1.5847;-3.0862,-6.3533,-3.1588;-2.8697,-6.3141,-4.1049;-3.5353,-4.0092,-3.3633;-4.6159,-4.1378,-3.2986;-3.0419,-3.0292,-4.1228;-3.6983,-2.3531,-4.6644;-1.9755,-2.8524,-4.2353)|",6.223243760935
"[H]C(COC([H])([H])[H])C([H])C1=C([H])C([H])=C(Cl)C([H])=C1[H] |(4.6918,-0.3335,-2.25;4.231,-0.8528,-1.4093;2.9541,-0.2893,-1.01;1.9593,-1.0623,-0.6772;1.9273,-2.5227,-0.8346;0.9176,-2.756,-1.1751;2.1033,-2.9818,0.1407;2.6784,-2.8479,-1.5557;4.9986,-1.6465,-0.6123;4.5305,-2.1143,0.2537;6.4304,-1.9034,-0.7429;7.0226,-2.8945,0.0645;6.4093,-3.4256,0.7885;8.3711,-3.2195,-0.0461;8.8099,-3.9876,0.5817;9.1579,-2.5397,-0.9747;10.8635,-2.9352,-1.1238;8.6097,-1.5398,-1.7809;9.238,-1.0079,-2.4875;7.2609,-1.2266,-1.659;6.8534,-0.4301,-2.2739)|",3.229991405435
"[H]O[C@@]([H])([C@@]([H])(O[H])C([H])=C([H])[H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(0.8752,2.9306,-0.7566;1.3675,2.2386,-1.2247;2.7516,2.3133,-0.8059;2.808,2.5672,0.2577;3.305,0.8781,-0.926;4.3229,0.9007,-0.516;2.5634,0.0457,-0.0428;1.6346,0.1746,-0.3038;3.347,0.3304,-2.3383;3.7853,0.985,-3.0879;2.8873,-0.8725,-2.6809;2.9484,-1.2311,-3.7047;2.4467,-1.5388,-1.9452;3.4312,3.436,-1.6151;3.0096,4.3857,-1.2268;3.1372,3.3265,-2.9993;2.2199,2.9994,-3.0376;4.9517,3.5087,-1.4665;5.3142,4.299,-2.1446;5.3922,2.5698,-1.8181;5.3177,3.692,-0.0572;5.1164,4.6483,0.2323;6.3212,3.5687,0.0584)|",7.27088208536
"[H]N([H])C1=NC(=O)N=C2C1=NC(=O)N2C([H])([H])[H] |(2.0797,-0.9466,-5.1455;1.0962,-0.7211,-5.1789;0.5947,-0.7581,-6.0551;0.4376,-0.3725,-4.0575;-0.8284,-0.0776,-4.0539;-1.486,0.2895,-2.8567;-2.6644,0.5739,-2.8878;-0.8181,0.3445,-1.5763;0.4269,0.0489,-1.6103;1.2028,-0.3344,-2.8044;2.441,-0.58,-2.5667;2.593,-0.3688,-1.1296;3.6194,-0.4962,-0.5134;1.3469,0.0127,-0.5949;1.1014,0.2994,0.8095;2.0532,0.564,1.2723;0.6803,-0.5742,1.3169;0.3968,1.1302,0.884)|",3.2544816519799995
"[H]O[C@@]1([H])C(=C([H])[H])C([H])([H])C([H])([H])[C@@]2(C([H])([H])[H])[C@@]([H])(C([H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C(=O)[C@@]2([H])C1([H])[H] |(-0.98,5.1372,0.3106;-0.8455,5.3149,1.2561;-0.3001,4.1299,1.8308;-0.1017,4.4058,2.8771;-1.2808,2.967,1.8189;-2.5055,3.0966,1.299;-3.2058,2.2652,1.2879;-2.8596,4.0415,0.9004;-0.827,1.661,2.4368;-0.5584,1.8289,3.4889;-1.6876,0.9832,2.4571;0.3344,0.9427,1.7023;0.1677,1.0556,0.6281;0.2507,-0.1316,1.9097;1.767,1.3767,2.0821;2.0275,0.9674,3.5549;1.8827,-0.1109,3.6921;1.3566,1.4861,4.2478;3.0511,1.2062,3.866;2.9154,0.6715,1.227;3.1861,-0.2488,1.7596;2.7084,0.2163,-0.253;3.7223,-0.0904,-0.5531;1.8434,-1.0469,-0.4145;1.9667,-1.4576,-1.4239;0.7768,-0.854,-0.2716;2.1415,-1.8264,0.2972;2.2894,1.3058,-1.2554;2.3885,0.9265,-2.2794;2.9121,2.2043,-1.1816;1.245,1.6092,-1.1274;4.0869,1.6756,1.3182;4.8059,1.6207,0.4955;4.6593,1.535,2.2458;3.4449,3.0513,1.3849;3.9567,4.093,1.0329;2.0216,2.9194,1.9538;2.073,3.3579,2.9627;1.0507,3.8086,1.1562;0.8656,3.3933,0.1574;1.5603,4.7671,1.011)|",5.934803079405
"[H]ONC([H])C1=C(C#CC2=C([H])C([H])=C([H])C([H])=C2[H])C([H])=C(F)C([H])=C1[H] |(3.3844,3.2743,2.852;4.3426,3.211,2.7161;4.4402,2.236,1.709;5.6725,2.0394,1.4117;6.4412,2.6141,1.9359;6.1483,1.0924,0.403;5.329,0.2158,-0.3676;3.9153,0.1489,-0.2445;2.7058,0.0257,-0.2219;1.2872,-0.058,-0.1412;0.5672,-0.9559,-0.9541;1.1085,-1.5901,-1.6493;-0.8203,-1.0269,-0.8646;-1.3653,-1.7231,-1.4962;-1.5111,-0.2088,0.0328;-2.594,-0.2672,0.1;-0.806,0.6848,0.8442;-1.3414,1.3215,1.5433;0.5806,0.7631,0.761;1.1406,1.4518,1.3854;5.9386,-0.6418,-1.3045;5.3315,-1.3155,-1.8981;7.3132,-0.6289,-1.4705;7.8661,-1.4614,-2.3749;8.1367,0.2139,-0.7319;9.2101,0.1971,-0.8855;7.5387,1.0604,0.1942;8.1665,1.7255,0.7811)|",4.046332956935
"[H]C1=NC(=O)N=C2O/C(C([H])([H])C([H])([H])C([H])([H])[H])=C(/[H])[C@@]12[H] |(8.8008,-0.1181,0.4226;8.5015,0.7503,1.0136;9.2783,1.7578,1.074;8.8572,2.8653,1.9155;9.6862,3.5195,2.4975;7.4736,3.1695,1.9475;6.7284,2.1435,1.8728;5.3821,2.2179,1.7012;4.9415,0.9413,1.2978;3.4829,0.8583,1.011;3.2357,1.5862,0.2256;3.271,-0.1375,0.6042;2.5985,1.1283,2.2464;2.841,0.3947,3.026;2.8483,2.1143,2.6549;1.1056,1.0632,1.9135;0.4971,1.2587,2.8029;0.8249,0.0758,1.5272;0.8354,1.8073,1.1545;5.9338,0.0434,1.2633;5.8283,-1.0028,1.0105;7.1826,0.7078,1.7764;7.4313,0.3267,2.7835)|",4.810972876839999
"[H]C1=C([H])C([H])=C([C@@]([H])(C([H])([H])N(=O)=O)[C@@]2([H])N([H])C([H])([H])C([H])([H])C2([H])[H])C([H])=C1[H] |(-2.9465,-4.7469,1.8345;-1.9967,-4.2885,1.5725;-1.9684,-3.0621,0.9078;-2.896,-2.5592,0.6476;-0.7459,-2.4769,0.5751;-0.732,-1.52,0.0577;0.4668,-3.1022,0.897;1.7805,-2.424,0.5344;1.5396,-1.4812,0.0269;2.5172,-2.0516,1.8405;2.853,-2.9421,2.3698;1.8932,-1.4212,2.4747;3.754,-1.2456,1.5444;3.6026,-0.0503,1.3097;4.8324,-1.841,1.5141;2.7129,-3.2274,-0.4158;3.5122,-2.5349,-0.7196;3.2984,-4.4222,0.2349;4.246,-4.2165,0.5347;3.2613,-5.5571,-0.714;4.2113,-6.1026,-0.7072;2.4779,-6.2651,-0.4093;2.9203,-4.9501,-2.0854;3.8318,-4.6006,-2.5871;2.4194,-5.6571,-2.7552;2.0353,-3.7562,-1.6958;1.0169,-4.0965,-1.4779;1.973,-2.987,-2.4724;0.4265,-4.3346,1.5691;1.3586,-4.836,1.8082;-0.7958,-4.9214,1.8993;-0.8086,-5.8771,2.417)|",4.193274436205
"[H]O[C@@]1(C([H])([H])[H])C([H])([H])C([H])([H])O[C@@]([H])(/C([H])=C(\[H])C2=C([H])C([H])=C([H])C([H])=C2[H])C1([H])[H] |(2.1095,0.9071,1.5926;2.5109,0.101,1.2295;2.1881,0.0553,-0.1748;0.6643,0.04,-0.3363;0.3686,-0.0204,-1.3897;0.2237,0.9569,0.0789;0.2354,-0.8153,0.1968;2.826,1.2581,-0.8977;2.5008,1.267,-1.9457;2.4785,2.1969,-0.4409;4.3519,1.1754,-0.8483;4.701,1.2673,0.1899;4.807,1.9832,-1.4299;4.8194,-0.0308,-1.4382;4.3555,-1.2296,-0.7897;4.6692,-2.0248,-1.4755;5.0329,-1.4708,0.5391;4.6899,-0.8699,1.3762;5.9985,-2.3891,0.6956;6.303,-2.9662,-0.1797;6.728,-2.7232,1.9288;7.786,-3.6472,1.8631;8.0393,-4.0918,0.9031;8.5143,-3.9969,2.9997;9.3292,-4.7118,2.92;8.1966,-3.4307,4.2348;8.7599,-3.7009,5.1239;7.1437,-2.514,4.3197;6.8851,-2.0715,5.2783;6.4183,-2.1653,3.184;5.598,-1.4587,3.2712;2.8111,-1.2429,-0.7183;2.4736,-2.0887,-0.1078;2.4286,-1.3952,-1.7351)|",5.069481034815
"[H]OC(=O)[C@@]([H])(O[H])[C@]([H])(OC(=O)C([H])([H])[H])C(=O)OC([H])([H])[H] |(8.5974,2.5299,-2.6714;8.025,2.8157,-3.4144;6.991,1.9538,-3.4547;6.056,2.0817,-4.2044;7.1469,0.7805,-2.4644;7.695,-0.0164,-2.979;7.9642,1.1703,-1.3618;7.403,1.7413,-0.8033;5.8055,0.1832,-2.0049;6.0048,-0.457,-1.1375;5.2637,-0.6003,-3.058;4.5346,-1.6988,-2.6847;4.4407,-2.0766,-1.5416;3.8911,-2.3271,-3.8916;3.0811,-1.6763,-4.2389;3.4856,-3.3028,-3.6222;4.6106,-2.4216,-4.7097;4.7746,1.2307,-1.5781;3.5947,1.1952,-1.8029;5.3893,2.2019,-0.8479;4.5288,3.2618,-0.3742;3.7461,2.8505,0.2669;4.0741,3.7782,-1.222;5.1762,3.9349,0.1874)|",7.05863328197
"[H]OC(=O)[C@@]([H])(OC(=O)C([H])([H])[H])[C@]([H])(O[H])C(=O)OC([H])([H])[H] |(4.661,1.5607,3.3174;5.344,0.8699,3.4214;5.9178,0.6755,2.2216;6.7962,-0.1328,2.0435;5.4013,1.5538,1.0799;5.0364,0.9048,0.2793;4.3196,2.3696,1.5616;3.2335,2.5308,0.7266;3.1023,1.932,-0.3098;2.2772,3.5384,1.3069;2.757,4.5228,1.327;2.0145,3.2771,2.3369;1.3788,3.5801,0.6909;6.4898,2.5006,0.4993;6.8732,3.1466,1.293;5.9214,3.3335,-0.4848;5.8524,2.7912,-1.2927;7.618,1.6774,-0.1234;7.4884,1.1566,-1.2129;8.7102,1.6342,0.6376;9.7626,0.7652,0.1705;10.0775,1.0553,-0.8344;9.4071,-0.2675,0.1641;10.5766,0.8878,0.8842)|",7.004210511869999
"[H]O[C@@]([H])(C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(O[H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(3.2757,-3.2653,-1.7866;2.9283,-2.3723,-1.6328;2.9571,-2.1432,-0.217;2.4185,-1.195,-0.0997;2.1651,-3.2519,0.4992;1.1767,-3.2955,0.022;2.645,-4.2193,0.2877;1.9969,-3.1112,2.0211;1.4648,-4.0008,2.3877;2.9722,-3.1165,2.52;1.223,-1.8597,2.4604;0.2358,-1.8445,1.9772;1.7512,-0.9561,2.1344;1.0288,-1.7831,3.976;0.6022,-2.7449,4.3137;2.3367,-1.6237,4.5427;2.264,-1.7521,5.5006;0.0601,-0.6656,4.4336;-0.9029,-0.875,3.9437;-0.1702,-0.733,5.9524;-0.9409,-0.0199,6.265;0.743,-0.477,6.5065;-0.4934,-1.7328,6.2697;0.528,0.7365,4.0177;-0.1387,1.505,4.4258;0.5439,0.8574,2.9296;1.5395,0.9277,4.3925;4.4014,-1.9196,0.2956;4.3305,-1.7114,1.3726;5.3013,-3.1526,0.1129;6.3103,-2.9501,0.4892;5.4028,-3.4171,-0.9478;4.9226,-4.0305,0.6484;5.0203,-0.689,-0.3837;6.0399,-0.5093,-0.0232;4.43,0.2133,-0.1823;5.056,-0.8268,-1.4694)|",8.5987976758
"[H]O[C@](/C([H])=C(\[H])C([H])([H])[H])(C([H])([H])[H])C([H])([H])C1=NC([H])=C([H])C([H])=C1[H] |(4.673,-1.8224,-2.5059;5.0387,-1.8314,-1.6063;4.513,-0.6632,-0.9357;3.0019,-0.774,-0.9379;2.639,-1.7387,-0.5766;2.1171,0.1397,-1.3472;2.4713,1.1043,-1.7102;0.6266,-0.0491,-1.3444;0.1333,0.7029,-0.7137;0.3456,-1.0405,-0.9733;0.209,0.0676,-2.3537;5.0493,0.597,-1.6218;4.7585,1.5107,-1.0967;4.6818,0.6568,-2.6536;6.1418,0.5568,-1.6462;5.0737,-0.8192,0.5054;4.7416,-1.7957,0.8756;6.1649,-0.8503,0.4251;4.6831,0.2653,1.482;5.4564,1.3657,1.5093;5.1386,2.3415,2.3679;5.7923,3.2129,2.3583;4.0532,2.2849,3.2419;3.849,3.1064,3.9222;3.2521,1.1441,3.215;2.3968,1.0496,3.8791;3.5702,0.1228,2.3235;2.9706,-0.7814,2.2783)|",6.059975450635
"[H]OC([H])([H])C([H])([H])[C@]1([H])O[C@@]([H])(C([H])([H])C([H])([H])[H])C(C([H])([H])[H])=C([H])C1([H])[H] |(5.8554,0.542,-0.1388;6.64,0.276,0.3742;6.5717,1.0165,1.5826;5.927,0.5115,2.3246;7.5852,1.0322,2;6.0731,2.4517,1.3759;6.6652,2.9244,0.5814;6.2354,3.0318,2.2947;4.5892,2.5359,1.019;4.0042,2.0536,1.8187;4.4,1.7991,-0.2038;3.0311,1.605,-0.5925;3.0982,1.3612,-1.6632;2.4102,0.3878,0.1261;2.3648,0.5917,1.2039;1.3701,0.2855,-0.2072;3.1603,-0.9227,-0.1353;2.6786,-1.7561,0.3879;4.1997,-0.8714,0.2026;3.1693,-1.1644,-1.2057;2.2149,2.8785,-0.4558;0.8525,2.889,-1.1026;0.405,3.8866,-1.0533;0.1591,2.1893,-0.6179;0.9084,2.5945,-2.1598;2.7168,3.948,0.1727;2.1313,4.866,0.2144;4.0714,3.9616,0.8292;4.019,4.4668,1.8037;4.7838,4.5385,0.2198)|",7.102171498050001
"[H]O[C@@]([H])(C([H])([H])/C([H])=C(\[H])[C@@]([H])(O[H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(1.0976,-2.3644,5.0613;1.0993,-3.2843,4.7515;2.1036,-3.389,3.7357;2.2099,-4.4693,3.5706;1.5862,-2.7657,2.4151;2.2923,-2.9721,1.6026;0.6528,-3.2914,2.1712;1.3112,-1.289,2.4977;0.558,-0.9821,3.2268;1.8844,-0.3439,1.7462;2.6296,-0.6386,1.0034;1.5644,1.1274,1.8304;0.8402,1.2775,2.6401;2.7097,1.8927,2.2354;3.43,1.6927,1.6168;0.9297,1.6932,0.5302;0.0797,1.0366,0.2947;0.4018,3.115,0.7674;-0.0523,3.5229,-0.1434;1.2162,3.7803,1.0723;-0.3591,3.1316,1.5571;1.8957,1.6634,-0.6652;1.4007,2.0408,-1.5667;2.2529,0.6518,-0.889;2.7677,2.3058,-0.4865;3.4605,-2.8397,4.2256;3.3233,-1.7644,4.4245;3.8844,-3.5233,5.5352;4.7988,-3.067,5.9315;4.0885,-4.5892,5.3688;3.1025,-3.4517,6.2958;4.5657,-2.9779,3.1666;5.5307,-2.6632,3.5802;4.3722,-2.3653,2.2808;4.6735,-4.0214,2.8419)|",6.870874725125001
"[H]OC([H])([H])/C([H])=C(\[H])C([H])([H])[C@@]([H])(O[H])C([H])([H])C([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(6.7662,-3.2015,3.5693;7.3565,-2.6584,4.1151;6.8495,-1.3245,4.0825;5.8521,-1.2589,4.547;7.5394,-0.749,4.711;6.8122,-0.7556,2.6909;7.761,-0.7682,2.1523;5.7216,-0.2365,2.1117;4.7869,-0.2361,2.6763;5.6713,0.3241,0.7149;6.684,0.5714,0.377;5.0895,1.2548,0.7053;5.0744,-0.6678,-0.3164;5.1267,-0.19,-1.303;5.9058,-1.82,-0.428;6.0521,-2.1445,0.477;3.6166,-1.0806,-0.053;3.358,-1.8409,-0.7995;3.5407,-1.5753,0.9254;2.5953,0.0733,-0.1281;2.7176,0.5937,-1.0878;2.8077,0.8122,0.655;1.1649,-0.4045,0.0123;0.4367,-0.831,-1.1068;0.8975,-0.7869,-2.0917;-0.8689,-1.3052,-0.9761;-1.4171,-1.6271,-1.8579;-1.4719,-1.3608,0.2819;-2.4897,-1.7271,0.3854;-0.7595,-0.9371,1.4049;-1.2216,-0.9705,2.3883;0.5461,-0.4632,1.268;1.0919,-0.1287,2.1483)|",6.372906378710001
"[H]OC([H])([H])C([H])([H])/C([H])=C(\[H])[C@@]([H])(O[H])C([H])([H])C([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(8.7578,-1.4494,-0.228;9.4203,-2.1252,-0.4492;9.3052,-2.3653,-1.8415;9.6649,-1.503,-2.4287;9.9671,-3.2097,-2.0587;7.8622,-2.7093,-2.2576;7.8569,-2.9663,-3.3266;7.5437,-3.5985,-1.7006;6.9245,-1.5617,-1.9957;7.0242,-0.6923,-2.647;6.0457,-1.5031,-0.9888;5.9453,-2.3546,-0.3135;5.1177,-0.3396,-0.7122;5.4531,0.176,0.1998;5.1847,0.6661,-1.7215;4.9063,0.2477,-2.5535;3.6726,-0.8226,-0.4831;3.3081,-1.2958,-1.4072;3.6728,-1.6103,0.2823;2.7143,0.3084,-0.0655;2.7674,1.1048,-0.8157;3.0714,0.7441,0.8777;1.2861,-0.1665,0.0962;0.3699,-0.0627,-0.9588;0.6846,0.3964,-1.894;-0.938,-0.5311,-0.8245;-1.6335,-0.4366,-1.6546;-1.3537,-1.1133,0.3741;-2.3725,-1.4755,0.4825;-0.4525,-1.2212,1.4351;-0.7679,-1.6675,2.3748;0.8537,-0.7515,1.2946;1.5477,-0.8336,2.1291)|",6.4246080103050005
"[H]/C(=C(/[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])C(F)(F)C(=O)N(C([H])([H])C([H])([H])[H])C([H])([H])C([H])([H])[H] |(6.9035,2.1423,-2.6751;6.032,2.6219,-3.1091;5.3133,3.524,-2.4395;4.4333,3.9529,-2.9166;5.6058,3.9918,-1.0428;6.534,3.5321,-0.68;5.7769,5.0793,-1.0559;4.4543,3.6888,-0.064;3.524,4.1313,-0.4488;4.2882,2.6034,-0.033;4.7163,4.2117,1.354;5.6485,3.7709,1.7344;4.8862,5.2973,1.3152;3.5726,3.908,2.327;3.7899,4.2927,3.33;3.4025,2.8279,2.4136;2.6334,4.3646,1.9913;5.7097,2.1903,-4.5058;4.5659,2.8177,-4.9509;6.7281,2.6013,-5.3572;5.5764,0.6497,-4.6535;5.8682,-0.0525,-3.6882;5.157,0.1396,-5.844;5.0613,-1.3251,-5.9403;4.6957,-1.7025,-4.9829;4.3105,-1.5535,-6.7038;6.4008,-1.9813,-6.285;6.2782,-3.0664,-6.3798;7.1303,-1.7844,-5.4947;6.7981,-1.6004,-7.2328;4.8735,0.9089,-7.0646;5.3837,1.8675,-7.0066;5.3282,0.3637,-7.9013;3.378,1.1132,-7.319;3.2296,1.6537,-8.2612;2.9324,1.6977,-6.5097;2.8425,0.1601,-7.3937)|",6.413723456285
"[H]OC(=O)/C([H])=N/N([H])C1=C([H])C([H])=C(C#N)C([H])=C1[H] |(1.6145,-6.1533,-0.6065;1.2771,-5.2455,-0.492;-0.0443,-5.3836,-0.2392;-0.6052,-6.4613,-0.1845;-0.7579,-4.1108,-0.0334;-1.8282,-4.2063,0.1714;-0.1427,-2.9805,-0.0923;-0.8382,-1.8638,0.1037;-1.8382,-1.9177,0.2958;-0.2391,-0.6055,0.0551;-1.0392,0.5281,0.2704;-2.1018,0.4144,0.471;-0.4769,1.7958,0.228;-1.097,2.6704,0.3947;0.8956,1.9537,-0.0293;1.4779,3.2603,-0.0735;1.9508,4.3233,-0.109;1.6896,0.8117,-0.243;2.7503,0.929,-0.44;1.1338,-0.4582,-0.2021;1.7409,-1.34,-0.3663)|",4.19599557471
"[H]C1NN2C([H])([H])C([H])([H])C([H])([H])[C@]2([H])[C@]1([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(3.0508,-1.8277,1.7527;3.432,-0.9059,1.3248;4.5689,-0.4403,1.6935;4.8783,0.6885,0.9314;5.4853,1.7875,1.7171;5.5542,1.4762,2.7636;6.4983,2.001,1.3592;4.539,2.9994,1.534;4.4809,3.6174,2.4353;4.8964,3.6415,0.7198;3.1887,2.3666,1.1528;2.5156,3.0573,0.6358;2.6715,2.0057,2.0491;3.6241,1.1801,0.2774;3.8521,1.529,-0.7353;2.7455,-0.1067,0.235;2.936,-0.6015,-0.7295;1.2463,0.0747,0.3735;0.4494,0.2236,-0.7704;0.9157,0.1881,-1.7529;-0.9287,0.4116,-0.6669;-1.5273,0.5255,-1.567;-1.5379,0.446,0.5891;-2.612,0.5878,0.6725;-0.7582,0.2899,1.7359;-1.2234,0.3069,2.7181;0.6213,0.107,1.628;1.2193,-0.0219,2.5265)|",5.412344486445
"[H]OC(=O)[C@@]([H])(/C([H])=C(\C([H])([H])[H])[C@]1([H])O[C@@]([H])(C([H])([H])C([H])([H])[H])C(C([H])([H])[H])=C([H])C1([H])[H])C([H])([H])[H] |(5.6187,-3.89,7.1123;4.9396,-3.4908,7.6804;4.1263,-2.6879,6.9384;3.2038,-2.1187,7.4651;4.5241,-2.5254,5.4638;4.9915,-3.4591,5.1237;3.3091,-2.2076,4.6287;2.5885,-1.5818,5.1459;3.0652,-2.5969,3.3699;3.9988,-3.4405,2.5335;4.8506,-3.8308,3.0975;4.3917,-2.8617,1.6893;3.4646,-4.2955,2.0982;1.7943,-2.1987,2.6185;1.3638,-3.1222,2.2099;2.1227,-1.4461,1.4389;2.6596,-0.1402,1.6663;3.7147,-0.2337,1.9875;2.6564,0.5455,0.2888;3.212,1.4884,0.3634;3.2256,-0.0999,-0.3912;1.2581,0.7924,-0.2862;1.3224,1.2498,-1.2799;0.6703,1.4606,0.3535;0.7099,-0.1496,-0.3767;1.921,0.6298,2.7478;2.3001,2.0755,2.9441;1.7757,2.5036,3.8045;2.0649,2.6905,2.0662;3.3793,2.1818,3.1241;1.0265,0.0112,3.5268;0.5066,0.5759,4.3002;0.7071,-1.4551,3.4053;-0.2491,-1.589,2.879;0.5697,-1.9072,4.3947;5.5871,-1.3977,5.377;5.9376,-1.2962,4.3457;6.4499,-1.6072,6.0204;5.1516,-0.4434,5.6891)|",6.288551085055
"[H]C1([H])C2=C(C([H])([H])C([H])([H])C1([H])[H])C([H])([H])[C@@]1(C#N)C([H])([H])C([H])([H])C([H])([H])[C@]1([H])C2([H])[H] |(-1.7748,-1.0432,-1.7705;-1.7195,-1.0494,-0.6696;-1.8015,-2.1075,-0.3826;-0.3611,-0.5237,-0.2449;-0.202,0.7,0.2965;-1.3672,1.6424,0.5354;-1.5733,1.6916,1.6178;-1.0702,2.662,0.2517;-2.6421,1.2453,-0.2189;-3.4961,1.8236,0.1543;-2.5282,1.4989,-1.282;-2.896,-0.2576,-0.0861;-3.8282,-0.5434,-0.5884;-3.0172,-0.5129,0.9761;1.1509,1.2424,0.7357;1.2419,1.1802,1.8315;1.2198,2.3098,0.4889;2.3252,0.4688,0.1116;2.4512,0.837,-1.3133;2.5741,1.1288,-2.4312;3.714,0.6027,0.7885;4.2372,1.5226,0.512;3.5547,0.6279,1.8732;4.4831,-0.6853,0.3771;5.204,-0.4701,-0.4173;5.0539,-1.0803,1.2234;3.4056,-1.6991,-0.1189;3.5215,-2.6906,0.3307;3.474,-1.8274,-1.2054;2.0598,-1.0526,0.2466;1.88,-1.2085,1.3211;0.8012,-1.4697,-0.5054;1.0029,-1.5112,-1.5876;0.5127,-2.4905,-0.2179)|",6.759308046419999
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"[H]OC([H])([H])/C([H])=C(\[H])[C@@]1([H])O[C@@]([H])(C([H])([H])[H])OC1([H])[H] |(2.5886,2.8007,-5.7842;3.0444,2.2249,-6.4186;2.8459,0.8824,-5.9788;1.7869,0.5851,-6.0446;3.4033,0.2661,-6.6942;3.3595,0.6653,-4.5833;4.4006,0.9464,-4.4182;2.626,0.1912,-3.5733;1.5792,-0.0743,-3.7283;3.1463,-0.0579,-2.1938;4.1521,0.3767,-2.079;2.259,0.5124,-1.2265;2.5217,-0.1844,-0.0257;3.4264,0.2356,0.4513;1.3292,-0.1106,0.901;1.5262,-0.69,1.8079;0.4489,-0.5215,0.3988;1.1326,0.9286,1.1821;2.7888,-1.5362,-0.395;3.1666,-1.5478,-1.7684;4.16,-1.9949,-1.8936;2.4401,-2.14,-2.3401)|",6.819173093530001
"[H]C1=C([H])C([H])=C(/C([H])=C2\C(=O)SC(=O)C2([H])[H])C([H])=C1[H] |(-2.8001,-0.2034,0.132;-1.7184,-0.2834,0.0663;-1.1174,-1.5244,-0.1567;-1.7333,-2.4128,-0.2664;0.267,-1.6431,-0.2436;0.7201,-2.6084,-0.4173;1.0902,-0.5047,-0.1068;2.5498,-0.4686,-0.1756;2.912,0.5571,-0.0828;3.5698,-1.361,-0.3149;3.485,-2.8345,-0.4389;2.5086,-3.5463,-0.5161;5.1576,-3.5839,-0.4558;6.0005,-1.9906,-0.3269;7.1955,-1.865,-0.2599;4.996,-0.8477,-0.3335;5.2258,-0.2023,0.523;5.2055,-0.2537,-1.2342;0.4614,0.7429,0.1151;1.0759,1.6339,0.2208;-0.921,0.8551,0.2027;-1.3756,1.8265,0.3754)|",4.17422646667
"[H]OC([H])([H])C#C[C@@](O[H])(/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])[H] |(-1.8708,-1.8482,-0.2034;-2.2956,-1.2555,-0.8435;-1.2727,-0.7558,-1.6988;-0.902,-1.5373,-2.3822;-1.7575,0.0057,-2.3198;-0.1383,-0.1744,-0.9698;0.7848,0.2467,-0.3076;1.9101,0.8067,0.475;1.4141,1.516,1.6156;0.7135,2.1086,1.3009;2.7831,-0.3301,0.9748;3.2291,-0.9249,0.1807;3.0014,-0.6019,2.2674;2.5251,0.0415,3.0031;3.8281,-1.688,2.8167;4.417,-2.6917,2.0253;4.2632,-2.6921,0.9499;5.1879,-3.6972,2.601;5.629,-4.4636,1.9689;5.3915,-3.7286,3.9841;5.9929,-4.5156,4.431;4.8126,-2.7437,4.7845;4.962,-2.7571,5.861;4.0394,-1.738,4.2057;3.5906,-0.9735,4.8357;2.7346,1.7647,-0.4145;3.1876,1.2355,-1.2594;3.5239,2.2188,0.1917;2.0899,2.5552,-0.8151)|",5.074923311825
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"[H]OC(NNC([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])[H] |(1.107,-0.6557,-1.6106;1.3597,-0.9952,-0.7388;2.5859,-0.5005,-0.4164;3.0294,-0.8588,0.7336;4.3017,-0.3328,1.0114;4.781,-0.7762,2.1142;4.204,-1.4921,2.7096;6.1289,-0.3354,2.6009;6.5966,0.2665,1.8137;6.7553,-1.2285,2.7488;6.1097,0.4694,3.9289;7.1607,0.7071,4.1489;5.351,1.7946,3.7737;5.4085,2.3891,4.6934;4.2914,1.6214,3.552;5.7648,2.3971,2.9566;5.5674,-0.3514,5.1089;5.6683,0.2069,6.0471;6.1096,-1.2981,5.2239;4.504,-0.5873,4.9827;3.2745,0.3616,-1.4498;2.5183,0.9591,-1.9807;3.9366,1.0493,-0.92;4.1052,-0.4523,-2.4678;4.6278,0.2598,-3.1193;4.8793,-1.0003,-1.9187;3.2859,-1.4231,-3.325;3.9256,-1.9438,-4.046;2.5141,-0.8931,-3.9001;2.7903,-2.1856,-2.7139)|",5.64908353638
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"[H]O/C(=N/NC([H])C([H])([H])C([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(4.8599,0.2688,-2.2539;4.2832,1.0045,-2.0055;2.9826,0.6318,-2.1396;2.1631,1.5756,-1.8372;0.8041,1.2542,-1.9481;0.0499,2.244,-1.6442;0.4956,3.1975,-1.3363;-1.4427,2.1245,-1.6754;-1.8559,2.8956,-2.3414;-1.7186,1.1494,-2.09;-2.0735,2.303,-0.2695;-1.7448,3.262,0.1508;-1.6813,1.5197,0.3905;-3.5859,2.248,-0.3001;-4.3466,3.4191,-0.4142;-3.8411,4.3821,-0.4505;-5.74,3.3674,-0.4765;-6.3113,4.2883,-0.5605;-6.3978,2.1374,-0.4246;-7.4828,2.0948,-0.4689;-5.6523,0.9625,-0.3091;-6.1556,0.0002,-0.2621;-4.2597,1.02,-0.247;-3.6857,0.1004,-0.1521;2.6934,-0.812,-2.6154;1.8821,-1.5566,-1.5293;1.6916,-2.5868,-1.8542;0.9284,-1.0587,-1.3519;2.4354,-1.5979,-0.5835;3.9982,-1.6031,-2.863;3.7466,-2.6161,-3.1935;4.603,-1.713,-1.9526;4.6153,-1.1596,-3.6561;1.9014,-0.767,-3.9429;1.6996,-1.7893,-4.2854;2.4738,-0.2557,-4.7261;0.9534,-0.2452,-3.8096)|",5.44771928701
"[H]C([H])=C([H])C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])[H])C([H])([H])C(=O)N(OC([H])([H])[H])C([H])([H])[H] |(1.9165,-4.3487,-0.8026;2.0713,-3.4437,-0.2209;1.6992,-3.4543,0.8019;2.6768,-2.3716,-0.7343;3.0323,-2.4109,-1.7664;2.9272,-1.0751,-0.012;2.5019,-1.1276,0.9994;2.3926,-0.2684,-0.5356;4.4201,-0.6881,0.0699;4.4889,0.3372,0.453;4.8418,-0.6646,-0.9461;5.2509,-1.6363,0.9444;5.0798,-2.6662,0.602;4.8723,-1.5955,1.978;6.7675,-1.3566,0.9449;7.1287,-1.4098,-0.0908;7.1114,0.038,1.4918;6.664,0.8313,0.884;8.1935,0.1942,1.4924;6.7422,0.1567,2.52;7.4946,-2.4525,1.7437;7.0754,-3.4398,1.5031;7.3321,-2.323,2.8208;8.9941,-2.5056,1.4731;9.5615,-1.815,0.6361;9.7115,-3.4565,2.1885;9.0901,-3.9594,3.3523;8.821,-5.3548,3.2042;8.3761,-5.6629,4.1538;9.7421,-5.9245,3.0297;8.1176,-5.542,2.3844;11.1559,-3.3554,2.3264;11.5392,-2.896,1.4156;11.5876,-4.3536,2.4447;11.4266,-2.7388,3.1927)|",7.055912143465
"[H]C([H])=C([H])C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])[H])C([H])([H])C(=O)OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(2.0243,3.07,-5.5591;2.319,2.5099,-4.6755;2.1447,1.4356,-4.7016;2.8607,3.1049,-3.6119;3.0182,4.185,-3.632;3.3055,2.4055,-2.3565;3.0459,1.3398,-2.4148;2.7549,2.8168,-1.4962;4.8153,2.5613,-2.0869;5.061,3.6301,-2.0545;5.3692,2.1326,-2.9322;5.2404,1.8969,-0.7685;4.8984,0.8518,-0.7642;4.707,2.3906,0.0558;6.7548,1.8975,-0.4568;6.8672,1.4877,0.5561;7.5528,0.9974,-1.4123;7.1551,-0.0243,-1.4082;8.6075,0.9469,-1.117;7.5163,1.3655,-2.4442;7.3729,3.319,-0.4344;7.3397,3.7702,-1.4299;8.4226,3.2414,-0.1286;6.6583,4.2596,0.5124;5.9013,5.1481,0.1783;6.9455,3.9594,1.8021;6.3024,4.7651,2.8181;5.2896,5.01,2.4872;6.2503,4.1115,3.6937;7.1022,6.0284,3.1174;8.1331,5.7451,3.3688;7.1473,6.6392,2.2076;6.4901,6.8469,4.2626;5.4539,7.11,4.0086;6.4362,6.2264,5.1687;7.28,8.124,4.5662;6.8224,8.6891,5.3858;7.3212,8.7808,3.6891;8.3122,7.8921,4.8556)|",7.04774872795
"[H]C([H])=C([H])C([H])([H])C([H])([H])C([H])([H])/C(=C(\[H])C(=O)OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])C([H])([H])[H] |(3.7437,6.3414,0.0968;3.5604,5.3409,-0.2862;2.6489,5.2033,-0.8653;4.4089,4.3356,-0.0657;5.3122,4.5296,0.5137;4.2185,2.9243,-0.5552;5.1114,2.6106,-1.1153;3.3788,2.8973,-1.2611;3.9465,1.8989,0.5674;3.7211,0.928,0.1084;3.0434,2.2007,1.1126;5.0898,1.7289,1.5943;4.7545,1.0027,2.3494;5.2529,2.6754,2.1217;6.3967,1.2364,1.0073;7.489,2.0264,1.0741;7.409,2.9991,1.5516;8.8407,1.7188,0.562;9.2105,0.7166,-0.026;9.6734,2.7572,0.8454;11.039,2.6333,0.3946;11.6129,3.2614,1.0823;11.3579,1.593,0.5035;11.1991,3.1058,-1.0474;10.5876,2.4671,-1.6961;10.8036,4.1269,-1.1322;12.6614,3.0713,-1.5121;13.2693,3.7125,-0.8579;13.0567,2.0525,-1.396;12.8324,3.5208,-2.9668;13.8843,3.4901,-3.2721;12.4731,4.5474,-3.1087;12.267,2.8747,-3.6489;6.3695,-0.1375,0.3896;5.8882,-0.8469,1.0758;7.3679,-0.4892,0.1364;5.7644,-0.1338,-0.5272)|",5.8831014478100006
"[H]C([H])=C([H])/C([H])=C(\[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])O[Si](C([H])([H])[H])(C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(3.8126,3.1208,-4.7949;4.7312,2.9136,-4.2545;5.4154,2.2005,-4.7093;5.011,3.509,-3.0856;4.2959,4.2183,-2.6666;6.2204,3.2827,-2.3055;6.9428,2.5825,-2.7194;6.4668,3.9048,-1.1408;5.719,4.6169,-0.7809;7.654,3.7587,-0.2111;8.333,5.1362,-0.0499;7.6163,5.8842,0.3099;8.7397,5.4791,-1.0047;9.1546,5.0835,0.676;7.1259,3.2828,1.1639;6.3664,3.9736,1.5469;7.9366,3.2354,1.9018;6.6673,2.2898,1.0916;8.6881,2.7203,-0.6844;8.1915,1.7481,-0.8257;9.4296,2.5978,0.1199;9.3274,3.1222,-1.8867;10.6419,2.318,-2.5787;12.1059,2.3218,-1.3776;12.9773,1.8204,-1.815;11.8581,1.7909,-0.4506;12.4048,3.3407,-1.1073;10.1709,0.5222,-2.9549;10.9982,-0.0157,-3.4332;9.3032,0.4612,-3.6213;9.9219,-0.0196,-2.0343;11.0124,3.3072,-4.1738;9.7656,3.3063,-5.0841;9.9593,3.8822,-6.001;8.9021,3.7571,-4.5832;9.4841,2.2919,-5.393;11.3703,4.7659,-3.8181;11.5644,5.3447,-4.7329;12.2718,4.828,-3.1967;10.5544,5.2592,-3.2783;12.1964,2.667,-4.9317;12.4065,3.2328,-5.8507;11.9863,1.6321,-5.2296;13.1167,2.6643,-4.3345)|",5.488536364585
"[H]C([H])=C([H])[C@]([H])(OC([H])([H])OC([H])([H])[H])[C@]([H])(OC([H])([H])OC([H])([H])[H])C([H])=C([H])[H] |(0.5846,1.312,3.0017;1.0271,1.0167,2.0542;2.1065,1.1308,1.9682;0.29,0.5371,1.0521;-0.789,0.4352,1.1593;0.8469,0.1076,-0.2825;1.9317,0.3075,-0.2994;0.2123,0.8004,-1.3524;0.666,2.1161,-1.6184;0.0099,2.8551,-1.1438;1.6962,2.2327,-1.2315;0.6043,2.3687,-2.9825;1.5067,1.5742,-3.7449;1.3338,1.8255,-4.7938;1.3307,0.5056,-3.5833;2.5518,1.8101,-3.4868;0.6487,-1.402,-0.5526;1.0464,-1.9605,0.3029;1.376,-1.7611,-1.7446;2.6442,-2.3258,-1.5096;3.1139,-2.3765,-2.5023;3.2487,-1.7019,-0.8374;2.6064,-3.585,-0.8978;1.9781,-4.5873,-1.6889;2.4753,-4.6915,-2.6657;0.9177,-4.3653,-1.8527;2.0748,-5.5252,-1.137;-0.7922,-1.7755,-0.7738;-1.249,-1.3398,-1.6591;-1.4913,-2.568,0.0386;-2.5343,-2.8049,-0.1546;-1.05,-3.0106,0.9296)|",7.069517835990001
"[H]C([H])([H])C(=O)N1C([H])([H])C([H])([H])C([H])([H])[C@]2([H])C(=O)OC(=O)[C@@]12[H] |(2.3578,0.6026,-2.5684;2.1129,0.0732,-1.6395;1.1482,0.442,-1.2784;2.0386,-0.9912,-1.8682;3.2191,0.2449,-0.613;4.0866,-0.5943,-0.454;3.1855,1.4186,0.1668;2.9416,2.6814,-0.5868;3.7448,2.8217,-1.33;2.0059,2.5705,-1.1379;2.8667,3.9408,0.2884;2.7816,4.8073,-0.3771;1.9521,3.9096,0.8949;4.0839,4.0787,1.2208;3.9826,4.9444,1.8828;5.008,4.212,0.6432;4.13,2.7847,2.0105;3.1726,2.6819,2.548;5.1996,2.5074,3.046;5.8852,3.2534,3.6824;5.2521,1.1241,3.1984;4.439,0.4948,2.2265;4.0104,-0.6019,2.3938;4.2756,1.5382,1.1208;5.2498,1.5817,0.6013)|",6.17698440635
"[H]C1=C([H])C([H])=C(/C([H])=C(\[H])C(=O)C2=C([H])C([H])=C([H])C(Cl)=C2[H])O1 |(2.5601,-7.8313,-0.5389;1.5709,-7.5296,-0.2286;0.4417,-8.2205,0.1066;0.3252,-9.2948,0.1222;-0.5362,-7.2366,0.4291;-1.5578,-7.4036,0.7417;0.0631,-6.0074,0.2681;-0.4293,-4.6757,0.4438;-1.4634,-4.5892,0.7702;0.2652,-3.5325,0.2447;1.3009,-3.5779,-0.071;-0.3843,-2.2277,0.4817;-1.5348,-2.1552,0.9125;0.3812,-0.9608,0.2031;-0.1897,0.2483,0.6293;-1.1471,0.2095,1.1369;0.4613,1.4561,0.401;0.0157,2.3877,0.7382;1.69,1.4845,-0.2636;2.2061,2.4207,-0.4484;2.2483,0.282,-0.6925;3.7905,0.3011,-1.5421;1.6134,-0.9371,-0.4666;2.0802,-1.8444,-0.8298;1.3646,-6.1918,-0.1377)|",3.823199599525
"[H]O[C@@]([H])(C([H])([H])C([H])=C([H])[H])[C@@]([H])(C([H])([H])[H])C([H])([H])[C@]([H])(C(=O)OC([H])([H])[H])C([H])([H])[H] |(6.6133,-2.6152,1.242;6.3065,-1.6984,1.3458;5.0865,-1.5824,0.6174;5.2991,-1.5931,-0.4668;4.1612,-2.7847,0.9183;3.9641,-2.8307,1.9948;3.2015,-2.6295,0.4061;4.7886,-4.0732,0.4596;4.8348,-4.2258,-0.6204;5.3375,-4.9875,1.265;5.8101,-5.8855,0.8755;5.311,-4.8826,2.3482;4.4671,-0.2086,0.9426;3.5311,-0.1578,0.3681;4.1431,-0.0486,2.4367;3.7034,0.9321,2.6461;5.0496,-0.1555,3.0411;3.4207,-0.7983,2.7769;5.4026,0.903,0.4182;5.6552,0.6816,-0.6264;6.3402,0.8732,0.9853;4.8457,2.3355,0.4698;4.5653,2.6063,1.4929;3.6047,2.4794,-0.3995;3.4074,1.9038,-1.4499;2.7389,3.3815,0.1209;1.5648,3.6321,-0.668;0.9878,2.713,-0.8014;1.8371,4.0229,-1.6522;0.9874,4.3694,-0.1091;5.8968,3.3566,-0.018;6.7997,3.2893,0.5984;5.515,4.3811,0.0448;6.1772,3.1557,-1.058)|",7.134825160110001
"[H]C1([H])C(=O)[C@@]2([H])C([H])([H])C([H])([H])[C@@](C([H])([H])F)(C1=O)C2([H])[H] |(1.8731,-1.541,-0.3923;0.8982,-1.4975,0.1214;0.7151,-2.495,0.5278;-0.1424,-1.1419,-0.9447;-0.6856,-1.9978,-1.6121;-0.456,0.3439,-1.0932;-0.773,0.5212,-2.1242;-1.5678,0.7362,-0.0673;-2.1648,1.5555,-0.4795;-2.257,-0.0926,0.1217;-0.811,1.1931,1.2141;-1.1166,0.6473,2.1115;-0.9923,2.2569,1.4096;0.7041,0.9874,0.8974;1.6063,1.9305,1.6736;1.6014,1.6883,2.7403;1.2935,2.9717,1.5258;2.9131,1.8007,1.2021;1.0412,-0.4769,1.2457;1.3674,-0.8079,2.3674;0.745,1.1883,-0.6324;0.5841,2.2465,-0.8735;1.6989,0.8935,-1.0808)|",5.651804674885
"[H]OC([H])([H])[C@@]12C(=O)C([H])=C(OC([H])([H])[H])[C@@]([H])(C([H])([H])C1([H])[H])C2([H])[H] |(3.9947,5.0162,-0.8179;4.55,5.3608,-0.0956;5.3101,4.2425,0.3248;5.8631,3.7986,-0.517;6.0473,4.6141,1.0471;4.4566,3.1578,0.9913;3.3087,2.7332,0.0557;3.3026,3.0514,-1.136;2.2499,1.9317,0.6542;1.4578,1.5605,0.0124;2.2548,1.6615,1.9852;1.2741,0.8642,2.4664;1.1416,0.6237,3.8663;0.2945,-0.0576,3.9602;0.9224,1.5457,4.417;2.0342,0.1467,4.286;3.3672,2.1807,2.8715;3.0603,2.2136,3.9193;4.6454,1.3114,2.6594;5.3093,1.434,3.5225;4.4154,0.2448,2.575;5.297,1.8929,1.3738;5.3314,1.1688,0.5543;6.3321,2.1967,1.5723;3.8229,3.553,2.3413;4.5796,3.9847,3.0093;3.0079,4.2745,2.2368)|",5.270845284185
"[H]OC([H])([H])[C@]12C(=O)C([H])([H])C(OC([H])([H])[H])(OC([H])([H])[H])[C@]([H])(C([H])([H])C1([H])[H])C2([H])[H] |(3.7082,-1.4262,-4.3271;3.7771,-2.3073,-3.9189;2.7841,-2.3086,-2.9084;1.7865,-2.1062,-3.3298;2.7652,-3.3212,-2.4888;3.0713,-1.3095,-1.7826;3.1158,0.1201,-2.3211;3.0315,0.3831,-3.5113;3.3402,1.2294,-1.2969;3.0127,2.1747,-1.7351;4.424,1.2916,-1.1316;2.6575,0.9842,0.0663;1.3225,1.4388,-0.1285;0.44,1.3085,0.9819;0.9154,1.637,1.9126;0.0851,0.2765,1.1036;-0.4177,1.9503,0.766;3.2834,1.6812,1.1278;3.4171,3.09,0.9672;2.4679,3.5548,0.6776;4.1823,3.3461,0.2221;3.7307,3.4784,1.9391;2.7194,-0.5113,0.454;2.2599,-0.6252,1.4398;4.1694,-1.0592,0.4374;4.8901,-0.2958,0.7418;4.2578,-1.8792,1.1577;4.4012,-1.5846,-1.0101;4.5833,-2.6647,-1.0148;5.2659,-1.1271,-1.5007;2.0244,-1.3367,-0.6403;1.0527,-0.931,-0.9381;1.8756,-2.3727,-0.3093)|",6.000110403525
"[H]C1=C([H])C2=C(N=C1[C@@]1([H])N([H])N([H])[C@]([H])(N([H])[H])[C@]1([H])F)C([H])=C([H])C([H])=C2[H] |(-3.3685,1.1433,-0.2975;-2.389,0.6841,-0.1991;-1.237,1.4304,-0.1919;-1.2732,2.5141,-0.2806;0.02,0.7831,-0.0697;0.0111,-0.6433,0.0428;-1.1435,-1.3735,0.0375;-2.2929,-0.7352,-0.0839;-3.5452,-1.5953,-0.0875;-3.2427,-2.6123,0.1747;-4.6094,-1.1349,0.8427;-4.1976,-0.4878,1.516;-5.4832,-0.2887,-0.0199;-6.3717,-0.2256,0.4823;-5.7061,-1.1634,-1.1682;-6.1166,-0.5763,-1.9967;-6.645,-2.2369,-0.8607;-6.2717,-2.7767,-0.0795;-6.7006,-2.8786,-1.6506;-4.2584,-1.6326,-1.4661;-3.7614,-0.9861,-2.1953;-4.2675,-2.9166,-2.0011;1.2463,-1.3311,0.1678;1.2155,-2.4129,0.2514;2.4344,-0.6341,0.1782;3.3761,-1.1681,0.2717;2.4427,0.7784,0.0673;3.3889,1.3124,0.0791;1.2594,1.4731,-0.0541;1.2595,2.5574,-0.1395)|",4.133409389095
"[H]C1([H])OC([H])([H])[C@]([H])(C([H])([H])N2/C(=N/N(=O)=O)SC([H])([H])C2([H])[H])C1([H])[H] |(0.6127,-1.7159,-2.6474;0.2026,-1.5969,-1.6413;0.407,-2.5166,-1.0703;0.8615,-0.4869,-1.0339;0.0332,-0.1002,0.047;0.1714,-0.7809,0.9071;0.3256,0.9076,0.3556;-1.4195,-0.1954,-0.4832;-1.6898,0.7724,-0.9221;-2.4082,-0.5306,0.642;-2.3213,0.2151,1.4443;-2.1868,-1.5113,1.0663;-3.8155,-0.5523,0.2291;-4.5077,-1.6963,0.0138;-3.9071,-2.8485,0.2039;-4.6613,-3.9965,-0.0041;-5.7853,-3.9245,-0.5291;-4.121,-5.0401,0.336;-6.1856,-1.3857,-0.494;-5.9745,0.414,-0.1235;-6.539,0.9983,-0.8526;-6.3625,0.6192,0.8782;-4.4743,0.6773,-0.2108;-4.166,1.5031,0.4393;-4.1754,0.9226,-1.24;-1.3053,-1.2617,-1.6119;-1.9113,-2.148,-1.4056;-1.6334,-0.8459,-2.5693)|",5.246355037640001
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"[H]C1=C(C(=O)OC([H])([H])C([H])([H])[H])N=C(/C([H])=C(\C([H])([H])[H])[C@]([H])(OC([H])([H])[H])C([H])([H])[H])O1 |(3.9747,0.8024,1.5895;4.441,0.2516,0.7891;5.0323,0.5991,-0.3889;5.1748,1.9843,-0.8811;4.764,2.948,-0.2597;5.8023,2.0433,-2.0682;5.9868,3.3631,-2.6336;6.1866,4.0695,-1.8244;6.8765,3.2631,-3.26;4.7739,3.7852,-3.4493;4.9695,4.748,-3.9356;3.8972,3.8981,-2.8052;4.5515,3.0455,-4.2251;5.4677,-0.5457,-1.0362;5.1308,-1.5282,-0.2458;5.3841,-2.9325,-0.4945;5.9525,-3.0664,-1.4111;5.0318,-4.0201,0.2208;4.2266,-4.0262,1.4935;3.6544,-3.1092,1.6284;4.8816,-4.1397,2.3687;3.544,-4.881,1.4872;5.5059,-5.3792,-0.2725;5.9231,-5.2598,-1.2847;4.3545,-6.2243,-0.3412;4.4958,-7.3438,-1.1916;3.5248,-7.8458,-1.2142;5.2504,-8.0588,-0.8317;4.7634,-7.042,-2.2169;6.5877,-5.9791,0.6406;7.4483,-5.3049,0.7088;6.9433,-6.9387,0.2505;6.1932,-6.147,1.6476;4.4965,-1.1006,0.9015)|",5.0068948492
"[H]OC1=C([H])C(N(C([H])([H])C([H])([H])[H])C([H])([H])C([H])([H])[H])=C([H])C([H])=C1/C([H])=N/C([H])([H])[H] |(8.3676,-0.1045,-1.2419;7.4306,-0.4424,-1.1403;6.6412,0.633,-0.9746;5.2709,0.4235,-0.8201;4.9313,-0.6031,-0.864;4.3747,1.4987,-0.6443;3.0187,1.2815,-0.4853;2.4654,-0.0642,-0.3529;3.1628,-0.6845,0.2189;1.5599,0.0124,0.2605;2.1248,-0.733,-1.6912;1.7031,-1.7312,-1.5241;3.015,-0.8362,-2.3192;1.3873,-0.1427,-2.247;2.0582,2.3815,-0.451;2.384,3.168,-1.1391;1.1119,2.0042,-0.8562;1.8219,2.9592,0.9509;1.083,3.7687,0.9133;2.7491,3.3575,1.3746;1.4456,2.1887,1.6333;4.9145,2.8169,-0.634;4.2755,3.6763,-0.4775;6.2724,3.0141,-0.7928;6.6622,4.0303,-0.7754;7.1773,1.952,-0.9663;8.593,2.1965,-1.1283;8.9136,3.2506,-1.1046;9.4527,1.2516,-1.2948;10.8518,1.5895,-1.4486;11.2271,1.1953,-2.4014;11.4389,1.1165,-0.6509;11.0381,2.6752,-1.4219)|",4.34293705398
"[H]C1=NC([H])=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])[C@]2([H])F)C([H])=C1[H] |(2.0614,1.5166,-0.0705;1.3409,0.7002,-0.0826;0.2802,0.8404,0.72;-0.6179,-0.1512,0.7235;-1.476,-0.0106,1.3815;-0.5189,-1.3165,-0.0468;-1.5955,-2.3805,0.026;-2.4098,-2.0211,0.6642;-2.166,-2.7721,-1.2925;-1.9346,-2.0458,-1.9707;-1.3287,-3.9418,-1.6865;-1.8655,-4.4205,-2.4134;-1.4003,-4.7751,-0.4887;-0.6219,-5.5439,-0.5372;-2.6923,-5.4392,-0.3644;-3.4202,-4.7254,-0.3286;-2.7379,-5.9431,0.5201;-1.096,-3.7287,0.6148;-0.0315,-3.6878,0.8606;-1.7718,-4.0537,1.7884;0.5992,-1.4359,-0.8859;0.7122,-2.3181,-1.5112;1.5425,-0.412,-0.9033;2.4211,-0.4706,-1.5394)|",4.889885893485
"[H]C1=C([H])C([H])=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])[C@]2([H])F)C([H])=C1[H] |(-2.6648,-0.047,-0.2943;-1.5802,-0.0021,-0.3437;-0.9317,-0.0491,-1.5794;-1.5098,-0.1306,-2.4961;0.4601,0.0124,-1.6422;0.9466,-0.0177,-2.6151;1.2285,0.1167,-0.4728;2.7387,0.1654,-0.5261;3.1242,0.2882,0.4967;3.3311,-1.0602,-1.0984;2.7657,-1.3529,-1.8946;4.6322,-0.7117,-1.624;5.3375,-1.0783,-0.9874;4.7907,0.757,-1.6688;5.1235,1.0688,-2.6659;5.8101,1.1966,-0.7257;5.4268,1.2281,0.2193;6.0925,2.1508,-0.9436;3.3591,1.2865,-1.3769;2.7904,1.4497,-2.2991;3.406,2.4981,-0.6967;0.5657,0.1585,0.759;1.1481,0.2348,1.6739;-0.827,0.1036,0.8259;-1.3219,0.14,1.7927)|",5.6599680904000005
"[H]OC(=N/N([H])[H])/C(C#N)=C(\[H])C1=C([H])C2=C(OC([H])([H])O2)C([H])=C1[H] |(5.3057,-3.5518,0.7919;5.9919,-2.966,1.1471;5.4738,-1.7123,1.3434;6.3328,-0.814,1.6382;5.9275,0.4958,1.8931;6.6593,0.9159,2.4594;5.0466,0.5453,2.415;3.9906,-1.5812,1.2152;3.2522,-2.7443,1.6059;2.713,-3.7319,1.9112;3.4103,-0.4597,0.6904;4.1167,0.3119,0.3948;2.0226,-0.1116,0.4392;0.9075,-0.9258,0.7893;1.0199,-1.8822,1.2814;-0.3413,-0.4467,0.4776;-0.5413,0.7834,-0.1562;-1.8741,1.0014,-0.3394;-2.5482,-0.1475,0.2032;-3.1157,-0.6412,-0.5934;-3.2089,0.1736,1.016;-1.5551,-1.0409,0.7129;0.5177,1.5993,-0.5075;0.3605,2.5536,-0.9973;1.7987,1.1294,-0.196;2.6567,1.7424,-0.456)|",3.3252312531099992
"[H]C1=C([H])C2=C([H])C([H])([H])C([H])([H])C([H])([H])[C@]23N(C1=O)C([H])([H])C([H])([H])C3([H])[H] |(-0.7629,1.1469,-4.0039;-0.5079,0.779,-3.0161;0.5293,1.2583,-2.3087;1.1761,2.0281,-2.7269;0.8864,0.7078,-1.0114;2.1113,0.9261,-0.4912;2.7876,1.5874,-1.0334;2.678,0.2405,0.7183;3.5443,-0.3522,0.3828;3.0947,0.9798,1.4171;1.6635,-0.6676,1.4367;1.1335,-0.0902,2.2037;2.1917,-1.466,1.9705;0.6446,-1.2792,0.4649;-0.0499,-1.919,1.0194;1.1523,-1.9194,-0.2672;-0.1281,-0.1851,-0.3145;-1.0867,-0.8212,-1.2586;-1.4083,-0.2707,-2.4932;-2.4005,-0.6245,-3.1276;-2.2107,-1.3465,-0.4643;-3.0982,-1.3399,-1.097;-2.0165,-2.3816,-0.165;-2.3071,-0.4006,0.7699;-3.2617,0.1334,0.7887;-2.238,-0.9664,1.7048;-1.1178,0.5774,0.6157;-0.6738,0.8693,1.5715;-1.434,1.4945,0.1065)|",4.713011890660001
"[H]C1NC([H])([H])[C@]2([H])[C@]1([H])[C@]1([H])C([H])=C([H])[C@@]2([H])C1([H])[H] |(-0.3611,-3.8086,-0.0332;0.7156,-3.6365,-0.0271;1.5194,-4.5242,-0.4947;2.9263,-3.992,-0.3808;3.6093,-4.8238,-0.1879;3.2007,-3.5315,-1.3413;2.7656,-2.9783,0.7559;2.533,-3.632,1.6085;1.4314,-2.3465,0.323;1.5735,-1.9081,-0.6789;1.3249,-1.0982,1.2895;0.3284,-0.7651,1.5859;2.1912,-0.0823,0.5257;1.7977,0.7442,-0.0583;3.4605,-0.514,0.5687;4.3126,-0.0867,0.05;3.5085,-1.7761,1.4278;4.473,-2.016,1.8817;2.3402,-1.4307,2.4197;2.0496,-2.2653,3.0671;2.5598,-0.5548,3.0367)|",6.315762470105
"[H]/N=C(O[H])\C(C#N)=C(/C([H])([H])[H])C([H])([H])C(=O)OC([H])([H])C([H])([H])[H] |(3.5582,4.3437,1.3832;2.6595,4.0026,1.7277;2.4981,2.7828,1.4241;1.3586,2.1631,1.8404;1.2653,1.3214,1.3659;3.4551,1.9209,0.6362;3.9042,2.5033,-0.5957;4.2681,2.994,-1.5867;3.916,0.7093,1.0411;3.5561,0.08,2.3592;3.0601,-0.8823,2.1841;2.9099,0.7041,2.9765;4.4745,-0.1328,2.9222;4.8489,-0.0991,0.1722;5.6808,-0.5,0.7654;5.2955,0.5017,-0.6262;4.1293,-1.2805,-0.4791;2.9514,-1.5374,-0.3437;4.9924,-1.9918,-1.222;4.457,-3.1398,-1.9354;5.3137,-3.809,-2.0414;3.6972,-3.6149,-1.3107;3.892,-2.7311,-3.2864;3.5676,-3.6225,-3.8352;4.6482,-2.2141,-3.8856;3.0276,-2.0725,-3.1624)|",5.371527408870001
"[H]C1=C([H])C([H])([H])[C@@]([H])(/C([H])=C(\[H])C2=C([H])C([H])=C([H])C([H])=C2[H])O1 |(8.0736,-0.3638,0.7585;7.2438,-0.4258,0.0639;7.2264,-0.7543,-1.2281;8.0832,-1.0364,-1.8254;5.7904,-0.7497,-1.7022;5.4265,-1.7643,-1.9223;5.6215,-0.1507,-2.6048;5.0503,-0.1295,-0.4772;4.808,0.9226,-0.6728;3.8245,-0.8661,-0.0424;3.9872,-1.9061,0.2378;2.6011,-0.3177,-0.0133;2.5113,0.7332,-0.2923;1.3324,-0.9627,0.3551;0.1579,-0.1901,0.364;0.2163,0.8629,0.0977;-1.0723,-0.7477,0.7091;-1.9648,-0.1276,0.7087;-1.1555,-2.097,1.0533;-2.1121,-2.5364,1.3226;0.0024,-2.8808,1.0476;-0.053,-3.9337,1.3118;1.2294,-2.3229,0.7021;2.116,-2.9503,0.6993;6.028,-0.1293,0.6165)|",4.639541151025
"[H]C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[C@@]([H])(C#N)C([H])([H])N(=O)=O |(0.3105,-0.9429,-0.5429;0.857,-0.1455,-0.0277;0.6013,0.8036,-0.5154;0.4918,-0.1001,1.0048;2.3721,-0.3981,-0.0793;2.5532,-1.3769,0.3897;2.8628,-0.4685,-1.5335;2.2849,-1.208,-2.0989;3.9177,-0.7561,-1.61;2.7428,0.5003,-2.0353;3.0962,0.6895,0.7414;2.6925,0.7099,1.7618;2.8702,1.67,0.3038;4.6432,0.5671,0.8202;5.064,0.5302,-0.1893;5.2007,1.7579,1.4809;5.6124,2.7012,2.0171;5.0854,-0.7039,1.5556;4.6123,-1.5882,1.121;4.8765,-0.6762,2.6249;6.572,-0.9683,1.4318;7.0871,-1.6004,2.3448;7.1384,-0.5788,0.4153)|",6.163378713825001
"[H]C1=C(C(=O)C(F)(F)F)C([H])([H])C([H])([H])[C@]2([H])N1C([H])([H])[C@@]([H])(C([H])([H])[H])N2[H] |(4.444,2.3242,-1.8337;4.3254,1.302,-2.1759;5.0498,0.797,-3.2267;5.9563,1.6004,-4.0159;6.6469,1.1595,-4.9284;6.0664,3.1238,-3.7201;6.4846,3.3535,-2.4466;6.9296,3.7175,-4.5444;4.8667,3.7433,-3.8539;4.9461,-0.6751,-3.5728;4.3003,-0.819,-4.4511;5.9334,-1.0401,-3.8726;4.4133,-1.4864,-2.3804;5.1554,-1.5234,-1.575;4.1921,-2.5177,-2.678;3.1517,-0.828,-1.823;2.3633,-0.8579,-2.5991;3.4109,0.5803,-1.4929;2.8853,0.9293,-0.1712;3.6989,1.0264,0.5588;2.3282,1.8726,-0.2031;1.9866,-0.2773,0.1601;2.0075,-0.478,1.2373;0.5295,-0.0758,-0.2841;0.0523,0.7246,0.2937;-0.0529,-0.9911,-0.1238;0.4626,0.1904,-1.3453;2.7074,-1.3611,-0.5355;2.1235,-2.1856,-0.6567)|",4.563349272885
"[H]C1=C([H])[C@]2([H])C3=C(C(C(F)(F)F)=C(C#N)S3)[C@@]1([H])C2([H])[H] |(-2.5711,1.106,0.7149;-1.9979,0.3011,0.2695;-1.9373,-0.9759,0.6587;-2.4434,-1.4472,1.4933;-0.9154,-1.6952,-0.253;-0.9592,-2.7843,-0.2754;0.4398,-1.0579,0.0425;0.3913,0.2629,-0.3449;1.5686,0.9751,-0.031;1.8121,2.4298,-0.3189;2.9378,2.6165,-1.036;0.7864,2.96,-1.0233;1.9372,3.1478,0.8158;2.5108,0.1669,0.6007;3.8109,0.5129,1.0335;4.8845,0.7729,1.4049;1.9125,-1.4718,0.8118;-1.018,0.4835,-0.9115;-1.16,1.3674,-1.5304;-1.208,-0.9181,-1.5815;-0.4808,-1.1233,-2.3737;-2.2269,-1.0814,-1.9439)|",4.476272840725
"[H]C([H])=C([H])[C@]([H])(OC([H])([H])Cl)[C@]([H])(OC([H])([H])Cl)C([H])=C([H])[H] |(0.6954,-3.4098,1.6325;1.2968,-2.6927,1.0804;2.3761,-2.825,1.1314;0.7412,-1.7118,0.368;-0.34,-1.5977,0.3247;1.5226,-0.6994,-0.428;2.6016,-0.8997,-0.3368;1.2404,0.6423,0.0207;1.768,0.9382,1.281;1.4586,0.2138,2.0403;2.8615,1.0201,1.2548;1.1363,2.5453,1.7879;1.1772,-0.7044,-1.9336;1.7177,0.1241,-2.4048;-0.243,-0.513,-2.1006;-0.6607,0.7692,-2.3679;-1.7429,0.7965,-2.254;-0.1627,1.5261,-1.763;-0.3408,1.2762,-4.1275;1.547,-2.0035,-2.5943;0.9423,-2.8625,-2.3107;2.5512,-2.1341,-3.4594;2.7982,-3.0942,-3.9037;3.1603,-1.2863,-3.7658)|",7.123940606090001
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"[H]C1=C([H])C2=C(C([H])=C1[H])N(/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])/N=C\2[H] |(6.3103,-4.0963,-5.7748;6.2218,-3.4566,-4.9017;7.3366,-2.7847,-4.4228;8.3028,-2.8876,-4.9083;7.1843,-1.9657,-3.2921;5.92,-1.8445,-2.6736;4.7893,-2.5189,-3.152;3.8175,-2.425,-2.6782;4.965,-3.3227,-4.2709;4.1119,-3.8628,-4.6717;6.0962,-0.9791,-1.6029;5.1383,-0.5585,-0.6994;4.1481,-0.9663,-0.8728;5.3425,0.281,0.3301;6.3133,0.7108,0.5404;4.2009,0.6163,1.1939;3.0627,0.196,1.0636;4.5819,1.4665,2.1799;3.5528,1.8816,3.105;2.89,1.0349,3.3003;4.0968,2.136,4.0184;2.7743,3.0758,2.5715;2.0532,3.417,3.3235;2.224,2.8016,1.6671;3.4481,3.907,2.3386;7.4,-0.5512,-1.5154;8.0421,-1.1283,-2.5058;9.0994,-0.9368,-2.6395)|",4.503484225775001
"[H]C1N=C([H])NN1/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H] |(6.5953,3.503,0.2598;6.9667,2.8752,-0.5392;7.8968,3.1835,-1.4144;7.971,2.0477,-2.177;8.651,1.9459,-3.0119;7.1545,1.0704,-1.8303;6.4982,1.6111,-0.76;5.514,0.9296,-0.0474;5.0878,1.4934,0.7763;5.0974,-0.3143,-0.3159;5.5081,-0.8946,-1.1327;4.0444,-0.9037,0.5356;3.5101,-0.3472,1.4786;3.7533,-2.1592,0.1275;2.7325,-2.8619,0.8759;2.9728,-3.9179,0.7302;2.8348,-2.6082,1.9338;1.3402,-2.5298,0.3605;0.5939,-3.1259,0.8982;1.2557,-2.7548,-0.7076;1.113,-1.472,0.5197)|",5.208259098569999
"[H]C1=C([H])N(/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])N=C1C(F)(F)F |(8.5676,3.708,3.0218;8.1089,2.8484,2.5587;6.9336,2.7937,1.8509;6.2101,3.553,1.5915;6.7637,1.4884,1.4593;5.6952,0.9842,0.7172;4.9535,1.7273,0.4435;5.5507,-0.2954,0.3501;6.2767,-1.0548,0.6121;4.3584,-0.6709,-0.4361;3.4798,0.0954,-0.7908;4.369,-1.995,-0.708;3.2553,-2.5061,-1.4788;3.1922,-3.5587,-1.1922;2.3447,-1.9884,-1.1677;3.4976,-2.3488,-2.9726;2.6762,-2.8111,-3.5322;4.4325,-2.8356,-3.2689;3.5457,-1.291,-3.246;7.7751,0.6932,1.8815;8.5824,1.5129,2.5416;9.8392,0.9867,3.1684;10.5082,1.9925,3.7757;9.5829,0.045,4.0972;10.6646,0.4366,2.2569)|",5.053154203785001
"[H]C1=NN(/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])C([H])=C1[H] |(9.5136,0.5882,-2.708;8.477,0.2766,-2.7206;7.7487,0.3959,-1.6199;6.5195,-0.0755,-1.9728;5.4635,-0.118,-1.0703;4.5416,-0.5175,-1.481;5.5185,0.287,0.207;6.4247,0.6873,0.6436;4.3034,0.1768,1.0325;3.2264,-0.2521,0.6543;4.5394,0.6271,2.2885;3.4271,0.5767,3.2106;3.6456,1.3609,3.94;2.51,0.8274,2.6718;3.3182,-0.7876,3.8759;2.5119,-0.7761,4.6185;4.2517,-1.0484,4.3852;3.091,-1.5589,3.1347;6.4842,-0.4858,-3.2835;5.5795,-0.8868,-3.7175;7.7371,-0.2721,-3.8017;8.0755,-0.4772,-4.8066)|",5.015058264715
"[H]C1=NC([H])=C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])C([H])=C1[H] |(5.1648,-2.2864,6.4979;5.3892,-1.9616,5.484;4.4124,-2.1204,4.5847;4.6498,-1.7356,3.3267;3.8519,-1.9,2.6053;5.863,-1.1645,2.9299;6.131,-0.7883,1.6288;5.1312,-0.2727,0.8691;4.2481,0.082,1.3952;5.2501,-0.1624,-0.461;6.1271,-0.5197,-0.9883;4.158,0.4654,-1.2212;3.1269,0.9091,-0.7454;4.4515,0.4881,-2.5448;3.4543,1.0711,-3.4145;3.005,1.9305,-2.9108;4.0223,1.4142,-4.283;2.3964,0.0509,-3.8093;1.6907,0.5015,-4.5169;1.8361,-0.28,-2.9304;2.8544,-0.821,-4.288;6.885,-1.0116,3.8652;7.8332,-0.5802,3.5602;6.6399,-1.4221,5.1717;7.4032,-1.3217,5.9375)|",5.412344486445
"[H]C1=C([H])C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=C(C([H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(3.6027,4.1619,4.8661;4.1674,4.1947,3.9388;4.565,3.0051,3.331;4.3355,2.0472,3.7874;5.2898,3.0504,2.1381;5.7295,1.8753,1.5331;4.9026,0.8065,1.4961;3.8533,0.9907,1.7151;5.3455,-0.417,1.1676;6.3908,-0.6081,0.954;4.3827,-1.5237,1.0916;3.1871,-1.4454,1.3203;5.0078,-2.6757,0.7357;4.175,-3.8511,0.6263;4.8561,-4.6847,0.8171;3.412,-3.8186,1.4078;3.5421,-3.956,-0.7539;2.9848,-4.8963,-0.8387;4.3086,-3.9384,-1.5357;2.8469,-3.1284,-0.9207;5.6556,4.2623,1.5314;6.4651,4.302,0.2409;6.5766,3.2707,-0.1064;5.7418,5.0886,-0.868;6.3155,5.0435,-1.8012;5.6232,6.1463,-0.6047;4.7448,4.6782,-1.0618;7.8796,4.8617,0.4883;8.4676,4.8401,-0.4369;8.4117,4.2741,1.2441;7.8426,5.901,0.8366;5.2492,5.4381,2.1764;5.5145,6.3955,1.7357;4.5107,5.4159,3.3596;4.2098,6.3486,3.8281)|",5.35520057784
"[H]C1=C([H])C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=C(C([H])([H])[H])C([H])=C1[H] |(5.737,0.8457,5.8276;6.3404,0.912,4.9269;5.7199,0.8548,3.6772;4.6456,0.719,3.6007;6.4988,0.9443,2.5245;5.9413,0.8669,1.2533;4.7234,1.4094,1.0287;4.3581,2.1177,1.7685;4.0166,1.1226,-0.0751;4.3723,0.4131,-0.8132;2.7258,1.7923,-0.2849;2.2161,2.6073,0.466;2.1527,1.3716,-1.4417;0.8706,1.9502,-1.7711;0.3873,1.1987,-2.4009;0.2935,2.0788,-0.852;1.0304,3.2729,-2.5071;0.0485,3.6476,-2.8189;1.6507,3.148,-3.4009;1.4927,4.0201,-1.8561;7.8965,1.0645,2.5754;8.71,1.1429,1.3079;9.7734,1.2569,1.5376;8.4003,1.9899,0.6847;8.5863,0.2416,0.6962;8.4872,1.1093,3.8415;9.5683,1.2049,3.9079;7.7252,1.0403,5.0104;8.2143,1.0824,5.9793)|",5.287172115215
"[H]C1=C([H])C(C(=O)N([H])[H])=C([H])C([H])=C1O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H] |(6.6579,5.0968,-3.9214;6.0502,4.4899,-4.5844;5.4452,5.0594,-5.7012;5.5461,6.1204,-5.9039;4.704,4.2804,-6.5977;4.0954,4.9774,-7.7792;3.9567,6.1936,-7.8132;3.672,4.1714,-8.8131;4.0991,3.2678,-8.9579;3.3904,4.6677,-9.6488;4.5575,2.9082,-6.3425;3.9493,2.2888,-6.9953;5.1433,2.3273,-5.2229;5.025,1.2711,-5.0046;5.8865,3.122,-4.3475;6.472,2.4682,-3.2811;6.6644,3.1292,-2.1129;6.0936,4.0423,-1.9641;7.483,2.6502,-1.166;8.0561,1.7418,-1.3113;7.6014,3.381,0.1056;7.0169,4.4132,0.3871;8.4697,2.7526,0.9364;8.6814,3.3656,2.2285;9.6791,3.0323,2.5254;8.6815,4.4517,2.1086;7.6254,2.9243,3.2316;7.8536,3.3399,4.22;7.5993,1.8327,3.3146;6.6368,3.2806,2.929)|",5.113019250895
"[H]C1=C([H])C(N(=O)=O)=C([H])C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=C1[H] |(3.2068,-4.5097,-2.6182;4.2311,-4.259,-2.3607;5.2378,-5.2056,-2.5334;5.0357,-6.1981,-2.9148;6.543,-4.8414,-2.2067;7.6258,-5.8276,-2.3872;7.3185,-6.9336,-2.8286;8.7671,-5.4834,-2.0858;6.8705,-3.5853,-1.7098;7.8929,-3.3344,-1.4587;5.8422,-2.6594,-1.5403;6.2169,-1.4148,-1.0806;5.3535,-0.6972,-0.3159;4.5307,-1.2422,0.14;5.5373,0.6104,-0.0935;6.3571,1.1589,-0.5426;4.5975,1.3139,0.7962;3.6274,0.8114,1.3373;4.9505,2.6144,0.9281;4.1089,3.4279,1.7788;4.2504,4.4443,1.4029;3.0679,3.1281,1.6358;4.5201,3.3119,3.2393;3.9214,3.998,3.8496;5.5764,3.5696,3.3679;4.3556,2.2946,3.605;4.5229,-2.9826,-1.8742;3.7357,-2.2427,-1.7749)|",4.166063051155001
"[H]C1=C([H])C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=C([H])C(F)=C1[H] |(7.7463,0.0822,6.0141;7.8938,0.3949,4.9847;7.1589,-0.2158,3.9707;6.4378,-0.9981,4.1806;7.3512,0.1947,2.6496;6.6187,-0.4751,1.6872;6.2443,0.1745,0.5585;6.2915,1.2606,0.5769;5.7901,-0.4896,-0.5136;5.7429,-1.5721,-0.5382;5.3389,0.2781,-1.6843;5.3501,1.4934,-1.7834;4.9001,-0.5558,-2.6598;4.4357,0.0679,-3.8785;3.7282,-0.6524,-4.2972;3.9084,0.9907,-3.6248;5.5864,0.3383,-4.8371;5.1994,0.7504,-5.7763;6.1299,-0.5848,-5.0641;6.2827,1.063,-4.406;8.2725,1.1928,2.3275;8.4681,1.496,1.3056;8.9783,1.7833,3.3712;9.8693,2.7485,3.0697;8.8113,1.4085,4.6981;9.3899,1.8984,5.4733)|",5.344316023820001
"[H]C1=C([H])C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=C([H])C([H])=C1F |(8.5923,-3.2429,-4.4211;7.6855,-2.7568,-4.0773;7.6743,-1.9738,-2.9256;8.5745,-1.8276,-2.338;6.4867,-1.3666,-2.5137;6.559,-0.5656,-1.384;5.5097,-0.5392,-0.5291;4.7778,-1.3379,-0.624;5.399,0.4049,0.4169;6.1231,1.2058,0.5115;4.2649,0.3483,1.3511;3.3842,-0.4949,1.3518;4.3193,1.3857,2.224;3.2613,1.4598,3.2058;3.1996,2.522,3.4563;2.3253,1.1404,2.7407;3.5803,0.6168,4.4323;2.8025,0.7551,5.1924;4.5419,0.9094,4.8668;3.6186,-0.444,4.1691;5.3091,-1.5228,-3.2473;4.3987,-1.0176,-2.9413;5.3162,-2.3162,-4.3959;4.4176,-2.4572,-4.9869;6.5028,-2.9205,-4.792;6.5122,-3.6831,-5.9049)|",5.325268054285001
"[H]/C(SC1([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C1([H])[H])=C(/[H])C(=O)OC([H])([H])C([H])([H])[H] |(5.8873,3.0334,-1.8143;4.9429,3.5259,-1.5877;3.5977,3.146,-2.6365;4.494,2.2173,-3.9856;5.1485,1.5178,-3.454;5.3347,3.1483,-4.878;6.0035,3.7619,-4.2633;5.9805,2.5089,-5.4987;4.4721,4.035,-5.7886;3.9077,4.7472,-5.1726;5.1221,4.6303,-6.4416;3.4932,3.1977,-6.6258;4.0636,2.5658,-7.3232;2.8629,3.8513,-7.2415;2.6171,2.3036,-5.7341;1.9667,1.6698,-6.3493;1.9525,2.9318,-5.1252;3.4753,1.4178,-4.8167;4.0553,0.7202,-5.4383;2.8472,0.8053,-4.1597;4.9015,4.3491,-0.5246;4.0114,4.8968,-0.2324;6.1241,4.5412,0.275;7.1953,3.9923,0.079;5.908,5.4357,1.2737;7.046,5.7531,2.1062;6.6071,6.0797,3.0526;7.6282,4.843,2.2705;7.9022,6.845,1.4813;8.7127,7.1215,2.1656;7.3039,7.7385,1.2747;8.3461,6.4935,0.5457)|",5.020500541725
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"[H]OC1=C([H])C([H])=C(S/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])C([H])=C1[H] |(6.6313,-7.3765,-2.3395;6.0068,-7.2669,-1.6055;6.1548,-6.0222,-1.0704;7.093,-5.1,-1.5487;7.7435,-5.3641,-2.3803;7.1967,-3.8433,-0.9558;7.9283,-3.1318,-1.3256;6.3627,-3.4925,0.1116;6.5339,-1.8948,0.909;5.4352,-0.8974,-0.0336;4.9264,-1.3866,-0.8605;5.2011,0.4035,0.209;5.6721,0.9465,1.0224;4.263,1.1424,-0.6545;3.6623,0.6844,-1.6105;4.1472,2.4297,-0.2359;3.258,3.2756,-0.9969;2.3977,2.6851,-1.3219;2.9284,4.0359,-0.2836;3.9729,3.9019,-2.1858;3.3038,4.6033,-2.6979;4.2752,3.1306,-2.9;4.8627,4.4508,-1.8599;5.4314,-4.426,0.5898;4.7887,-4.164,1.4246;5.323,-5.6819,0.0037;4.6038,-6.411,0.3627)|",5.14839405146
"[H]C1=C(OC([H])([H])[H])C(OC([H])([H])[H])=C([H])C(/C([H])=C(\[H])C([H])([H])[H])=C1Cl |(6.8673,1.4217,1.0582;6.4995,0.6542,0.3897;7.3636,-0.2833,-0.1734;8.7087,-0.323,0.0397;9.2861,0.6764,0.868;10.357,0.4675,0.8834;8.8909,0.6293,1.8911;9.1168,1.6807,0.4592;6.8302,-1.2674,-1.0391;7.637,-2.1806,-1.6718;8.2346,-3.1714,-0.8315;8.7998,-3.8279,-1.497;7.4648,-3.7574,-0.3118;8.9113,-2.7219,-0.0986;5.4746,-1.2671,-1.3193;5.1194,-2.0311,-2.0024;4.5702,-0.3283,-0.7769;3.1359,-0.349,-1.0945;2.5528,0.4578,-0.6584;2.4891,-1.2483,-1.8522;3.0318,-2.0793,-2.3021;1.0186,-1.2016,-2.1447;0.5196,-2.1281,-1.8288;0.831,-1.1001,-3.2229;0.5324,-0.3625,-1.6361;5.1352,0.6228,0.0872;4.1392,1.8631,0.8596)|",4.80553059983
"[H]C1=C([H])C([H])=C([H])[C@@]2([H])C1NN(C([H])([H])C([H])([H])C([H])([H])SC([H])([H])C([H])([H])[H])C2=O |(9.9832,-6.7886,2.2815;10.2336,-6.2562,3.1938;10.2156,-6.8552,4.4144;9.989,-7.9152,4.4901;10.4064,-6.105,5.6542;10.2483,-6.6284,6.5934;10.7557,-4.8023,5.6572;10.9043,-4.238,6.5732;11.0621,-4.1446,4.3494;12.1655,-4.1626,4.2271;10.4709,-4.8373,3.1563;10.1108,-4.0103,2.2192;10.3443,-2.7216,2.7031;9.8638,-1.5706,1.952;8.8757,-1.8163,1.5456;9.7455,-0.7699,2.6875;10.8166,-1.1133,0.8355;11.8056,-0.9335,1.274;10.4596,-0.1471,0.4584;10.9193,-2.0997,-0.3323;9.9403,-2.2249,-0.8085;11.2243,-3.0925,0.0097;12.0296,-1.545,-1.6889;13.6935,-1.8526,-0.9647;14.3781,-1.3177,-1.6315;13.7568,-1.3602,0.012;14.0848,-3.3266,-0.8661;15.0933,-3.4251,-0.4442;14.0757,-3.8006,-1.8525;13.3992,-3.8844,-0.2193;10.7286,-2.6817,4.0385;10.8433,-1.6915,4.7357)|",3.850410984575
"[H]C1=C([H])C([H])=C(N2[C@]3([H])C([H])([H])C4=C(C([H])=C([H])C([H])=C4[H])[C@@]2([H])C([H])([H])C3([H])[H])C([H])=C1[H] |(-2.5038,-0.9379,0.6582;-1.4958,-0.8007,0.2782;-1.2705,-0.1018,-0.9098;-2.1079,0.3167,-1.463;0.019,0.0751,-1.4025;0.1624,0.6418,-2.3168;1.1362,-0.4496,-0.718;2.434,-0.1994,-1.1891;2.7407,-0.2148,-2.6352;2.0904,0.4739,-3.1793;2.5622,-1.649,-3.1773;1.4879,-1.8715,-3.2431;2.9555,-1.7169,-4.2004;3.2296,-2.6818,-2.2801;3.7025,-2.3129,-1.0066;4.2847,-3.275,-0.1763;4.6471,-2.9817,0.8076;4.4025,-4.6009,-0.5929;4.852,-5.3408,0.0641;3.9365,-4.9704,-1.856;4.0236,-6.0006,-2.1911;3.3543,-4.0144,-2.6882;2.9897,-4.3054,-3.6714;3.6113,-0.8469,-0.5942;3.6054,-0.7434,0.4943;4.7515,-0.0261,-1.2391;4.8892,0.9062,-0.6822;5.7,-0.5703,-1.2362;4.2157,0.2594,-2.6678;4.7814,-0.2744,-3.4376;4.2733,1.3266,-2.9015;0.8973,-1.1621,0.4739;1.7219,-1.6088,1.017;-0.3991,-1.3249,0.9612;-0.5471,-1.8827,1.8828)|",5.025942818735
"[H]C1=C([H])C(N(=O)=O)=C([H])C([H])=C1OC([H])([H])/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])[H] |(7.1792,1.8112,5.7041;7.2043,2.779,5.2147;7.4687,3.936,5.9271;7.6554,3.9088,6.9936;7.497,5.1592,5.2505;7.7752,6.386,5.9917;7.9785,6.2926,7.2044;7.7912,7.448,5.3646;7.2612,5.2313,3.8792;7.2866,6.1972,3.3902;6.9929,4.069,3.1623;6.7925,4.1347,2.1013;6.9672,2.8319,3.8279;6.7442,1.6312,3.2398;6.5042,1.5459,1.8219;7.2515,2.1304,1.2705;6.6835,0.487,1.6061;5.1027,1.9325,1.4469;4.3177,1.4673,2.0437;4.7856,2.7383,0.4298;5.5919,3.1947,-0.1516;3.3907,3.0849,-0.013;3.2507,2.7311,-1.0454;2.6611,2.5446,0.6043;3.0954,4.5996,0.0359;3.8606,5.1348,-0.5439;2.142,4.787,-0.4738;3.037,5.161,1.4601;2.8409,6.2389,1.4549;3.9784,4.9922,1.9942;2.2384,4.6805,2.0385)|",4.56062813438
"[H]C(=O)/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C#N |(3.8317,-4.3915,-1.264;4.5815,-3.845,-0.6462;5.5885,-4.4003,-0.249;4.2418,-2.4412,-0.371;4.9545,-1.8741,0.225;3.1117,-1.8913,-0.843;2.4426,-2.5213,-1.4356;2.6652,-0.4741,-0.6412;3.4089,0.0724,-0.0479;2.6216,0.0178,-1.6244;1.2737,-0.3548,0.0172;0.9689,0.6996,-0.0078;0.5371,-0.9019,-0.5884;1.2291,-0.8638,1.464;1.9696,-0.3143,2.0637;1.5347,-1.9188,1.4942;-0.1569,-0.7128,2.1012;-0.9026,-1.262,1.5131;-0.4637,0.3404,2.0895;-0.1834,-1.2288,3.5556;0.5482,-0.6833,4.1653;0.1073,-2.2865,3.5889;-1.5021,-1.0907,4.1805;-2.5542,-0.9725,4.6568)|",5.232749345115
"[H]C1=C([H])C(=O)C(OC([H])([H])[H])=C([H])C1C1=NNC(=S)S1 |(4.6184,-3.7317,3.1367;4.2853,-2.907,2.5182;5.0017,-1.768,2.4081;5.9403,-1.6224,2.9335;4.5626,-0.6401,1.5782;5.2092,0.4047,1.5075;3.2796,-0.8375,0.8224;2.7612,0.0876,0.0106;3.3988,1.3402,-0.3063;2.7231,1.8041,-1.0263;4.3818,1.1806,-0.7525;3.5048,1.9653,0.5812;2.571,-2.0037,0.9643;1.6422,-2.1072,0.4118;3.0305,-3.0633,1.7961;2.32,-4.2569,1.935;2.8015,-5.2716,2.7375;2.0823,-6.3248,2.7878;0.9101,-6.2739,2.0134;-0.1795,-7.4966,1.9042;0.7988,-4.6952,1.1815)|",1.967383139115
"[H]OC([H])([H])C([H])([H])/C([H])=C(\[H])[C@@]([H])(C1=C([H])C([H])=C([H])C([H])=C1[H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(3.1531,-4.6393,4.2958;2.666,-5.4517,4.0895;2.9712,-5.7948,2.7425;4.0599,-5.8375,2.5817;2.5748,-6.8055,2.5981;2.3371,-4.8374,1.7174;2.5899,-5.1906,0.7084;1.2452,-4.9052,1.8228;2.7784,-3.408,1.8904;2.4331,-2.9048,2.7961;3.5485,-2.7297,1.0303;3.8787,-3.2414,0.1251;3.9801,-1.2941,1.1812;3.7187,-0.9639,2.197;5.4707,-1.0668,0.9857;6.1738,-1.5941,-0.1103;5.6551,-2.2056,-0.8435;7.5377,-1.3498,-0.2772;8.0568,-1.7734,-1.1333;8.2341,-0.5688,0.6462;9.2961,-0.3798,0.5161;7.5503,-0.0362,1.7399;8.0785,0.5703,2.4714;6.1877,-0.2857,1.9057;5.6686,0.129,2.7672;2.9152,-0.143,0.0337;3.0083,-0.7185,-1.7699;2.3838,-0.0776,-2.4042;2.6487,-1.7473,-1.8861;4.0315,-0.6694,-2.1584;3.5537,1.6355,0.1706;2.9531,2.3079,-0.4543;4.596,1.7136,-0.1557;3.4976,2.0051,1.2015;1.12,-0.2176,0.6367;0.4746,0.4138,0.0141;1.0323,0.139,1.6701;0.7277,-1.2395,0.6011)|",5.989225849505
"[H]C1=C([H])C([H])=C([C@]([H])(/C([H])=C(\[H])C([H])(C([H])([H])[H])C([H])([H])[H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(4.4578,7.1957,3.0207;4.5926,6.3919,2.3021;4.3745,6.6155,0.942;4.0653,7.5977,0.5929;4.5434,5.5788,0.0239;4.3611,5.7643,-1.0325;4.9375,4.2954,0.4341;5.1363,3.1945,-0.5952;4.7145,3.5445,-1.548;4.4624,1.8925,-0.2394;4.656,1.5009,0.7605;3.6732,1.1785,-1.0506;3.4617,1.5576,-2.0545;3.018,-0.1347,-0.6973;3.3102,-0.3934,0.3304;1.4829,-0.0103,-0.741;1.0042,-0.9594,-0.4701;1.1409,0.2599,-1.7482;1.1293,0.7622,-0.0496;3.4986,-1.2644,-1.6283;3.0229,-2.2177,-1.367;4.5844,-1.3956,-1.5646;3.2488,-1.0441,-2.6742;7.0132,2.9023,-0.9974;7.8816,2.0287,0.4435;8.9359,1.849,0.1996;7.424,1.057,0.6618;7.8526,2.6321,1.3576;7.1156,1.8233,-2.5517;8.1592,1.6011,-2.8053;6.666,2.3263,-3.4167;6.5919,0.8716,-2.4106;7.8472,4.5704,-1.3321;8.9031,4.4287,-1.5933;7.8014,5.2247,-0.4548;7.369,5.0983,-2.1659;5.1544,4.0879,1.8068;5.4572,3.1079,2.1656;4.9824,5.1215,2.7286;5.1537,4.9312,3.7853)|",5.9919469880100005
"[H]OC([H])([H])[C@@]([H])(ON([H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])C([H])([H])C([H])([H])C([H])=C([H])[H] |(7.2302,0.7273,-1.7705;7.8913,1.0388,-1.1315;7.1685,1.6722,-0.0892;7.9091,2.0251,0.6351;6.6085,2.5432,-0.462;6.1895,0.7191,0.6073;5.6642,1.2894,1.3942;5.2418,0.432,-0.4497;4.04,-0.2018,-0.0556;3.7863,0.106,0.8811;3.9794,-1.5967,-0.2402;4.7586,-2.2522,-1.2041;5.4847,-1.6857,-1.7745;4.5876,-3.6191,-1.4213;5.2014,-4.1151,-2.1689;3.6419,-4.3494,-0.6991;3.5138,-5.4132,-0.8762;2.8622,-3.6917,0.2546;2.1227,-4.2431,0.8295;3.0264,-2.3286,0.4883;2.4114,-1.8254,1.2315;6.8904,-0.5027,1.2274;7.1126,-1.2231,0.4334;7.8653,-0.1559,1.5984;6.1599,-1.1819,2.3948;5.9003,-0.4244,3.1506;5.22,-1.6351,2.0633;7.0152,-2.2802,3.0586;7.2589,-3.052,2.3171;7.9719,-1.8362,3.3758;6.3395,-2.911,4.2454;6.1076,-2.2371,5.0729;6.0017,-4.197,4.3457;5.5081,-4.593,5.2294;6.2088,-4.9055,3.5461)|",5.532074580664999
"[H]C1=C([H])C2=C(C([H])=C1F)C([H])([H])[C@@]([H])(C([H])([H])[H])O2 |(5.8483,0.5027,4.755;5.2809,0.099,3.9229;4.6162,0.9412,3.0249;4.6471,2.0199,3.135;3.9088,0.3516,1.9814;3.851,-1.0339,1.8152;4.5078,-1.8731,2.7077;4.4859,-2.9547,2.617;5.2169,-1.2801,3.7516;5.8672,-2.0792,4.6314;3.004,-1.3306,0.6019;3.5575,-1.88,-0.1688;2.1189,-1.9308,0.8497;2.6014,0.0934,0.1119;3.0356,0.3031,-0.8722;1.1017,0.3463,0.0812;0.622,-0.3001,-0.6632;0.8907,1.3883,-0.1782;0.6574,0.1363,1.0605;3.2134,1.0459,1.0343)|",5.3334314698
"[H]OC1=C(C([H])([H])[C@@]([H])(O[H])C([H])([H])[H])C([H])=C(F)C([H])=C1[H] |(1.2624,-0.3465,-5.3523;1.6534,-0.1953,-4.4779;2.0192,-1.4073,-3.9234;2.6301,-1.3758,-2.6561;2.9168,-0.0775,-1.9242;3.6234,-0.3006,-1.1162;3.4157,0.6294,-2.5984;1.7036,0.6694,-1.3027;2.1257,1.4017,-0.6018;0.9944,1.4523,-2.255;0.9223,0.9317,-3.0729;0.7576,-0.2488,-0.5234;1.2864,-0.7948,0.2682;0.2889,-0.9857,-1.1855;-0.035,0.3488,-0.063;2.9928,-2.6026,-2.0899;3.4729,-2.6354,-1.1167;2.754,-3.7968,-2.7604;3.1203,-4.9596,-2.1765;2.1571,-3.824,-4.0115;1.9857,-4.7692,-4.5149;1.7889,-2.6096,-4.5921;1.3167,-2.6017,-5.5728)|",5.662689228905
"[H]C1=C([H])C([H])=C(/C([H])=N/N([H])C2=C([H])C([H])=C(C([H])([H])N([H])[H])C([H])=C2[H])C([H])=C1[H] |(6.537,5.7473,-1.4127;6.2365,4.7217,-1.2163;7.153,3.6759,-1.379;8.1684,3.8904,-1.7027;6.7754,2.3611,-1.1291;7.483,1.548,-1.2538;5.4648,2.0635,-0.7083;5.0268,0.6961,-0.4351;3.9871,0.5664,-0.1015;5.8079,-0.3204,-0.5736;5.3254,-1.5406,-0.2992;4.3595,-1.6355,0.0091;6.0947,-2.6968,-0.4328;5.5111,-3.9379,-0.1275;4.4755,-3.9796,0.2055;6.2493,-5.1106,-0.2495;5.7964,-6.0658,-0.0021;7.5845,-5.0841,-0.6748;8.3791,-6.3649,-0.8371;9.4476,-6.1034,-0.9402;8.0954,-6.8608,-1.7767;8.0942,-7.3147,0.2467;8.4089,-6.9217,1.133;8.6241,-8.1726,0.1028;8.1539,-3.8388,-0.9673;9.1924,-3.7913,-1.2894;7.4308,-2.6535,-0.8562;7.8848,-1.6963,-1.0823;4.553,3.1209,-0.5477;3.5377,2.9042,-0.2225;4.935,4.4381,-0.7996;4.2157,5.2424,-0.6697)|",3.910276031685
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"[H]/C(C(=O)OC([H])([H])C([H])([H])[H])=C(/[H])[C@]([H])(OC(=O)OC([H])([H])[H])C([H])([H])[H] |(5.5703,-0.4495,-1.1843;5.3086,0.1335,-0.3085;4.3289,1.2136,-0.5657;3.8553,1.4347,-1.6639;4.021,1.9283,0.5459;3.0679,3.0024,0.3765;3.3054,3.7042,1.1802;3.2463,3.4825,-0.5887;1.6363,2.4978,0.4826;0.9391,3.3419,0.4271;1.4748,1.9802,1.4339;1.41,1.811,-0.3377;5.8425,-0.1258,0.8898;5.5527,0.4794,1.7459;6.8305,-1.2111,1.1985;7.7717,-0.7636,1.54;7.1033,-1.9405,-0.0205;8.3167,-2.5102,-0.1091;9.1952,-2.4653,0.7252;8.3849,-3.1399,-1.2894;9.6338,-3.8034,-1.5432;10.4582,-3.0857,-1.5411;9.5224,-4.2564,-2.5284;9.8231,-4.5697,-0.7869;6.32,-2.1748,2.2744;6.0909,-1.6282,3.1955;7.0879,-2.9202,2.4987;5.4112,-2.6799,1.9329)|",6.1497730213
"[H]C1=C([H])C([H])=C([C@@]2([H])N(C([H])([H])[H])O[C@]3([H])C(=O)N(C([H])([H])[H])C(=O)[C@]23[H])O1 |(7.5288,-4.7758,3.3657;7.3082,-3.7894,2.987;8.0712,-2.7048,2.6813;9.1448,-2.6232,2.7792;7.1642,-1.7027,2.2054;7.4097,-0.7,1.8859;5.9132,-2.251,2.2526;4.5298,-1.7586,1.9559;3.8469,-2.6128,1.9808;3.9828,-0.7186,2.8735;4.2752,-0.8771,4.2902;3.7906,-1.7994,4.6251;3.8353,-0.0325,4.8259;5.3508,-0.9279,4.5077;4.7194,0.4817,2.4471;4.5421,0.4825,1.0394;5.3826,1.0319,0.6087;3.2118,1.1343,0.6132;2.884,2.2914,0.7541;2.427,0.1507,0.0155;1.082,0.4068,-0.4764;0.8194,-0.3872,-1.1766;1.0645,1.3819,-0.9668;0.3654,0.4136,0.351;3.0111,-1.1126,0.0127;2.4821,-2.1217,-0.4046;4.4124,-0.9973,0.6144;5.1496,-1.3375,-0.1154;5.9937,-3.5327,2.7369)|",5.270845284185
"[H]OC([H])([H])[C@]1(C([H])([H])[H])C([H])([H])C([H])=C2C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])C([H])([H])C(=O)C([H])([H])[C@@]21[H] |(2.436,-2.6219,-2.4459;3.197,-2.6737,-3.0462;4.1985,-3.4583,-2.409;4.0379,-4.5299,-2.6161;5.1433,-3.178,-2.8879;4.3087,-3.2844,-0.875;5.542,-4.0649,-0.4006;5.4861,-5.1169,-0.7069;5.6268,-4.0398,0.6914;6.4673,-3.6492,-0.8175;2.9955,-3.7868,-0.2071;2.5029,-4.5721,-0.7986;3.1986,-4.2338,0.7787;2.169,-2.5354,-0.0581;1.1164,-2.5738,0.2038;2.8888,-1.409,-0.1984;2.3688,0.0152,-0.0078;0.8271,0.0278,0.0418;0.4675,1.0519,0.1931;0.4421,-0.5786,0.8682;0.3909,-0.3473,-0.8912;2.8935,0.578,1.3374;2.5499,1.6099,1.4812;3.9856,0.5811,1.4001;2.5141,-0.0232,2.1709;2.8017,0.9215,-1.2043;2.663,0.3526,-2.1322;2.1055,1.767,-1.2634;4.2237,1.5117,-1.158;4.3077,2.3165,-0.4214;4.4385,1.9651,-2.138;5.3448,0.5209,-0.878;6.3222,0.8372,-0.2223;5.1786,-0.8828,-1.4266;6.1811,-1.3107,-1.53;4.6915,-0.8668,-2.4059;4.3612,-1.7705,-0.453;4.885,-1.7171,0.5157)|",6.05181203512
"[H]OC([H])([H])[C@]1(C([H])([H])[H])C([H])([H])[C@]2([H])C(=C(C([H])([H])[H])C([H])([H])[H])[C@@]1([H])C([H])([H])[C@@]2(O[H])C([H])=C([H])[H] |(8.0218,2.1091,-1.9499;7.7182,2.7254,-2.6332;6.4224,3.1833,-2.2536;6.4349,3.5879,-1.2301;6.1982,4.014,-2.9322;5.3078,2.1227,-2.3836;5.2811,1.605,-3.8286;5.1891,2.4487,-4.5242;4.4297,0.945,-4.0123;6.2071,1.0771,-4.0786;3.942,2.7613,-1.9416;4.0726,3.8166,-1.669;3.1845,2.7093,-2.7259;3.5288,1.9563,-0.6835;2.7743,2.4493,-0.0691;4.89,1.6768,-0.0628;5.3783,1.9256,1.1588;4.571,2.5955,2.2466;5.0774,3.5014,2.6083;4.4655,1.9333,3.1176;3.5689,2.879,1.9172;6.7858,1.5588,1.572;6.7739,0.8784,2.4353;7.3456,2.4494,1.8906;7.3519,1.072,0.7738;5.498,1.0094,-1.2912;6.5293,0.6529,-1.2114;4.482,-0.1434,-1.4824;4.723,-0.9653,-0.7992;4.4589,-0.553,-2.4958;3.0948,0.5128,-1.1139;2.2233,0.6119,-2.2386;1.9679,-0.2938,-2.4777;2.4272,-0.21,0.0343;3.0744,-0.4166,0.8861;1.1489,-0.5877,0.0682;0.7344,-1.1076,0.9272;0.4684,-0.3735,-0.7513)|",6.64502022921
"[H]C1=C([H])C([H])=C([C@@]2([H])OC(C([H])([H])C([H])([H])[H])=NN(C([H])([H])[H])[C@]2([H])C([H])([H])[H])C([H])=C1[H] |(9.4673,2.414,-2.4441;8.423,2.1904,-2.646;8.047,1.6468,-3.8744;8.796,1.4422,-4.6347;6.7051,1.3556,-4.1244;6.4183,0.9211,-5.08;5.7219,1.6119,-3.1612;4.2725,1.3094,-3.4857;4.2424,0.7422,-4.4218;3.6858,0.4159,-2.5123;2.9502,0.9707,-1.4994;2.508,-0.0509,-0.4915;1.7932,0.4383,0.1759;1.9795,-0.8572,-1.0175;3.6761,-0.6493,0.3113;3.3098,-1.4016,1.0181;4.1952,0.1293,0.8809;4.4029,-1.1267,-0.3529;2.6526,2.2095,-1.3636;3.1679,3.1056,-2.3035;2.4932,4.3906,-2.2227;3.0277,5.1099,-2.8521;2.5286,4.7335,-1.186;1.4377,4.3569,-2.5373;3.3737,2.5563,-3.6446;3.9394,3.3041,-4.213;2.077,2.2198,-4.4056;2.3066,1.8599,-5.4151;1.441,3.105,-4.5048;1.5035,1.4441,-3.8891;6.1089,2.158,-1.9286;5.3593,2.3639,-1.1735;7.4507,2.4413,-1.6752;7.738,2.8619,-0.715)|",5.074923311825
"[H]OC(NN(C([H])([H])[H])[C@@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])C([H])([H])[H] |(0.3343,1.9697,2.4425;-0.1621,1.4173,1.8199;0.6268,0.3728,1.4325;0.0596,-0.4775,0.6666;0.9189,-1.5537,0.2268;1.4577,-1.2253,-1.0967;0.7011,-0.8799,-1.8128;1.9545,-2.1114,-1.5039;2.2117,-0.4369,-0.9866;0.1793,-2.834,0.3282;0.7679,-3.5555,-0.2561;0.1864,-3.2804,1.7961;-0.3009,-4.2505,1.9131;-0.3531,-2.5552,2.4159;1.217,-3.3438,2.16;-1.2806,-2.8288,-0.2249;-1.8375,-2.0789,0.3393;-1.9317,-4.0729,0.0823;-1.5853,-4.7453,-0.5253;-1.4081,-2.516,-1.7072;-0.8891,-3.3874,-2.6762;-0.3483,-4.2832,-2.3725;-1.0426,-3.1216,-4.0369;-0.6335,-3.8097,-4.7723;-1.7184,-1.9723,-4.4523;-1.8385,-1.7624,-5.5118;-2.239,-1.0966,-3.498;-2.7671,-0.1999,-3.8121;-2.0864,-1.3695,-2.1374;-2.483,-0.6819,-1.3956;2.0447,0.3783,1.9555;2.0005,0.4077,3.0545;2.4998,-0.5717,1.6689;2.8822,1.5649,1.4444;3.8938,1.5233,1.8608;2.4495,2.5328,1.7265;2.9639,1.549,0.3528)|",5.042269649765
"[H]C(C1=C([H])C([H])=C(C([H])([H])[H])O1)N(O)C([H])([H])[H] |(6.6241,0.6732,0.2539;5.674,1.1734,0.3945;4.4906,0.3939,0.2063;4.3701,-0.9338,-0.1386;5.1875,-1.6155,-0.3281;2.9742,-1.217,-0.1944;2.5094,-2.1627,-0.4372;2.3184,-0.0601,0.1159;0.8841,0.3148,0.2329;0.2557,-0.5527,0.0142;0.6171,1.116,-0.468;0.6397,0.6683,1.2428;3.234,0.9331,0.3635;5.7908,2.4481,0.7246;6.9257,3.0174,0.8577;4.6094,3.3162,0.9653;4.0098,2.9151,1.7844;3.9931,3.3682,0.0659;5.0203,4.2906,1.218)|",3.787824798960001
"[H]C(C1=C([H])C([H])=C(C([H])([H])[H])S1)N(O)C([H])([H])[H] |(6.7017,0.6836,0.0536;5.8538,1.3258,0.2645;4.551,0.7343,0.16;4.3551,-0.5973,-0.1657;5.1829,-1.2734,-0.3515;2.9912,-0.9821,-0.231;2.6631,-1.9884,-0.4716;2.1123,0.0349,0.0373;0.6142,0.0001,0.0598;0.267,-1.0142,-0.1603;0.1753,0.6737,-0.6869;0.2097,0.2876,1.0382;2.9821,1.5133,0.3828;6.2173,2.5563,0.5838;7.4419,2.919,0.6164;5.2343,3.6125,0.9229;4.6015,3.2904,1.7539;4.6128,3.8427,0.0531;5.8283,4.4782,1.2063)|",3.659931289225
"[H]C1=C(F)C2=C3C(=N1)/C([H])=C(/[H])C(=O)N3C([H])([H])[C@]21OC1([H])[H] |(-3.1432,-2.0068,-0.0024;-2.2468,-1.3913,-0.0062;-2.391,0.0114,-0.0734;-3.6256,0.5379,-0.143;-1.2689,0.8136,-0.0807;-0.0522,0.1368,-0.0194;0.0487,-1.2561,0.0435;-1.071,-2.0124,0.0502;1.3909,-1.7807,0.0914;1.5258,-2.8573,0.1464;2.4562,-0.9341,0.066;3.4739,-1.3076,0.0987;2.3448,0.5355,-0.0153;3.2846,1.3205,-0.0597;1.0212,0.984,-0.0401;0.5775,2.3838,-0.135;0.9248,2.9614,0.7265;0.9784,2.8417,-1.0444;-0.9704,2.2772,-0.133;-1.6916,3.1701,0.7219;-1.8656,3.3007,-0.6955;-2.8433,3.0058,-1.075;-1.4354,4.1986,-1.1402)|",4.27762972986
"[H]C1=N[C@@]2([H])/C(=N\C(=O)/C([H])=C\2[H])C([H])=C1F |(2.5271,1.8745,2.4993;2.1505,0.8711,2.2942;2.5631,-0.1028,3.013;1.9906,-1.4243,2.7513;1.1646,-1.494,3.4888;1.3252,-1.6579,1.397;1.1823,-2.811,0.8346;1.8154,-3.942,1.4194;1.5371,-5.0629,1.0329;2.8457,-3.7123,2.4661;3.4787,-4.56,2.7088;2.9466,-2.5337,3.0891;3.6663,-2.3378,3.8794;0.7967,-0.4749,0.737;0.1775,-0.5898,-0.1456;1.2066,0.7265,1.1849;0.8224,1.8698,0.6038)|",3.872180092615
"[H]/C(=C(/[H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])[H])C(C(=O)OC([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H] |(3.6363,1.4266,0.3447;2.9014,0.668,0.6224;3.3068,-0.5975,0.757;2.5724,-1.357,1.0371;4.7178,-1.086,0.5683;5.3494,-0.2516,0.2408;4.7426,-1.834,-0.24;5.294,-1.7337,1.846;4.5845,-2.5004,2.18;5.3235,-0.9771,2.6411;6.6945,-2.3573,1.6548;6.6598,-2.9674,0.7422;7.7562,-1.2633,1.4377;7.5229,-0.665,0.5512;8.7558,-1.6849,1.2869;7.8093,-0.5788,2.292;7.0889,-3.3493,2.8116;8.4856,-3.9116,2.5113;9.4598,-3.8147,3.2266;8.5163,-4.5674,1.324;9.7835,-5.1426,0.9702;9.6279,-5.6222,0.0032;10.0952,-5.8769,1.7179;10.5518,-4.3682,0.8957;7.1322,-2.6684,4.1906;6.1458,-2.272,4.4511;7.8573,-1.8513,4.2207;7.4253,-3.3865,4.9622;6.1191,-4.5564,2.8646;5.1369,-4.2537,3.2399;6.5012,-5.326,3.5455;5.9903,-5.0108,1.8778;1.4934,1.1544,0.8176;1.4403,1.918,1.6056;0.8202,0.3359,1.0944;1.1014,1.6207,-0.0967)|",6.887201556155
"[H]C(=O)[C@@]1([H])C([H])([H])C2=C(SC([H])=C2[H])[C@@](C([H])=C(C([H])([H])[H])C([H])([H])[H])(C([H])([H])[H])C1([H])[H] |(5.3715,0.9923,-4.8878;6.0778,0.2715,-4.4129;7.0579,-0.1117,-5.0137;5.696,-0.1412,-3.0076;4.6852,-0.5759,-3.0838;6.6643,-1.1664,-2.4089;7.6954,-0.8453,-2.6104;6.5536,-2.1309,-2.9199;6.4391,-1.3257,-0.926;5.6866,-0.4448,-0.1893;5.6257,-0.9321,1.4942;6.6259,-2.3169,1.1816;6.896,-2.9835,1.9896;6.9787,-2.3926,-0.1334;7.6024,-3.1822,-0.541;4.9456,0.7774,-0.7173;3.4506,0.4286,-0.8326;3.2161,-0.5826,-0.5045;2.4069,1.1625,-1.2566;1.0188,0.5598,-1.25;0.5804,0.5659,-2.258;0.3399,1.145,-0.6139;1.0202,-0.4716,-0.8855;2.4335,2.5881,-1.7563;1.8017,3.2243,-1.1209;2.0071,2.6463,-2.7674;3.4267,3.0362,-1.787;5.1735,1.9771,0.2368;4.7358,2.8961,-0.1626;6.245,2.1464,0.3891;4.716,1.7929,1.2143;5.5745,1.1056,-2.105;4.9758,1.8731,-2.6056;6.5755,1.53,-1.9488)|",5.379690824385
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"[H]C1=C([H])C2=C(C([H])=C1OC([H])([H])[H])C([H])=C(C1=N[C@]([H])(C([H])([H])[H])N([H])O1)N2[H] |(7.6274,3.5607,6.4629;6.7838,3.4728,5.7859;6.5466,2.2982,5.097;7.2009,1.4398,5.2208;5.4388,2.2565,4.2379;4.5797,3.3796,4.0743;4.8408,4.5703,4.7883;4.1886,5.4273,4.6662;5.9395,4.6044,5.6357;6.313,5.687,6.3883;5.5267,6.8609,6.295;5.9927,7.5902,6.9605;4.4926,6.6821,6.6204;5.5149,7.2596,5.2712;3.5668,2.9954,3.1439;2.7428,3.5932,2.7814;3.8305,1.69,2.7768;3.1447,0.7979,1.8692;3.451,-0.4252,1.6448;2.5639,-0.8414,0.551;2.1029,-1.8107,0.7758;3.3241,-0.9162,-0.7719;4.1553,-1.6229,-0.6869;3.7263,0.0682,-1.032;2.6564,-1.2428,-1.5756;1.4817,0.1861,0.4916;0.761,-0.087,1.1673;2.0904,1.3301,1.171;4.9611,1.2467,3.4363;5.3357,0.3168,3.3192)|",4.329331361455
"[H]C([H])=C([H])C([H])([H])[C@@]([H])(C([H])=C([H])[H])N([H])[S@](=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(0.5299,2.9675,-2.7276;1.4886,2.458,-2.7722;2.3254,3.0332,-3.1645;1.6294,1.194,-2.3713;0.7653,0.6586,-1.9795;2.9201,0.4227,-2.4088;3.7096,1.0563,-2.8353;2.8245,-0.4537,-3.0667;3.3808,-0.0793,-1.0157;3.4462,0.7888,-0.343;2.4159,-1.0828,-0.4384;2.3545,-2.0271,-0.9809;1.6533,-0.8716,0.6344;0.9523,-1.6205,0.993;1.7059,0.0562,1.1998;4.7082,-0.72,-1.205;5.3214,-0.0782,-1.7134;5.5001,-1.1472,0.2818;6.1267,0.0764,0.9079;6.9269,-2.1228,-0.4942;7.735,-1.2094,-1.4191;8.6853,-1.6911,-1.6798;7.9601,-0.2614,-0.9183;7.2077,-1.0059,-2.3579;7.7743,-2.5447,0.7161;8.6348,-3.1332,0.3756;7.1986,-3.1651,1.4134;8.1442,-1.6699,1.2588;6.3458,-3.3397,-1.2183;7.1649,-3.9716,-1.5845;5.7267,-3.0427,-2.0683;5.7327,-3.9503,-0.5444),wD:17.17|",6.125282774755
"[H]C([H])=C([H])C([H])([H])[C@@]([H])(/C([H])=C(\[H])C([H])([H])[H])N([H])[S@](=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(0.5207,0.8544,4.0597;1.1935,1.1138,3.2461;1.8633,1.9541,3.4203;1.1853,0.4476,2.0892;0.4845,-0.3744,1.9457;2.0735,0.7487,0.9105;2.522,1.7442,1.0149;1.4574,0.7583,0.0023;3.2358,-0.2704,0.6986;3.6933,-0.0616,-0.2754;4.2937,-0.1377,1.7649;3.9505,-0.2608,2.7933;5.577,0.1426,1.5268;5.904,0.2727,0.4933;6.6376,0.3015,2.5779;7.0927,1.3004,2.5357;7.4525,-0.4199,2.43;6.2326,0.1552,3.5848;2.8072,-1.6943,0.6505;2.3776,-1.9705,1.536;1.7621,-2.1095,-0.6571;0.311,-1.849,-0.311;1.9757,-3.9881,-0.5447;1.5204,-4.4687,0.8354;1.432,-5.5618,0.8361;0.5399,-4.0467,1.0807;2.2369,-4.199,1.619;1.0437,-4.5325,-1.6377;1.095,-5.6277,-1.6497;1.3357,-4.1719,-2.6314;0.0075,-4.2345,-1.4539;3.4393,-4.3239,-0.839;3.567,-5.4136,-0.8583;4.1045,-3.9072,-0.0786;3.7495,-3.937,-1.8171),wU:20.20|",6.147051882795001
"[H]C1=C([H])C([H])=C2C(=C1[H])/C([H])=C(/C1=N[C@]([H])(C([H])([H])[H])N([H])O1)N2[H] |(7.0859,4.226,6.9896;6.4607,3.9774,6.1367;5.7757,5.0119,5.46;5.8869,6.0363,5.8045;4.964,4.7485,4.3655;4.4387,5.5464,3.8478;4.8483,3.4142,3.9544;5.5315,2.353,4.6218;6.3462,2.6575,5.7296;6.8749,1.8665,6.2549;5.1911,1.1433,3.94;5.5237,0.141,4.1693;4.3379,1.4879,2.9132;3.6848,0.6827,1.9049;2.9508,1.1248,0.9536;2.4162,-0.0765,0.2992;2.5261,-0.0027,-0.7893;0.9496,-0.2933,0.6668;0.3559,0.5769,0.3704;0.8486,-0.4348,1.7476;0.5593,-1.1827,0.1618;3.2633,-1.2107,0.7764;4.0839,-1.2608,0.1645;3.8551,-0.6745,2.0027;4.1299,2.8567,2.9254;3.5462,3.3283,2.2499)|",4.536137887835
"[H]C1=C([H])C([H])=C2C(=C1[H])C([H])=C(C1=N[C@]([H])(N([H])[H])N([H])O1)N2[H] |(3.0013,-1.0471,0.4145;2.0322,-0.593,0.2284;1.9539,0.8038,0.0256;2.8637,1.3968,0.06;0.7418,1.4343,-0.2166;0.6852,2.5082,-0.372;-0.4065,0.6322,-0.2522;-0.3518,-0.7803,-0.0503;0.8965,-1.3862,0.1929;0.9634,-2.4596,0.3494;-1.6907,-1.2693,-0.1569;-2.0289,-2.2912,-0.0609;-2.4964,-0.1793,-0.4106;-3.9244,-0.0733,-0.6125;-4.5558,1.0065,-0.8931;-5.9722,0.6524,-0.8569;-6.5,0.9924,-1.7559;-6.6016,1.2642,0.286;-6.1206,0.9769,1.1375;-7.5633,0.9348,0.3583;-6.0076,-0.8556,-0.8573;-6.0262,-1.1651,-1.8346;-4.6489,-1.2243,-0.4661;-1.721,0.9661,-0.4656;-2.1086,1.882,-0.6413)|",4.5198110568050005
"[H]C1=C([H])C([H])=C(O/N=C(/C([H])([H])[H])C([H])([H])C(=O)C([H])([H])[H])C([H])=C1[H] |(2.5914,0.5217,6.1516;2.4128,0.2876,5.1062;2.4385,1.2964,4.1427;2.6341,2.3241,4.4376;2.2139,1.0119,2.7939;2.2314,1.7902,2.0429;1.9599,-0.3083,2.4189;1.7279,-0.7219,1.1189;1.7117,0.3377,0.1851;1.6074,-0.094,-1.0191;1.5297,0.9446,-2.1019;2.3941,0.8622,-2.7734;1.504,1.9495,-1.675;0.6328,0.7776,-2.7101;1.5136,-1.55,-1.4281;1.9915,-2.1831,-0.6786;2.0074,-1.6772,-2.3964;0.0304,-1.9303,-1.6168;-0.5557,-1.6314,-2.6384;-0.6379,-2.6496,-0.466;-0.4674,-2.1048,0.4694;-0.1862,-3.6425,-0.3398;-1.7067,-2.758,-0.6602;1.9299,-1.3314,3.3712;1.7305,-2.3489,3.0488;2.1569,-1.0282,4.7116;2.1314,-1.8254,5.4497)|",4.971520048635
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"[H]OC1=C(C(=O)/C([H])=C(\[H])C2=C([H])C([H])=C(F)C([H])=C2[H])OC([H])=C1[H] |(5.5567,4.4695,2.6014;4.7522,4.085,2.2204;4.3259,4.8781,1.2152;3.2,4.6454,0.4458;2.1981,3.5831,0.4672;2.2854,2.6668,1.286;1.0991,3.6706,-0.5279;1.1012,4.5126,-1.2126;0.1445,2.7206,-0.5561;0.2446,1.923,0.1793;-1.0079,2.6286,-1.4504;-1.2841,3.5754,-2.4577;-0.6223,4.4245,-2.5969;-2.3933,3.4457,-3.2844;-2.6121,4.1702,-4.062;-3.2419,2.3557,-3.1039;-4.318,2.2277,-3.9056;-3.0096,1.3979,-2.1245;-3.693,0.5624,-2.0159;-1.8931,1.5435,-1.3052;-1.6978,0.8026,-0.5344;3.0912,5.6724,-0.4801;4.1249,6.5129,-0.2825;4.1723,7.3698,-0.9383;4.9215,6.0877,0.7405;5.8168,6.5697,1.1102)|",4.179668743680001
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"[H]OC1=C(C(=O)/C([H])=C(\[H])C2=C([H])C([H])=C([H])C(C#N)=C2[H])OC([H])=C1[H] |(-4.9364,-7.6355,-0.8499;-4.3151,-6.891,-0.8417;-4.9973,-5.7555,-0.5917;-4.4313,-4.4949,-0.5108;-3.0589,-4.0253,-0.6566;-2.1481,-4.8191,-0.8931;-2.8249,-2.5624,-0.5053;-3.6839,-1.9308,-0.3025;-1.5782,-2.0706,-0.6217;-0.7917,-2.7963,-0.8245;-1.1543,-0.6733,-0.5067;-2.046,0.389,-0.2581;-3.1063,0.1855,-0.1452;-1.5906,1.6997,-0.1557;-2.2958,2.5029,0.0367;-0.2343,1.9919,-0.2978;0.1277,3.0116,-0.2185;0.6701,0.9476,-0.5463;2.0688,1.2316,-0.697;3.2014,1.4663,-0.8192;0.209,-0.3726,-0.649;0.9187,-1.1713,-0.8419;-5.4425,-3.5845,-0.2414;-6.5983,-4.2708,-0.1582;-7.4831,-3.6876,0.0508;-6.3999,-5.6054,-0.3628;-7.1524,-6.3826,-0.3522)|",4.068102064975
"[H]OC1=C(C(=O)/C([H])=C(\[H])C2=C([H])C([H])=C([H])C([H])=N2)OC([H])=C1[H] |(5.0408,6.8742,0.6968;4.3465,6.2003,0.7586;3.7039,6.13,-0.4261;2.6526,5.2748,-0.7076;1.9639,4.2688,0.0939;2.3021,4.0663,1.2612;0.8583,3.5218,-0.5623;0.6063,3.7378,-1.5948;0.1767,2.5875,0.1228;0.4643,2.4051,1.1566;-0.9297,1.7898,-0.4137;-1.567,0.8403,0.4031;-1.2358,0.7021,1.4283;-2.6176,0.0887,-0.1174;-3.1234,-0.651,0.4973;-3.0043,0.3049,-1.438;-3.8174,-0.2547,-1.8903;-2.3124,1.2716,-2.1767;-2.5879,1.4681,-3.2122;-1.3058,1.9977,-1.6944;2.2583,5.4943,-2.0182;3.0463,6.4596,-2.5268;2.8503,6.7413,-3.5509;3.9555,6.8951,-1.6063;4.7065,7.6613,-1.7459)|",4.1089191425500005
"[H]OC1=C(C(=O)/C([H])=C(\[H])C2=C([H])C([H])=C([H])C([H])=C2[H])OC([H])=C1[H] |(6.6276,4.0177,-0.6763;5.9136,3.3628,-0.6403;6.4291,2.1811,-0.241;5.7029,1.0152,-0.0761;4.2881,0.7044,-0.2642;3.5066,1.5825,-0.633;3.8605,-0.692,0.0069;4.6184,-1.4012,0.3242;2.5676,-1.0382,-0.1429;1.8954,-0.2432,-0.4642;1.9553,-2.349,0.0737;0.5702,-2.4882,-0.1319;-0.0054,-1.6202,-0.444;-0.0678,-3.7121,0.0591;-1.1389,-3.7947,-0.1044;0.6688,-4.8274,0.4599;0.1756,-5.7841,0.6092;2.0471,-4.7079,0.6678;2.6263,-5.5733,0.979;2.683,-3.4859,0.4774;3.7536,-3.4101,0.6424;6.5745,0.0204,0.3413;7.8037,0.5636,0.4303;8.5944,-0.0998,0.7485;7.7881,1.8844,0.0876;8.6326,2.5606,0.0708)|",4.206880128730001
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"[H]C([H])=C(C([H])([H])Cl)[C@]1([H])C([H])([H])C([H])([H])[C@]2(C([H])([H])[H])O[C@]2([H])C1([H])[H] |(1.811,1.33,2.1948;2.4638,0.635,1.6712;3.4442,0.4596,2.099;2.0506,0.0038,0.5655;0.6468,0.2346,0.0806;0.0647,0.8279,0.7855;0.1157,-0.6961,-0.1323;0.5927,1.1641,-1.5114;2.8787,-0.9778,-0.2566;2.5621,-0.8272,-1.2963;2.559,-2.4533,0.0908;1.4767,-2.629,0.0534;2.9996,-3.0861,-0.6925;3.1075,-2.8945,1.4558;2.9294,-3.969,1.5845;2.5708,-2.3846,2.2644;4.5995,-2.6014,1.6152;5.4612,-3.7199,2.158;5.437,-4.5906,1.4914;5.0972,-4.0413,3.1417;6.4994,-3.3926,2.2687;4.8985,-1.3016,2.1838;5.1994,-1.5258,0.7971;6.2727,-1.5801,0.5955;4.414,-0.7556,-0.2453;4.6442,0.3135,-0.1605;4.8055,-1.0758,-1.2208)|",6.576991766585
"[H]O[C@@]([H])(/C(=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])[H])C([H])([H])N(=O)=O |(2.4077,-2.51,0.934;3.0366,-2.0064,0.3934;2.7834,-0.6162,0.5881;3.2939,-0.1111,-0.2353;1.31,-0.2475,0.5689;0.8625,0.5263,-0.4391;1.586,0.7928,-1.21;-0.4947,1.0473,-0.6836;-0.9435,1.1588,-2.0129;-0.286,0.8469,-2.821;-2.215,1.6455,-2.3048;-2.5426,1.7136,-3.3388;-3.0624,2.0545,-1.2721;-4.0522,2.4419,-1.4977;-2.6218,1.9794,0.05;-3.2642,2.3183,0.8584;-1.3515,1.4833,0.3429;-1.0074,1.4729,1.3717;0.4551,-0.7978,1.6874;-0.6001,-0.8323,1.4078;0.5462,-0.1946,2.5998;0.7508,-1.8217,1.9584;3.4968,-0.1981,1.8986;4.5608,-0.423,1.8174;3.0634,-0.6806,2.7743;3.3766,1.2813,2.1132;2.6071,1.6738,2.9878;4.0319,2.0009,1.3641)|",4.43001348614
"[H]O[C@@]([H])(C([H])([H])C(=O)OC([H])([H])[H])C([H])([H])/C([H])=C(\[H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(1.4241,-0.1494,-0.2188;0.5162,0.0263,-0.5164;-0.3549,-0.3918,0.5284;-1.3602,-0.1561,0.1635;-0.2556,-1.9278,0.7121;0.7239,-2.182,1.1285;-0.3614,-2.386,-0.2747;-1.3093,-2.4739,1.6494;-1.1815,-2.6181,2.8487;-2.458,-2.7478,0.99;-3.5395,-3.2267,1.8069;-3.8027,-2.4877,2.5685;-3.2613,-4.1607,2.3023;-4.3733,-3.3882,1.1232;-0.1081,0.3991,1.8361;-0.3883,1.4421,1.6421;-0.7761,0.0147,2.6144;1.3245,0.3453,2.3143;2.0068,1.0408,1.8198;1.7973,-0.4733,3.2695;1.0733,-1.1524,3.7311;3.5793,-0.541,3.8769;3.6154,-0.1315,5.7286;4.6322,-0.2142,6.1319;2.9753,-0.8142,6.2998;3.2632,0.8894,5.9162;4.2649,-2.2908,3.6148;5.2934,-2.3682,3.9882;4.2733,-2.5628,2.5528;3.6628,-3.0387,4.1443;4.6445,0.7073,2.9272;5.6822,0.6696,3.2798;4.2873,1.7342,3.0666;4.6568,0.4999,1.8506)|",6.6695104757550006
"[H]O[C@]([H])(C([H])([H])Cl)C([H])([H])/C([H])=C(\[H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(5.7084,3.8856,3.022;6.1743,3.4094,2.313;5.507,3.7178,1.0988;6.1261,3.2642,0.3128;5.5301,5.2221,0.82;5.0533,5.4676,-0.1309;6.5553,5.5949,0.8313;4.6448,6.1695,2.1056;4.0976,3.0972,1.0292;3.4989,3.5246,1.8478;3.6109,3.3956,0.0912;4.1253,1.5956,1.1433;4.6668,1.219,2.0116;3.5625,0.7391,0.277;3.0365,1.1758,-0.5792;3.6035,-1.1378,0.4128;4.4583,-1.8536,-1.1232;4.4647,-2.9502,-1.0966;5.4986,-1.5147,-1.192;3.9478,-1.5481,-2.0444;1.8263,-1.7986,0.4933;1.8144,-2.8949,0.53;1.2429,-1.4906,-0.3828;1.3066,-1.429,1.3849;4.5486,-1.6584,1.9701;4.575,-2.7514,2.0552;4.0765,-1.2688,2.8795;5.5856,-1.3035,1.9528)|",6.876317002135
"[H]C([H])=C([H])C([H])([H])C([H])([H])/C(=N/OC([H])([H])[H])C1=C([H])C([H])=C(OC([H])([H])[H])C([H])=C1[H] |(0.8323,-1.7185,-2.4122;1.0238,-1.1618,-3.326;0.2746,-1.2484,-4.1117;2.1201,-0.4177,-3.4801;2.8485,-0.3569,-2.6736;2.4461,0.3804,-4.7146;1.6989,0.1681,-5.4895;3.4221,0.0692,-5.1076;2.4715,1.9056,-4.4868;1.5319,2.2075,-4.0064;2.4764,2.4177,-5.4602;3.6416,2.4567,-3.6867;4.5432,1.5976,-3.3592;5.6143,2.142,-2.6368;6.4938,1.0846,-2.2751;5.9903,0.3534,-1.631;7.311,1.5589,-1.7258;6.8876,0.573,-3.1616;3.6645,3.9202,-3.4134;4.8364,4.6875,-3.5741;5.7567,4.1964,-3.8646;4.831,6.0575,-3.3666;5.7321,6.6485,-3.4989;3.6534,6.7122,-2.9733;3.757,8.0584,-2.786;2.6016,8.7712,-2.3733;2.9121,9.8129,-2.2737;2.2276,8.4106,-1.406;1.7981,8.7025,-3.1185;2.48,5.9702,-2.8029;1.5553,6.4455,-2.4964;2.4957,4.5932,-3.0333;1.573,4.0391,-2.8894)|",5.210980237075
"[H]C1=C(C([H])([H])[H])[C@]([H])(C([H])([H])C#N)OC(=O)[C@@]1([H])C([H])([H])C([H])([H])[H] |(3.0631,-2.4313,-0.4699;2.6312,-1.8978,-1.3158;2.0886,-2.5835,-2.3233;2.0201,-4.0878,-2.3736;2.4476,-4.5271,-1.4679;2.5704,-4.4987,-3.2309;0.9829,-4.4379,-2.4642;1.5017,-1.8397,-3.4975;0.5413,-2.2935,-3.7737;2.3854,-1.8997,-4.7725;2.4834,-2.9393,-5.106;1.8827,-1.343,-5.571;3.7216,-1.336,-4.5655;4.7744,-0.8842,-4.3802;1.1862,-0.4636,-3.2458;1.73,0.277,-2.235;1.4099,1.4363,-2.1492;2.7105,-0.395,-1.2771;3.707,-0.0959,-1.6405;2.5592,0.183,0.1495;3.3732,-0.2291,0.7604;2.7151,1.264,0.0937;1.2096,-0.1129,0.8106;1.1816,0.2949,1.8268;1.0182,-1.1906,0.8783;0.3889,0.3438,0.2473)|",6.8354999245600006
"[H]O[C@@]([H])(/C(=C(\[H])C([H])([H])C([H])([H])[C@@]([H])(C([H])=C([H])[H])C([H])([H])[H])C([H])([H])[H])C([H])([H])C#N |(8.8233,-1.3799,2.6244;9.043,-2.2771,2.9303;8.6647,-3.1771,1.9067;8.9302,-4.1706,2.2931;7.1763,-3.1651,1.5674;6.3306,-2.4056,2.2782;6.7551,-1.8242,3.0956;4.8483,-2.2307,2.0962;4.4516,-2.9101,1.3333;4.3363,-2.4869,3.0343;4.4834,-0.7814,1.7161;4.9785,-0.5234,0.7708;4.8903,-0.0923,2.4713;2.9647,-0.531,1.573;2.564,-1.2616,0.8547;2.2439,-0.7189,2.8851;2.5568,-0.0467,3.6883;1.2759,-1.6048,3.1239;0.7893,-1.6762,4.0932;0.928,-2.2925,2.3553;2.7039,0.8825,1.0168;1.6321,1.0585,0.8757;3.2053,1.0228,0.052;3.0818,1.6507,1.7034;6.7716,-4.0836,0.4367;5.6951,-4.2652,0.4193;7.2657,-5.0609,0.5278;7.0487,-3.6753,-0.5447;9.5737,-2.9529,0.6588;10.6178,-3.0523,0.9744;9.3859,-3.7064,-0.1132;9.3816,-1.6174,0.0874;9.2073,-0.5434,-0.3182)|",6.859990171105
"[H]O[C@@]([H])(C([H])([H])O[Si](C([H])([H])[H])(C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[C@@]1([H])OC1([H])[H] |(3.3875,4.7247,-0.3507;4.004,3.9722,-0.275;3.3007,2.8651,-0.8128;4.0461,2.1173,-1.1104;2.3991,2.2479,0.2668;1.6371,2.9861,0.5609;3.0204,2.0315,1.1465;1.7953,1.0698,-0.2448;0.7048,0.1051,0.6154;-0.7989,1.1397,1.1147;-1.5424,0.5287,1.6404;-1.2887,1.5888,0.2435;-0.5138,1.955,1.7903;1.5469,-0.5589,2.1745;0.8858,-1.2373,2.7268;1.8119,0.2582,2.8562;2.4669,-1.1049,1.9379;0.2378,-1.2941,-0.602;-0.3836,-0.6983,-1.8823;-0.6466,-1.5005,-2.5869;0.3124,-0.0226,-2.3918;-1.3017,-0.1365,-1.6706;-0.7852,-2.2407,0.0628;-1.0502,-3.0593,-0.622;-1.7164,-1.7228,0.323;-0.3899,-2.7016,0.9768;1.5001,-2.0972,-0.9807;1.2487,-2.8921,-1.6983;1.9541,-2.58,-0.1067;2.2622,-1.4608,-1.4443;2.4953,3.2855,-2.0323;1.7849,2.5499,-2.4062;1.9672,4.6281,-1.9374;2.973,4.3404,-2.9314;2.6209,4.3729,-3.9617;3.942,4.799,-2.7446)|",8.699479800485001
"[H]O/C(=N/C1=NC([H])=C(/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])O[H])N=C1[H])C([H])([H])[H] |(3.7757,0.2853,-1.6448;3.8192,-0.319,-0.8875;3.002,0.1362,0.0982;2.9379,-0.6019,1.1357;2.2609,-0.2533,2.2934;2.1836,1.0172,2.7237;1.5636,1.2208,3.8895;1.5124,2.2524,4.237;1.0032,0.1888,4.6565;0.3372,0.4713,5.9302;0.3197,1.5187,6.2322;-0.226,-0.4562,6.7194;-0.193,-1.4938,6.3878;-0.9099,-0.1791,8.0245;-0.8185,0.8844,8.2833;-0.4184,-0.7404,8.8315;-2.3983,-0.5786,8.0154;-2.4972,-1.624,7.6841;-2.9443,0.0271,7.2794;-3.0679,-0.4222,9.3835;-4.1398,-0.6661,9.3037;-2.9958,0.6175,9.7222;-2.4453,-1.1912,10.4082;-2.543,-2.1268,10.1706;1.0877,-1.0897,4.219;1.712,-1.3,3.072;1.8003,-2.3257,2.7203;2.291,1.4342,-0.208;1.4418,1.5884,0.4564;1.9468,1.4409,-1.2501;2.9722,2.2807,-0.0629)|",4.269466314345
"[H]C1=C([H])C(=O)C([H])=C(C([H])([H])[H])C1C1N([H])C([H])([H])N([H])[C@]([H])(N([H])C([H])([H])[H])C1([H])[H] |(5.1145,1.9134,0.4633;4.5313,1.0338,0.1987;5.185,-0.1528,0.1613;6.2534,-0.2166,0.3469;4.4678,-1.403,-0.0815;5.0329,-2.5034,-0.1429;3.0202,-1.2504,-0.2153;2.473,-2.1767,-0.3711;2.3464,-0.0633,-0.1646;0.8442,-0.1314,-0.3613;0.5401,-1.1758,-0.4694;0.5175,0.393,-1.2683;0.2831,0.2842,0.4825;3.0978,1.1825,0.0161;2.563,2.4805,0.0128;3.3842,3.5548,-0.1462;4.3344,3.3715,-0.4315;3.073,4.9504,0.1358;3.0493,5.5029,-0.8258;3.8792,5.3788,0.7451;1.8278,5.0313,0.8662;1.5533,6.0052,0.9843;0.7706,4.3335,0.1421;0.7233,4.646,-0.9197;-0.4961,4.714,0.7397;-0.5153,4.3884,1.7055;-1.6882,4.2687,0.0232;-2.5745,4.6497,0.5398;-1.7956,3.1746,-0.0733;-1.6831,4.6956,-0.9861;1.0954,2.8266,0.206;0.7842,2.4522,1.1892;0.4907,2.3058,-0.5375)|",3.5755759955700004
"[H]C1=C([H])[C@@]2([H])C(C(=O)OC([H])([H])[H])NN(C([H])([H])[H])C2=C([H])C1=O |(6.4697,-2.0606,-2.5216;5.716,-1.8707,-1.7626;5.7991,-0.8163,-0.9381;6.626,-0.1136,-0.9603;4.7941,-0.6657,0.1683;5.2951,-1.0859,1.0654;4.1911,0.6388,0.6482;4.9746,1.8566,0.9149;6.1759,1.9097,0.7101;4.238,2.8774,1.3841;4.9688,4.0878,1.6448;5.4454,4.4528,0.7312;5.7374,3.9166,2.4031;4.2265,4.8008,2.0035;2.9337,0.5062,0.9456;2.5451,-0.7692,0.627;1.1297,-1.0793,0.5879;0.9659,-2.099,0.9486;0.7318,-0.9918,-0.4317;0.6152,-0.3752,1.242;3.5361,-1.4571,-0.0552;3.4538,-2.5397,-0.859;2.5473,-3.1211,-0.9903;4.5991,-2.8632,-1.7248;4.6139,-3.8533,-2.4532)|",4.00007360235
"[H]OC([H])([H])[C@@]1([H])O[C@]([H])(OC([H])([H])[H])[C@](OC([H])([H])[H])(C([H])([H])[H])[C@]1([H])O[H] |(5.9673,0.5314,-2.7814;6.2485,1.4579,-2.699;5.406,2.0203,-1.7067;5.6074,1.5888,-0.7157;5.6296,3.0924,-1.6732;3.9349,1.8106,-2.0531;3.708,2.3172,-2.9983;3.7452,0.3793,-2.2397;2.6262,-0.0472,-1.5015;2.7855,-1.0987,-1.2345;1.4253,0.1034,-2.2415;1.3719,-0.7097,-3.4104;0.3976,-0.5331,-3.8712;1.4604,-1.7735,-3.1471;2.1692,-0.4483,-4.1145;2.5473,0.8963,-0.2837;3.638,0.4578,0.5376;3.7672,1.0576,1.8161;4.7189,0.7022,2.2194;2.9615,0.7579,2.499;3.7956,2.1555,1.7695;1.2106,0.8864,0.4505;0.3868,1.0641,-0.2397;1.1764,1.665,1.2183;1.0581,-0.0866,0.9309;2.9158,2.2446,-0.9748;3.3544,2.95,-0.2633;1.7851,2.9011,-1.5254;1.3477,2.2458,-2.0993)|",8.6532204459
"[H]C1=N[C@@]([H])(Cl)C([H])=N/C1=C1\C(=O)C([H])=C([H])C([H])=C1[H] |(2.6536,-2.592,0.6535;1.81,-3.2172,0.3652;2.058,-4.4512,0.1297;0.9273,-5.2782,-0.2441;0.8924,-5.3432,-1.3379;1.2122,-6.992,0.3143;-0.3729,-4.772,0.3298;-1.13,-5.4893,0.6437;-0.5828,-3.5229,0.5123;0.4538,-2.6352,0.261;0.2233,-1.3051,-0.0349;-1.186,-0.8193,-0.258;-2.1183,-1.5851,-0.4943;-1.3833,0.6358,-0.2314;-2.411,0.9786,-0.2969;-0.3289,1.4866,-0.2431;-0.4909,2.5606,-0.2933;1.0294,0.9941,-0.2103;1.8475,1.7075,-0.2437;1.293,-0.3362,-0.1182;2.3244,-0.6698,-0.0829)|",2.86807998427
"[H]OC(=O)C1=C([H])C([H])=C([H])C([H])=C1[C@]1([H])N([H])[C@@]([H])(N([H])[H])N=C(O[H])C1([H])[H] |(1.3546,-1.6992,-2.005;0.5988,-1.3544,-1.4947;0.887,-0.0459,-1.2685;1.9333,0.4316,-1.6779;-0.1777,0.6599,-0.5069;-1.3949,-0.0144,-0.292;-1.5163,-1.0134,-0.6932;-2.4304,0.5799,0.4178;-3.3649,0.0465,0.5659;-2.2484,1.8584,0.9457;-3.0432,2.3343,1.5143;-1.0416,2.5277,0.7547;-0.8796,3.5015,1.2025;0.0087,1.9635,0.0216;1.2836,2.7895,-0.1795;2.1357,2.1082,-0.1487;1.4562,3.8156,0.87;2.3542,3.662,1.3262;1.4805,5.2164,0.3754;0.4567,5.4408,0.0361;1.823,6.1453,1.4278;2.8352,6.1763,1.5308;1.409,5.843,2.307;2.4201,5.4187,-0.7215;2.3065,4.5533,-1.6454;3.138,4.638,-2.7171;2.9716,3.8837,-3.3028;1.2925,3.4352,-1.6021;0.3022,3.8608,-1.8124;1.4853,2.6559,-2.3431)|",4.487157394745
"[H]C(=O)C1=C([H])C(=O)N=C2C(Cl)=C([H])C([H])=C(OC([H])([H])[H])[C@@]21[H] |(0.8965,4.9408,2.1895;1.4994,4.0835,1.8216;1.7504,3.9437,0.6419;1.8734,3.1034,2.8719;1.1543,3.0691,4.0074;0.4146,3.8329,4.2353;1.243,1.9443,4.9748;0.595,1.9645,6.0074;2.0671,0.8449,4.6604;2.8522,0.9044,3.6369;3.8383,-0.1404,3.3901;3.6673,-1.6733,4.2061;4.9559,0.1431,2.6661;5.727,-0.616,2.5764;5.2166,1.4442,2.1012;6.2045,1.6449,1.7056;4.249,2.3964,2.1319;4.3604,3.6726,1.7435;5.5962,4.1014,1.1698;5.4558,5.1525,0.9184;5.8191,3.5273,0.2636;6.4158,3.9934,1.8895;2.8464,1.9987,2.5662;2.4513,1.4886,1.665)|",3.300741006565
"[H]C1=C(Cl)C2=NC(=O)C([H])=C(C([H])([H])[H])[C@@]2([H])C(OC([H])([H])[H])=C1[H] |(4.8299,0.2135,-0.1749;4.7055,-0.7833,-0.5861;5.409,-1.1356,-1.6914;6.5355,-0.0183,-2.418;5.3495,-2.5046,-2.2035;6.2875,-2.9415,-2.9684;6.2861,-4.2972,-3.3736;7.2117,-4.7338,-4.0376;5.1442,-5.1497,-2.9929;5.1747,-6.1625,-3.385;4.1049,-4.7288,-2.2531;2.9195,-5.6296,-2.0101;2.9603,-6.488,-2.6868;1.9737,-5.1006,-2.1768;2.8936,-5.9977,-0.9807;4.0796,-3.2776,-1.8072;3.3061,-2.7966,-2.4442;3.5996,-2.9358,-0.4011;2.8481,-3.8915,0.1802;2.2702,-3.6122,1.4543;1.5649,-2.7753,1.3868;3.0463,-3.3797,2.1925;1.7413,-4.5197,1.7466;3.8508,-1.7084,0.1164;3.4382,-1.3992,1.0688)|",3.687142674275
"[H]C([H])([H])C([H])([H])OC(=O)/C(C#N)=C(/Cl)C([H])([H])C([H])([H])C([H])([H])[H] |(4.6202,-3.4986,3.9791;4.9052,-2.5966,3.4257;4.1583,-1.8228,3.6243;5.8773,-2.2572,3.7968;4.9749,-2.911,1.9397;3.9999,-3.205,1.5443;5.7071,-3.6923,1.726;5.4569,-1.7691,1.1835;4.5484,-0.8485,0.8247;3.3599,-0.9233,1.0724;5.2135,0.2762,0.0858;6.6456,0.2856,0.0712;7.8085,0.2908,0.0662;4.4955,1.2487,-0.5417;5.3642,2.5428,-1.333;3.0033,1.3781,-0.6207;2.7486,1.8046,-1.5972;2.5562,0.3861,-0.5407;2.4301,2.2768,0.5008;2.6835,1.8267,1.4668;2.9126,3.2611,0.4636;0.912,2.4315,0.3784;0.5211,3.0673,1.1801;0.4089,1.4601,0.4459;0.6327,2.8901,-0.5779)|",5.2055379600650005
"[H]C1C([H])=C(OC([H])([H])[H])[C@]([H])(OC([H])([H])[H])C2=C1C(=O)NC([H])=N2 |(2.1156,3.7177,0.0546;2.8837,2.9661,0.2207;2.5788,1.8234,1.0046;1.5867,1.6887,1.4216;3.5346,0.8765,1.2475;3.1841,-0.1843,1.9786;4.1146,-1.2107,2.3509;4.8079,-0.8398,3.1069;4.674,-1.5781,1.4845;3.4982,-2.0156,2.7525;4.9512,1.0115,0.712;5.1897,0.1113,0.1189;5.7927,1.0668,1.8613;7.1392,0.6388,1.6496;7.6221,0.6876,2.6285;7.6581,1.2833,0.9367;7.1673,-0.3979,1.2796;5.154,2.2183,-0.2087;4.1125,3.1823,-0.3686;4.3779,4.3709,-1.2117;3.5468,5.2505,-1.3951;5.6538,4.4317,-1.8128;6.4809,3.4619,-1.5805;7.4617,3.5235,-2.0538;6.3138,2.318,-0.8064)|",2.99869463251
"[H]C1N[C@]2([H])C(=NC3=C([H])N=C([H])C([H])=C32)C(=O)N1[H] |(-4.3959,-0.9023,-1.0689;-3.4849,-1.4682,-1.2613;-2.4195,-0.8462,-1.5879;-1.2879,-1.7113,-1.9024;-1.3256,-1.8926,-2.9941;-1.2818,-3.0673,-1.211;-0.1502,-3.435,-0.7139;0.7397,-2.3551,-0.9499;2.0843,-2.2782,-0.598;2.5906,-3.1143,-0.1208;2.8023,-1.1707,-0.8324;2.1948,-0.1323,-1.423;2.8139,0.7498,-1.5763;0.8529,-0.1195,-1.8368;0.4226,0.7558,-2.3137;0.1176,-1.2623,-1.5809;-2.5777,-3.7702,-1.0223;-2.7615,-4.9427,-0.7715;-3.6365,-2.8509,-1.1214;-4.5529,-3.2269,-0.9048)|",4.438176901655001
"[H]C1=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])C2([H])[H])C([H])=C([H])C(C(F)(F)F)=N1 |(-0.9132,-0.1748,0.563;0.0321,-0.5168,0.1435;0.1275,-1.8118,-0.3881;-1.0707,-2.7301,-0.3895;-1.8931,-2.216,0.129;-1.54,-3.042,-1.7594;-0.7212,-3.22,-2.3423;-2.2557,-4.2921,-1.6819;-3.2501,-4.0896,-1.7694;-2.0461,-4.9254,-0.3553;-1.7679,-5.9778,-0.4873;-3.3009,-4.9206,0.3942;-3.4774,-4.0036,0.8048;-3.2647,-5.5898,1.161;-0.8726,-4.1202,0.251;-0.8982,-4.1129,1.3454;0.0895,-4.545,-0.061;1.3656,-2.1841,-0.9212;1.5119,-3.1739,-1.3469;2.4264,-1.2808,-0.9017;3.3954,-1.5421,-1.3099;2.2068,-0.0258,-0.3365;3.3094,1.0118,-0.2649;4.4557,0.5458,-0.8139;2.9705,2.1354,-0.9235;3.5799,1.3539,1.0093;1.0361,0.3588,0.1801)|",4.677637090095
"[H]C1=C(\[H])N2\C(=C/1C#N)C(=O)N([H])C([H])([H])N2[H] |(-3.4691,0.0337,0.1305;-2.3921,0.0062,0.0539;-1.5517,1.0997,-0.0618;-1.7659,2.157,-0.1238;-0.2691,0.6333,-0.0917;-0.2475,-0.7385,-0.0332;-1.5805,-1.1586,0.0711;-2.0467,-2.4949,0.193;-2.4901,-3.5652,0.3013;1.0224,-1.4753,-0.1333;1.1042,-2.6889,-0.2307;2.1238,-0.6335,-0.1607;3.0218,-1.0998,-0.1416;2.025,0.7256,0.328;2.9317,1.2754,0.0665;1.8879,0.7548,1.4247;0.9047,1.3808,-0.3686;0.7585,2.3034,0.0421)|",5.175605436510001
"[H]C1=C(C([H])([H])[H])ON=C1/C([H])=C(/N(=O)=O)C([H])([H])[H] |(3.7889,2.9567,-2.6478;2.7357,2.8068,-2.4624;1.6871,3.5023,-2.9808;1.5655,4.6463,-3.9262;2.5553,4.9588,-4.2673;1.076,5.5008,-3.4457;0.966,4.369,-4.8004;0.533,2.9918,-2.4876;0.8105,1.9395,-1.6262;2.1301,1.8294,-1.6123;2.8595,0.837,-0.8304;3.94,0.8854,-0.9156;2.3684,-0.1221,-0.0275;3.385,-0.9812,0.6382;2.9512,-1.8541,1.3904;4.5811,-0.7877,0.4152;0.9552,-0.461,0.3124;0.2756,0.1913,-0.2352;0.7803,-0.3474,1.3878;0.7405,-1.5064,0.0674)|",4.585118380925001
"[H]OC(=O)/C([H])=C(\[H])C([H])(F)F |(-1.4933,2.3203,-0.2599;-1.8864,1.4324,-0.2397;-0.9261,0.4872,-0.061;-1.2249,-0.6822,-0.0185;0.4702,1.0018,0.0753;0.6556,2.0734,0.0651;1.4858,0.1482,0.2065;1.2939,-0.9221,0.2184;2.9138,0.5687,0.3638;3.3209,0.3142,1.3505;3.6789,-0.0663,-0.5769;3.0607,1.9172,0.1898)|",5.853168924255
"[H]OC([H])([H])[C@]1([H])N(C([H])([H])C2=C([H])C(Cl)=C([H])C([H])=C2[H])C([H])([H])C([H])([H])N([H])C1([H])[H] |(-6.9341,-5.2459,0.7478;-6.2157,-5.3993,1.3793;-5.6897,-4.1218,1.7366;-6.4952,-3.3888,1.8604;-5.2069,-4.2595,2.7098;-4.6491,-3.6152,0.7102;-3.8431,-4.3605,0.6729;-4.0314,-2.3213,1.0532;-3.2536,-2.317,2.2867;-3.8782,-2.203,3.1923;-2.7702,-3.3001,2.371;-2.1743,-1.2438,2.3015;-1.8837,-0.5593,3.486;-2.4555,-0.7544,4.3884;-0.8534,0.3798,3.5086;-0.5016,1.2318,5.0108;-0.1039,0.6654,2.3701;0.6898,1.4039,2.4081;-0.4046,-0.0148,1.1879;0.1689,0.2001,0.29;-1.4276,-0.9606,1.1508;-1.6719,-1.4807,0.23;-4.9497,-1.1839,0.9427;-5.7499,-1.2126,1.7041;-4.3765,-0.2658,1.1109;-5.5783,-1.1389,-0.4498;-4.7878,-0.8962,-1.1846;-6.3302,-0.3422,-0.4849;-6.2258,-2.4223,-0.7318;-6.6738,-2.3889,-1.6452;-5.2368,-3.5018,-0.7024;-5.7191,-4.4464,-0.9742;-4.4024,-3.3358,-1.4086)|",5.401459932425
"[H]OC1=NC(=O)O[C@]1(C1=C([H])C(C([H])([H])[H])=C(Cl)C([H])=C1[H])C([H])([H])[H] |(3.4064,3.7686,-0.9241;3.7629,4.2645,-0.1682;4.8593,3.6586,0.2959;5.5146,4.1101,1.2957;6.623,3.2409,1.4603;7.4817,3.3054,2.2907;6.6189,2.248,0.4815;5.4618,2.4163,-0.3522;4.5893,1.1629,-0.2719;3.2755,1.1819,0.2062;2.832,2.109,0.5574;2.4832,0.0258,0.2807;1.0721,0.0935,0.8055;0.815,1.1151,1.0991;0.9412,-0.5561,1.6783;0.35,-0.2412,0.0517;3.0684,-1.1731,-0.1443;2.1427,-2.6679,-0.0912;4.3828,-1.2267,-0.6086;4.807,-2.1778,-0.9121;5.1392,-0.0619,-0.6722;6.169,-0.115,-1.0109;5.9225,2.7248,-1.7877;5.0665,2.9089,-2.4472;6.4787,1.8764,-2.1935;6.5728,3.6046,-1.7955)|",5.877659170799999
"[H]C([H])([H])OC(=O)C([H])([H])[C@@]1([H])OC(C([H])([H])[H])(C([H])([H])[H])OC1(C([H])([H])[H])C([H])([H])[H] |(0.1223,3.0631,2.6581;-0.0231,2.8604,1.5941;-0.7947,2.094,1.4809;-0.3047,3.77,1.0633;1.209,2.4285,0.9905;1.7559,1.3112,1.5184;1.2703,0.7059,2.4511;3.0096,0.913,0.7659;3.5607,1.8128,0.4769;3.6382,0.3124,1.4252;2.6861,0.1402,-0.5191;1.9942,0.7446,-1.127;3.9073,-0.0618,-1.2235;3.665,-1.1023,-2.1742;4.9169,-1.969,-2.267;5.7673,-1.3778,-2.6215;4.7473,-2.7939,-2.9659;5.1596,-2.3849,-1.2858;3.258,-0.5156,-3.5287;4.0859,0.0539,-3.9636;2.3972,0.1499,-3.4128;2.9806,-1.3185,-4.2185;2.5666,-1.8628,-1.6569;2.1179,-1.2991,-0.4041;0.5916,-1.3568,-0.3909;0.1968,-0.9472,0.5445;0.2586,-2.3964,-0.4751;0.1717,-0.7979,-1.234;2.7122,-2.1025,0.7595;2.3234,-1.7529,1.7215;3.8041,-2.0248,0.7728;2.4482,-3.1578,0.6376)|",6.72665438436
"[H]N([H])N([H])[C@]1([H])C([H])([H])C([H])([H])N(C(=O)OC([H])([H])[H])C([H])([H])[C@@]1([H])C([H])([H])C([H])([H])[H] |(1.3559,1.4541,3.9847;2.2418,0.9713,4.1573;2.8484,1.616,4.658;2.9004,0.6318,2.9296;2.6938,1.3306,2.217;2.4661,-0.6882,2.4527;1.3583,-0.7486,2.4026;2.9571,-1.7637,3.4324;4.0465,-1.6746,3.5273;2.5239,-1.5725,4.4184;2.596,-3.1699,2.9579;1.5021,-3.3052,2.9667;3.0321,-3.9371,3.5975;3.0925,-3.385,1.6008;3.816,-4.5096,1.322;4.1356,-5.3609,2.1371;4.1619,-4.5792,0.0027;4.9451,-5.727,-0.3414;5.1266,-5.6426,-1.4142;4.4039,-6.65,-0.1167;5.8917,-5.732,0.2062;2.6658,-2.4046,0.6099;3.1381,-2.6495,-0.3381;1.5741,-2.4853,0.468;3.0101,-0.9585,1.029;4.1056,-0.873,1.0755;2.485,0.0409,-0.025;1.3862,-0.0078,-0.0528;2.7374,1.0634,0.2832;3.0432,-0.163,-1.4407;2.7144,0.6443,-2.1046;2.7116,-1.1062,-1.8868;4.14,-0.1632,-1.4357)|",7.834157755895001
"[H]C(=O)/C(=C(\[H])C([H])([H])C([H])([H])SC([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(4.3532,2.0989,2.0658;4.3544,1.2615,1.3308;5.4093,0.7745,0.9647;3.001,0.8461,0.8893;1.9854,1.5296,1.4577;2.2569,2.3085,2.1747;0.5064,1.3607,1.267;0.1019,0.8199,2.139;0.2814,0.7377,0.3964;-0.2545,2.6948,1.1731;-0.0526,3.3075,2.0591;-1.3342,2.5085,1.1497;0.1943,3.7724,-0.2436;-0.468,2.795,-1.6387;-0.3316,3.4025,-2.5371;-1.5367,2.5962,-1.5094;0.0715,1.8531,-1.7729;2.8593,-0.2775,-0.1251;1.7885,-0.4224,-0.3129;3.5159,0.086,-1.4724;3.3403,-0.7121,-2.2035;4.5951,0.2154,-1.3582;3.099,1.0153,-1.8775;3.4076,-1.6107,0.4233;3.2319,-2.4155,-0.3004;2.9128,-1.8868,1.3617;4.4826,-1.5421,0.6079)|",4.76199238375
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"[H]C1=C([H])C([H])=C(/C([H])=C([H])/C2=C(\[H])C3=C([H])C([H])=C([H])C([H])=C3OC2([H])[H])C([H])=C1[H] |(-2.1326,-2.6208,0.7051;-1.0668,-2.4858,0.5429;-0.2371,-3.5912,0.3508;-0.653,-4.5952,0.3622;1.1285,-3.4114,0.1424;1.7687,-4.2782,-0.0066;1.7042,-2.1263,0.1203;3.146,-2.0059,-0.1003;3.6567,-2.958,-0.2347;3.8742,-0.8646,-0.1449;3.3723,0.0916,-0.0028;5.3032,-0.7669,-0.3478;5.9509,0.4245,-0.3133;5.3954,1.3458,-0.1489;7.385,0.51,-0.5024;8.1287,1.6849,-0.3088;7.6144,2.5789,0.0369;9.4985,1.7153,-0.5576;10.0608,2.6308,-0.3997;10.1432,0.5631,-1.0193;11.2111,0.5801,-1.2193;9.4267,-0.6175,-1.2219;9.9084,-1.5206,-1.5833;8.0591,-0.6442,-0.9554;7.3743,-1.7957,-1.2184;6.1439,-2.0164,-0.5148;5.6233,-2.7724,-1.1079;6.3673,-2.4609,0.47;0.8496,-1.0223,0.316;1.2541,-0.0146,0.3049;-0.5139,-1.2013,0.5238;-1.1516,-0.3337,0.6727)|",3.499384117430001
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"[H]O/C(OC([H])([H])C([H])([H])[H])=C(\C([H])([H])[H])P(=O)(OC([H])([H])C([H])([H])[H])OC([H])([H])C([H])([H])[H] |(7.5176,-2.3505,0.2432;6.6625,-2.0358,0.5825;6.2544,-1.0571,-0.265;7.2262,-0.7655,-1.154;6.9617,-0.7027,-2.5833;7.1021,-1.7175,-2.9785;5.9312,-0.3967,-2.7622;7.937,0.2819,-3.1947;7.8146,0.2917,-4.2837;8.9706,0.0009,-2.9665;7.738,1.2825,-2.804;5.0362,-0.4841,-0.0923;4.1127,-1.0081,0.9958;4.3624,-2.0427,1.2433;3.0634,-0.9735,0.6881;4.1935,-0.4119,1.9126;4.6001,1.0141,-0.9594;5.684,1.9585,-1.334;3.7592,0.6223,-2.3123;2.6282,-0.2681,-2.2698;2.9681,-1.2685,-1.9714;1.9117,0.0873,-1.5192;1.9992,-0.2976,-3.6517;1.1398,-0.9774,-3.6603;2.7224,-0.6421,-4.3979;1.6575,0.701,-3.9412;3.4625,1.6123,0.0333;3.0619,2.9993,-0.1103;3.1315,3.3005,-1.1609;2.0076,3.0197,0.1819;3.9019,3.9055,0.7746;3.5317,4.936,0.7126;4.9459,3.8858,0.4517;3.8471,3.5805,1.8188)|",6.318483608609999
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"[H]C1=C([H])/C([H])=C([H])\C(C([H])([H])C([H])([H])[H])=C(C(=O)OC([H])([H])C([H])([H])[H])/C([H])=C\1[H] |(7.1052,2.3131,-2.1274;6.168,1.7973,-2.341;6.1494,0.4564,-2.2416;7.0702,-0.0514,-1.9523;5.0332,-0.4155,-2.6307;5.3042,-1.247,-3.2844;3.7471,-0.3331,-2.2494;3.0673,-1.1027,-2.6187;3.166,0.5447,-1.205;2.4828,-0.2471,-0.103;1.6779,0.3312,0.3514;2.0416,-1.1443,-0.5578;3.4672,-0.6815,0.9992;2.9594,-1.3211,1.7299;3.8551,0.196,1.5233;4.3106,-1.2429,0.5821;3.2313,1.9,-1.271;2.6329,2.7507,-0.196;2.3426,2.3958,0.9333;2.446,4.0249,-0.6281;1.8886,4.9578,0.3246;1.4073,5.7188,-0.2952;1.1317,4.4446,0.9226;2.9699,5.5632,1.2081;2.5288,6.3151,1.8728;3.741,6.0494,0.6016;3.4381,4.7916,1.8254;3.7992,2.6477,-2.4269;3.1167,3.3512,-2.8994;5.0714,2.6335,-2.8486;5.3465,3.3301,-3.6428)|",4.313004530424999
"[H]C1=C([H])C2=C(C([H])=C1[H])[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])/C2=N\OC([H])([H])[H] |(4.5693,2.5749,4.1987;4.1235,2.0857,3.3367;4.0492,0.6998,3.2895;4.4295,0.0985,4.1081;3.4794,0.043,2.1824;2.9847,0.8088,1.105;3.0746,2.2052,1.1682;2.7005,2.7936,0.3327;3.6344,2.8464,2.2696;3.6989,3.9311,2.2936;2.3515,0.139,-0.1037;2.539,0.7793,-0.9764;0.8209,0.0204,0.0533;0.3733,-0.4316,-0.8403;0.3687,1.0073,0.1987;0.5488,-0.5938,0.9186;3.0276,-1.2194,-0.3574;2.5624,-1.7209,-1.215;4.0773,-1.0421,-0.6255;2.9687,-2.1345,0.8704;3.601,-3.0176,0.7315;1.9536,-2.5295,1.0155;3.3905,-1.436,2.1445;3.6643,-2.0673,3.2357;3.5354,-3.4524,3.0774;3.8746,-4.0865,4.3048;3.7481,-5.1571,4.1253;3.2092,-3.7625,5.1135;4.9135,-3.876,4.5853)|",4.884443616475
"[H]O[C@]([H])(C1=C([H])C([H])=C([H])O1)[C@]([H])(OC([H])([H])[H])C(=O)OC([H])([H])[H] |(3.615,2.4896,2.4793;3.7089,3.2392,1.8646;2.9296,2.9031,0.7316;3.2526,3.5708,-0.0749;1.4667,3.1034,1.0159;0.7999,3.6762,2.0557;1.2529,4.1081,2.9356;-0.5968,3.5997,1.7316;-1.4274,3.9534,2.3267;-0.6734,2.9892,0.5196;-1.4888,2.7099,-0.1299;0.579,2.6786,0.064;3.2346,1.4479,0.298;2.5828,1.179,-0.5468;2.9761,0.65,1.4269;2.9078,-0.7483,1.1823;2.634,-1.2141,2.1318;3.8725,-1.145,0.8485;2.1333,-0.9771,0.4352;4.6959,1.3254,-0.1539;5.5517,0.6767,0.4016;4.8974,2.0453,-1.2778;6.2441,2.0363,-1.7849;6.2232,2.6564,-2.6812;6.5533,1.0166,-2.0283;6.9324,2.4516,-1.0445)|",5.96473560296
"[H]O[C@]([H])(/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1[H])[C@]([H])(OC([H])([H])[H])C(=O)OC([H])([H])[H] |(0.3912,2.6008,-0.5974;0.7011,2.7177,0.3175;2.1061,2.4698,0.2931;2.406,2.302,1.3317;2.8747,3.6281,-0.2852;2.6411,3.8738,-1.3194;3.7841,4.3267,0.4098;3.9709,4.0312,1.4434;4.5815,5.4741,-0.0495;4.4976,6.0019,-1.3517;3.8173,5.557,-2.0722;5.2752,7.0911,-1.7326;5.1925,7.4826,-2.7433;6.1596,7.682,-0.8248;6.7655,8.5322,-1.1259;6.2568,7.1708,0.4695;6.94,7.6207,1.185;5.4761,6.0801,0.8497;5.5557,5.687,1.8608;2.3629,1.1601,-0.4935;3.4453,0.9703,-0.5405;1.8214,1.3636,-1.7822;2.2228,0.4158,-2.7594;1.7872,0.7402,-3.7074;1.8558,-0.5899,-2.5206;3.3181,0.3895,-2.8572;1.7399,-0.004,0.288;2.2065,-0.4049,1.3322;0.6256,-0.4924,-0.2836;-0.0324,-1.5361,0.458;-0.8914,-1.8263,-0.1472;-0.3556,-1.1606,1.432;0.6411,-2.3839,0.6076)|",5.064038757804999
"[H]C1=C([H])C([H])=C(/C([H])=C(\[H])C(=O)[C@]([H])(OC([H])([H])[H])C(=O)OC([H])([H])[H])C([H])=C1[H] |(5.2699,-3.3095,-8.7399;4.8206,-3.2272,-7.754;3.6446,-3.9183,-7.4592;3.1728,-4.5413,-8.214;3.0735,-3.8094,-6.1932;2.1574,-4.348,-5.9633;3.6629,-3.0089,-5.1967;3.0192,-2.9338,-3.8879;2.1112,-3.5247,-3.7691;3.4136,-2.223,-2.8104;4.2932,-1.5884,-2.8124;2.6364,-2.2819,-1.5585;1.6393,-2.9664,-1.3943;3.1766,-1.4173,-0.3913;2.3627,-1.3238,0.3406;3.5728,-0.177,-0.917;3.8591,0.8096,0.0648;4.0757,1.7318,-0.4791;4.7307,0.5351,0.6709;2.9928,0.9763,0.7228;4.3528,-2.1579,0.2697;5.5202,-1.8808,0.1091;3.9037,-3.1733,1.0287;4.9201,-3.9883,1.6403;4.3818,-4.7606,2.1896;5.5332,-3.3886,2.318;5.5621,-4.4347,0.8766;4.8504,-2.3185,-5.5116;5.3274,-1.6954,-4.7612;5.4205,-2.4275,-6.7749;6.3366,-1.8884,-7.0005)|",4.27762972986
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"[H]C([H])([H])C([H])([H])C([H])([H])C(=O)O[C@]1([H])C([H])([H])[C@]2(C([H])([H])[H])OC(C([H])([H])[H])(C([H])([H])[H])[C@@]1([H])C([H])([H])C2([H])[H] |(2.7676,3.335,-2.3021;3.0893,2.4538,-1.736;4.1637,2.5493,-1.5455;2.5826,2.4747,-0.7639;2.7614,1.1675,-2.5005;3.2488,1.1773,-3.4834;1.682,1.117,-2.6906;3.1699,-0.1221,-1.7532;2.8412,-0.9957,-2.3261;2.6976,-0.1499,-0.7664;4.6673,-0.2086,-1.5392;5.2482,0.0975,-0.5188;5.2949,-0.6445,-2.6601;6.7401,-0.752,-2.5864;6.9642,-1.134,-1.5876;7.4435,0.6057,-2.8045;7.7755,1.0274,-1.8502;6.7431,1.3173,-3.2546;8.6264,0.4098,-3.7769;9.3922,1.7061,-4.0127;9.8493,2.0636,-3.0835;8.7201,2.4824,-4.3928;10.1832,1.5482,-4.7531;8.1009,0.0321,-5.0646;7.2732,-1.1626,-5.0692;7.8952,-2.132,-6.0888;7.3233,-3.066,-6.1477;8.9335,-2.373,-5.8482;7.8878,-1.6653,-7.0792;5.8821,-0.7565,-5.5816;5.2146,-1.6246,-5.6376;5.9823,-0.3337,-6.5871;5.4127,-0.0103,-4.9406;7.2627,-1.754,-3.6303;6.657,-2.6668,-3.6071;8.7159,-2.0466,-3.1863;8.7081,-2.4393,-2.163;9.1581,-2.8277,-3.809;9.5382,-0.7286,-3.2723;10.3756,-0.8337,-3.9705;9.9635,-0.461,-2.2974)|",6.729375522865
"[H]C([H])([H])C(OC(=O)[C@]1([H])C([H])([H])C([H])([H])C([H])([H])[C@@]2([H])O[C@]21[H])(C([H])([H])[H])C([H])([H])[H] |(3.1295,2.1685,4.1806;3.7033,1.5411,3.4894;4.4637,1.0065,4.0692;3.0292,0.8139,3.0342;4.3667,2.4244,2.4276;5.2763,1.6089,1.5962;4.8273,0.5991,0.8258;3.6701,0.2357,0.7401;5.9805,-0.0249,0.0455;6.8241,-0.1344,0.739;6.4239,0.8969,-1.1192;6.5941,1.906,-0.7301;7.382,0.525,-1.4989;5.3839,0.9111,-2.2472;5.6747,1.6369,-3.0159;4.4205,1.2489,-1.8439;5.2039,-0.4783,-2.8863;6.0111,-0.6757,-3.6042;4.2659,-0.5075,-3.4541;5.203,-1.6103,-1.872;4.5527,-2.4566,-2.0996;6.4957,-2.0129,-1.3695;5.5728,-1.3977,-0.4622;5.16,-2.0718,0.2895;3.3419,3.1375,1.5395;3.8492,3.7065,0.7525;2.761,3.8424,2.145;2.6564,2.4261,1.0763;5.3213,3.4309,3.0743;4.7661,4.1071,3.7329;5.8281,4.0295,2.3105;6.0824,2.9154,3.6687)|",7.246391838815
"[H]OC1=C(O[H])C([H])=C(C([H])([H])[C@]([H])(C(=O)N(C([H])([H])[H])C([H])([H])[H])N([H])[H])C([H])=C1[H] |(10.1247,0.8229,4.0882;9.6408,0.1332,4.568;8.3056,0.2911,4.3194;7.4212,-0.6086,4.944;7.9709,-1.5649,5.7511;7.2533,-2.0936,6.1331;6.0497,-0.4891,4.7214;5.3777,-1.1824,5.2269;5.5147,0.5117,3.8935;4.0266,0.5943,3.6347;3.7117,1.6425,3.5734;3.4584,0.1557,4.4614;3.6125,-0.121,2.3247;4.1705,0.3423,1.5005;2.1002,0.0756,2.0772;1.3632,0.4101,3.0039;1.629,-0.1361,0.8081;0.2252,0.1281,0.526;0.1289,0.8846,-0.2644;-0.2488,0.4898,1.4373;-0.2799,-0.7869,0.1895;2.4433,-0.5602,-0.3233;2.7568,0.2925,-0.9438;1.8471,-1.2314,-0.9519;3.3129,-1.1168,0.0247;3.9124,-1.5606,2.2761;3.3715,-2.04,2.9964;4.8945,-1.7012,2.5119;6.4065,1.3999,3.2855;6.0293,2.1944,2.6467;7.7831,1.2881,3.4976;8.4652,1.9902,3.0203)|",5.62731442834
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"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C([H])C([H])=C(C#N)C([H])=C1[H])C([H])([H])C([H])([H])[H] |(4.6811,-0.0994,-3.632;4.1741,-0.709,-4.1955;2.923,-0.888,-3.6948;2.1703,-1.6723,-4.2165;2.5597,-0.0649,-2.4617;2.8187,0.9898,-2.6277;1.4736,-0.1264,-2.3606;3.219,-0.5697,-1.1477;2.9788,-1.6372,-1.056;4.7374,-0.4469,-1.1722;5.5493,-1.5879,-1.0953;5.0858,-2.5679,-1.0221;6.9376,-1.4921,-1.1121;7.5509,-2.3852,-1.0511;7.551,-0.233,-1.2094;8.9806,-0.1237,-1.2248;10.1404,-0.0342,-1.2352;6.7535,0.9207,-1.289;7.2248,1.8956,-1.3608;5.366,0.8078,-1.2696;4.766,1.713,-1.3189;2.5899,0.1507,0.0691;2.7953,1.2279,0.0042;1.5001,0.0426,-0.0044;3.0615,-0.3871,1.4241;2.5681,0.1463,2.2434;4.1428,-0.268,1.5522;2.8269,-1.453,1.5295)|",5.534795719169999
"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C([H])C([H])=C(N(=O)=O)C([H])=C1[H])C([H])([H])C([H])([H])[H] |(2.115,3.4237,0.9033;1.2272,3.0636,1.0631;1.3067,1.9186,1.8007;0.3,1.3468,2.1325;2.7177,1.435,2.1116;3.3035,2.2597,2.5399;2.6296,0.6607,2.8774;3.4491,0.8763,0.8558;3.4655,1.6708,0.0953;4.8974,0.5552,1.1922;5.2276,-0.4367,2.1304;4.4416,-1.0027,2.6222;6.5531,-0.7171,2.4459;6.8181,-1.4803,3.1671;7.5615,0.0078,1.8125;8.9657,-0.2814,2.141;9.1884,-1.1643,2.9691;9.8336,0.3772,1.5685;7.2743,0.9966,0.8761;8.0837,1.5377,0.4016;5.9407,1.2608,0.5746;5.7083,2.0286,-0.159;2.6895,-0.327,0.2494;2.6659,-1.144,0.9829;1.6443,-0.036,0.0932;3.283,-0.8278,-1.0704;2.6919,-1.6592,-1.4689;4.3126,-1.1804,-0.9462;3.2907,-0.0348,-1.8284)|",4.889885893485
"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C(Cl)C([H])=C(Cl)C([H])=C1[H])C([H])([H])[H] |(4.8432,0.6828,-1.0055;4.4293,0.8552,-1.8693;3.24,1.4986,-1.6998;2.5848,1.8252,-2.6572;2.8256,1.7069,-0.2493;3.6626,2.1483,0.3041;1.998,2.4206,-0.2319;2.3862,0.3675,0.4203;3.127,-0.3951,0.1528;2.4269,0.5037,1.9353;3.6314,0.4075,2.6522;5.1597,0.0954,1.8107;3.7047,0.5454,4.0364;4.6545,0.4571,4.5499;2.5307,0.7963,4.7426;2.6015,0.9719,6.4863;1.3107,0.9064,4.0811;0.4015,1.101,4.6392;1.2767,0.7585,2.6958;0.3196,0.8392,2.192;1.0357,-0.0993,-0.1413;1.1147,-0.2556,-1.2213;0.2471,0.6447,0.0163;0.7192,-1.0406,0.3198)|",5.97562015698
"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C([H])C([H])=C(C#N)C([H])=C1[H])C([H])([H])[H] |(0.2401,-2.3368,-1.3893;-0.2308,-2.3889,-0.54;0.5398,-1.8896,0.4645;0.1007,-1.8263,1.5852;1.941,-1.4261,0.0764;2.4632,-1.2169,1.0131;2.4709,-2.2448,-0.4279;1.9836,-0.1492,-0.8208;3.044,0.1338,-0.8533;1.5837,-0.4386,-2.2656;2.5308,-1.0061,-3.1361;3.5345,-1.2106,-2.7705;2.2204,-1.2986,-4.4588;2.9672,-1.7283,-5.1185;0.9322,-1.0255,-4.95;0.6041,-1.3167,-6.3152;0.3409,-1.5534,-7.4232;-0.0269,-0.4628,-4.0946;-1.0225,-0.2495,-4.4701;0.3003,-0.1751,-2.7706;-0.457,0.2641,-2.1299;1.218,1.0136,-0.1717;1.644,1.2389,0.8113;0.1597,0.7803,-0.0146;1.2818,1.9169,-0.787)|",5.542959134685001
"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C([H])C(F)=C([H])C([H])=C1[H])C([H])([H])C([H])([H])C([H])([H])[H] |(5.8822,2.4797,-1.0497;5.5214,1.7479,-1.5775;5.0732,0.7569,-0.7576;4.5874,-0.2389,-1.2321;5.2786,1.0079,0.734;6.3607,0.9665,0.9237;4.9659,2.0338,0.9762;4.546,-0.0092,1.635;4.7526,-0.9999,1.2135;5.1129,0.0271,3.0479;4.9817,1.1699,3.8508;4.4588,2.0556,3.5024;5.5246,1.1767,5.1286;5.3873,2.2884,5.8845;6.2035,0.0846,5.657;6.6104,0.1357,6.6614;6.3328,-1.0515,4.859;6.8565,-1.9213,5.2457;5.7951,-1.0801,3.5701;5.9039,-1.9738,2.9613;3.0163,0.2051,1.5907;2.7714,1.1835,2.0306;2.7076,0.2469,0.5394;2.2102,-0.8882,2.3027;2.4446,-1.8599,1.8461;2.5256,-0.9565,3.3518;0.6989,-0.6452,2.2365;0.145,-1.444,2.7424;0.3484,-0.6021,1.1981;0.4278,0.303,2.7174)|",6.27494539253
"[H]OC(=O)C([H])([H])[C@@]([H])(C1=C([H])C([H])=C(N(=O)=O)C([H])=C1[H])C([H])([H])C([H])([H])C([H])([H])[H] |(5.6466,3.2478,-1.3209;5.2958,2.3506,-1.4485;4.9991,1.7854,-0.2471;4.5483,0.6686,-0.2029;5.3178,2.648,0.9726;6.4123,2.7336,1.0317;4.9399,3.6669,0.8066;4.7584,2.0666,2.2895;4.9959,0.9966,2.2796;5.4595,2.6942,3.485;5.3117,4.0588,3.7877;4.6701,4.6839,3.1727;5.9706,4.6348,4.8687;5.861,5.6852,5.1091;6.7896,3.8307,5.6598;7.4905,4.4308,6.8041;7.3179,5.6316,7.0147;8.2092,3.6979,7.4835;6.96,2.4745,5.3941;7.6032,1.8809,6.0321;6.292,1.9187,4.3059;6.4171,0.861,4.0903;3.2196,2.1978,2.3534;2.9417,3.262,2.3806;2.8075,1.7892,1.4234;2.579,1.4709,3.5426;2.8599,0.4093,3.5053;2.9866,1.8647,4.4829;1.052,1.5944,3.5562;0.6185,1.0618,4.4099;0.6116,1.1762,2.6431;0.7389,2.6436,3.6252)|",5.020500541724999
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"[H]O[C@]([H])(C([H])([H])OC(=O)C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(2.4694,2.8855,0.6467;2.817,2.2657,-0.0195;3.4186,1.1732,0.6621;3.2917,0.3041,0.0001;2.7002,0.8796,1.9842;2.8708,1.6835,2.7078;3.0335,-0.0676,2.4104;1.2764,0.7121,1.8029;0.5161,1.8243,1.8663;0.9765,2.9459,2.0218;-0.9377,1.5354,1.7452;-1.4336,0.2327,1.5921;-0.7432,-0.6022,1.5563;-2.8067,0.0217,1.484;-3.19,-0.9878,1.3643;-3.6884,1.1046,1.5287;-4.7587,0.9361,1.4445;-3.1964,2.4037,1.6816;-3.8816,3.246,1.7154;-1.8253,2.6196,1.7892;-1.4206,3.6193,1.9072;4.9349,1.4063,0.8751;5.0324,2.2899,1.5255;5.6307,0.2188,1.5619;6.7142,0.3792,1.5967;5.4559,-0.7135,1.0085;5.2933,0.0663,2.5931;5.6084,1.7273,-0.4666;6.6591,2.0016,-0.3172;5.0975,2.5516,-0.9693;5.5822,0.8549,-1.1328)|",5.559285965715
"[H]O[C@@]([H])(C([H])([H])OC(=O)C([H])([H])[H])C([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.3004,-0.1196,2.5553;3.5399,0.0127,3.1448;2.4991,-0.8603,2.72;1.6425,-0.6004,3.3482;2.1158,-0.6166,1.2579;1.2312,-1.2,0.9918;2.9435,-0.8905,0.5933;1.8839,0.7776,0.9823;0.6725,1.2716,1.3353;-0.208,0.6069,1.8362;0.5886,2.7463,1.0201;0.8339,2.9271,-0.0311;1.3181,3.2943,1.626;-0.4167,3.1077,1.2389;2.8857,-2.3361,2.9693;3.7452,-2.586,2.3303;3.2411,-2.388,4.0053;1.7725,-3.3382,2.7438;1.7807,-4.1951,1.6353;2.6108,-4.1518,0.933;0.7464,-5.1089,1.4247;0.7746,-5.7659,0.5592;-0.3172,-5.1804,2.3244;-1.1236,-5.8907,2.1635;-0.3371,-4.3342,3.4354;-1.1594,-4.3851,4.1442;0.6986,-3.424,3.6422;0.6757,-2.7731,4.5134)|",6.43277142582
"[H]OC([H])([H])C1=C([H])C(=O)C([H])=C(C([H])([H])[H])[C@@]12C([H])([H])C([H])([H])[C@@]([H])(C(=C([H])[H])C([H])([H])[H])C2([H])[H] |(3.6383,4.0198,2.5074;4.1247,3.1941,2.3501;5.0861,3.4631,1.3224;5.8464,4.1674,1.6883;5.5838,2.5065,1.1459;4.4696,4.0249,0.0503;4.5187,5.3577,-0.1315;5.0097,6.0085,0.59;3.9408,6.034,-1.306;3.9952,7.255,-1.444;3.2971,5.1553,-2.29;2.8555,5.6567,-3.1478;3.2309,3.8157,-2.1579;2.5107,3.0067,-3.2105;2.1597,3.6543,-4.0184;1.6377,2.49,-2.7933;3.1494,2.2336,-3.6538;3.8435,3.0786,-0.9684;4.9592,2.0607,-1.47;4.9399,2.0048,-2.5619;5.9625,2.4069,-1.2031;4.6165,0.6901,-0.862;5.0637,0.5907,0.1339;4.9949,-0.1409,-1.4678;3.0782,0.6894,-0.751;2.6697,0.5498,-1.7599;2.4693,-0.3781,0.1405;1.4596,-1.136,-0.3023;0.9927,-1.8917,0.3246;1.0616,-1.0305,-1.3091;3.0274,-0.5523,1.5352;2.4025,-1.2301,2.1244;3.1024,0.399,2.0761;4.0398,-0.9772,1.5086;2.7686,2.1362,-0.2915;2.8644,2.2114,0.7925;1.7478,2.4403,-0.5413)|",4.7293387216900005
"[H]OC([H])([H])C(=C([H])[H])[C@]1([H])C([H])([H])C(=O)C2=C([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])[H])[C@]2(C([H])([H])[H])C1([H])[H] |(0.0556,0.1333,-0.0038;0.5634,-0.3873,-0.6463;1.6335,-1.0051,0.0762;1.2456,-1.6699,0.8625;2.1416,-1.6326,-0.6657;2.606,-0.0081,0.671;2.7803,0.0372,1.9966;2.2312,-0.6282,2.659;3.4746,0.7189,2.48;3.3139,0.9105,-0.3211;3.3197,0.3811,-1.2826;4.7715,1.2164,0.0595;5.3936,0.3165,0.0825;4.7998,1.6446,1.0732;5.4599,2.2538,-0.8225;6.6687,2.2054,-0.9976;4.6204,3.3794,-1.3522;5.234,4.5631,-1.5232;6.2977,4.6166,-1.2984;4.5505,5.8231,-1.9603;4.5411,6.5249,-1.1104;5.1551,6.3207,-2.7319;3.1237,5.5763,-2.4647;3.1552,5.2525,-3.5129;2.5566,6.5155,-2.4539;2.382,4.5205,-1.6233;2.4032,4.8685,-0.5784;0.9094,4.442,-2.0553;0.4205,5.4101,-1.8951;0.3455,3.693,-1.4903;0.8123,4.2006,-3.12;3.1323,3.1461,-1.6313;2.9984,2.4409,-3.0067;1.9743,2.0906,-3.1761;3.6612,1.5725,-3.079;3.2744,3.1125,-3.8254;2.5402,2.2333,-0.523;2.5324,2.7795,0.4314;1.5022,1.9922,-0.7676)|",5.259960730165
"[H]C([H])([H])C(=O)OC([H])([H])[C@@]1([H])O[C@]1([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(12.2355,-2.1565,-3.7418;12.1673,-2.0445,-4.8289;13.1528,-1.7283,-5.1858;11.8928,-2.9966,-5.284;11.1289,-1.0038,-5.1744;10.1321,-1.1951,-5.8373;11.4584,0.1964,-4.6385;10.5485,1.2872,-4.906;11.1423,2.1895,-4.7468;10.2173,1.2298,-5.9472;9.3624,1.2696,-3.9781;8.7608,0.3634,-4.0184;9.5837,1.776,-2.65;8.7343,2.5255,-3.5334;9.1969,3.4539,-3.8818;7.2723,2.6742,-3.1592;7.2132,3.4328,-2.3651;6.7446,3.1013,-4.0245;6.5695,1.3948,-2.6899;6.518,0.6752,-3.5184;7.1754,0.9258,-1.9043;5.1516,1.654,-2.1643;5.1991,2.3616,-1.3234;4.5561,2.1499,-2.9455;4.4305,0.3773,-1.7132;4.3701,-0.3232,-2.5593;5.0346,-0.1275,-0.9448;3.0206,0.6302,-1.1643;3.0809,1.3196,-0.3089;2.4194,1.1467,-1.9277;2.2936,-0.6497,-0.7314;2.237,-1.3389,-1.586;2.8921,-1.1643,0.0338;0.8844,-0.3912,-0.1886;0.3916,-1.3244,0.1073;0.2521,0.0939,-0.9425;0.9119,0.2645,0.6905)|",7.436871534165
"[H]N1N=C([C@@]([H])(N([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H])S/C1=N/C([H])(C([H])([H])[H])C([H])([H])[H] |(3.1905,4.5529,-1.1373;2.5855,3.772,-1.3458;3.1353,2.5247,-1.1959;2.3837,1.6188,-1.72;2.7528,0.1416,-1.6858;2.0228,-0.3984,-2.3028;4.0795,-0.1128,-2.2642;4.7568,0.4869,-1.7927;4.0859,0.1764,-3.2417;2.6786,-0.4355,-0.2466;3.4463,0.091,0.34;1.3153,-0.1721,0.4096;1.2926,-0.5974,1.4192;0.504,-0.6407,-0.1629;1.0936,0.8965,0.4946;3.0025,-1.9358,-0.2439;3.0148,-2.322,0.782;3.9714,-2.134,-0.7074;2.2413,-2.4984,-0.8008;0.9229,2.2331,-2.5209;1.4367,3.9147,-2.127;0.8076,4.947,-2.5103;1.2878,6.2778,-2.1306;2.3939,6.3055,-2.0829;0.8525,7.274,-3.2093;1.1978,8.2867,-2.9701;-0.2399,7.2856,-3.2899;1.2592,6.9888,-4.1849;0.7381,6.6668,-0.7485;1.071,7.6722,-0.4652;1.069,5.9649,0.0253;-0.3571,6.6566,-0.766)|",5.36064285485
"[H]C([H])=C([H])C([H])([H])N1C([H])([H])[C@@]([H])(OC([H])([H])[H])[C@@]([H])(Cl)[C@@]1([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H] |(2.3839,2.4914,-3.716;2.3965,1.6153,-3.0732;2.065,0.6804,-3.521;2.796,1.6813,-1.8031;3.1149,2.6399,-1.397;2.7962,0.5092,-0.8572;2.4167,-0.3663,-1.3958;2.0728,0.7092,-0.0372;4.1184,0.149,-0.3272;4.1106,-1.198,0.2913;3.0814,-1.5227,0.512;4.5521,-1.9459,-0.3796;4.898,-1.0975,1.6133;4.4845,-1.7644,2.384;6.2826,-1.3428,1.4687;6.6255,-2.7141,1.4842;7.715,-2.7672,1.4207;6.1951,-3.2635,0.6331;6.2961,-3.1996,2.4162;4.6459,0.3741,1.9584;3.6144,0.4513,2.3128;5.65,1.006,3.3239;4.7491,1.1205,0.6069;4.1352,2.0272,0.6604;6.1789,1.4922,0.1723;6.6728,2.03,0.992;6.7346,0.5603,0.0325;6.2581,2.3447,-1.1088;5.5738,1.9062,-1.8477;5.8454,3.8044,-0.8611;5.8554,4.383,-1.793;6.539,4.2895,-0.1616;4.8394,3.8927,-0.4331;7.6765,2.2901,-1.6963;7.7539,2.8934,-2.6093;7.9624,1.262,-1.9476;8.4152,2.6758,-0.9809)|",6.065417727645
"[H]C([H])=C([H])C([H])([H])N1C([H])([H])[C@@]([H])(OC([H])([H])[H])[C@@]1([H])[C@]([H])(Cl)C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H] |(1.9855,4.269,0.5599;2.1443,3.3191,1.0629;2.0086,3.3127,2.1427;2.4827,2.2162,0.3944;2.6112,2.2511,-0.6872;2.6581,0.8615,1.022;1.8642,0.2025,0.6401;2.5046,0.9376,2.1169;3.9179,0.1905,0.6979;3.9739,-1.2289,1.1275;3.2343,-1.4797,1.9041;3.8898,-1.95,0.3055;5.3935,-1.0075,1.6991;5.5436,-1.3231,2.7398;6.4402,-1.4816,0.8861;6.7045,-2.8593,1.0618;6.9818,-3.0853,2.1033;7.5433,-3.1082,0.4073;5.8412,-3.4847,0.7866;5.1372,0.5119,1.4978;4.8908,1.0097,2.4485;6.2493,1.29,0.7869;6.684,0.6552,0.0166;7.6024,1.4869,2.0297;5.8407,2.6459,0.2263;5.0255,2.4454,-0.4814;5.4112,3.2562,1.0303;6.9395,3.4489,-0.4988;7.7464,3.6404,0.2203;7.5369,2.6905,-1.6936;8.2801,3.3088,-2.2106;6.759,2.4275,-2.4231;8.0382,1.766,-1.3877;6.3766,4.8068,-0.9459;7.1543,5.4184,-1.4184;5.9744,5.3727,-0.0971;5.5661,4.6794,-1.6758)|",6.38379093273
"[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])/C([H])=C([H])/C([H])=C(\[H])C(=O)C([H])([H])[H] |(4.6097,2.1932,2.6586;5.5678,2.1281,2.4983;6.2413,2.9271,3.3726;7.446,2.9644,3.3546;5.383,3.6937,4.3727;5.9113,4.6228,4.6025;4.4151,3.9608,3.9301;5.1671,2.8982,5.6786;6.1402,2.7288,6.1532;4.5833,3.5251,6.3643;4.459,1.5361,5.5017;5.0959,0.8466,4.9349;4.3364,1.0945,6.502;3.1104,1.639,4.8496;2.4088,2.3457,5.2983;2.7054,0.931,3.7742;3.3879,0.2075,3.3248;1.3863,1.0513,3.1865;0.6923,1.7623,3.6347;0.9471,0.3486,2.1222;1.5917,-0.3788,1.6309;-0.4261,0.5415,1.5867;-1.2061,1.3434,2.0821;-0.8101,-0.3067,0.3873;-0.691,-1.3738,0.6144;-0.1505,-0.0864,-0.4623;-1.845,-0.1039,0.1063)|",4.653146843550001
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"[H]C([H])=C1C([H])([H])C([H])([H])[C@]2([H])[C@]1(C(=O)OC([H])([H])[H])C(=O)C([H])([H])C([H])([H])[C@@]2([H])C([H])([H])[H] |(5.2074,4.3679,2.8814;5.1513,3.2978,2.6931;5.7988,2.657,3.2807;4.2972,2.8025,1.7983;3.3194,3.6035,0.938;2.5701,4.0817,1.5831;3.8213,4.4083,0.3897;2.641,2.5796,-0.0041;3.197,2.4831,-0.9397;1.6096,2.8576,-0.2427;2.7218,1.2692,0.7971;2.0235,1.3831,1.6455;4.14,1.3199,1.4492;5.2715,1.0044,0.4564;5.2399,1.1477,-0.7473;6.3723,0.5845,1.1191;7.5421,0.3648,0.3109;8.3017,-0.0128,0.9954;7.8677,1.3032,-0.1456;7.3342,-0.3624,-0.478;4.1444,0.2787,2.5846;4.3541,0.5368,3.7498;3.7804,-1.1232,2.0953;4.6183,-1.4851,1.4834;3.7078,-1.7774,2.9692;2.4781,-1.1626,1.2597;1.6233,-1.0235,1.9365;2.3654,-2.1626,0.8223;2.4027,-0.0868,0.1526;3.1627,-0.3008,-0.6096;1.0271,-0.1024,-0.5266;0.8019,-1.0966,-0.9301;0.9826,0.6117,-1.3555;0.2307,0.1558,0.1839)|",5.937524217910001
"[H]C([H])=C([H])C([H])([H])C([H])([H])[C@]1([H])[C@]([H])(C(=O)OC([H])([H])[H])C(=O)C([H])([H])C([H])([H])[C@@]1([H])C([H])([H])[H] |(7.573,-2.436,-0.0701;6.528,-2.1439,-0.0071;5.8599,-2.5674,-0.755;6.0831,-1.3179,0.9409;6.7955,-0.9307,1.671;4.6534,-0.868,1.0889;4.2955,-1.1007,2.1004;4.0239,-1.4446,0.3994;4.4257,0.6346,0.7923;3.3524,0.8522,0.8925;4.6793,0.8054,-0.2593;5.2242,1.6366,1.6532;6.2948,1.4448,1.4824;5.0054,1.3906,3.1859;5.3939,0.4095,3.4636;3.5346,1.4378,3.58;2.8041,2.4055,3.4928;3.1201,0.2485,4.0656;1.7445,0.1994,4.4857;1.5861,-0.8181,4.8432;1.0796,0.4229,3.6476;1.5654,0.9218,5.286;5.8002,2.412,4.0263;6.5488,2.0371,4.9065;5.613,3.8608,3.6411;4.5806,4.1435,3.8835;6.295,4.4727,4.2384;5.8376,4.0525,2.128;6.898,3.8784,1.8928;5.6294,5.0964,1.8643;4.9686,3.1173,1.2645;3.9153,3.3446,1.4727;5.2411,3.3941,-0.2232;5.1595,4.4677,-0.4292;4.5314,2.8823,-0.8798;6.2531,3.0778,-0.5082)|",5.80146729266
"[H]OC(=O)[C@@]([H])(O[H])C([H])([H])C([H])([H])C1=C([H])C([H])=C2OC([H])([H])OC2=C1[H] |(2.8477,-2.3015,-3.0765;2.27,-1.6287,-3.5037;1.2028,-1.5178,-2.6944;0.2725,-0.7806,-2.9228;1.2675,-2.4276,-1.4489;0.5246,-3.2186,-1.6077;2.537,-3.0947,-1.3917;3.1113,-2.5903,-0.789;0.8908,-1.6622,-0.1726;-0.1554,-1.3584,-0.2804;0.9413,-2.3619,0.6706;1.7295,-0.399,0.1258;1.3043,0.0742,1.0198;1.5864,0.3136,-0.6938;3.2164,-0.6281,0.3495;3.6729,-1.2377,1.5269;2.9506,-1.5408,2.2806;5.0392,-1.458,1.7785;5.3819,-1.9261,2.6949;5.9281,-1.0427,0.8072;7.2973,-1.1362,0.8033;7.7318,-0.5472,-0.43;8.2808,-1.2929,-1.0164;8.3627,0.3239,-0.2176;6.5702,-0.1278,-1.1586;5.492,-0.4379,-0.3692;4.1533,-0.2198,-0.6308;3.8335,0.2526,-1.5543)|",5.510305472624999
"[H]C([H])([H])/C1=C(\C([H])([H])[H])C([H])([H])[C@@]2(Cl)C(=O)C([H])([H])C([H])([H])C([H])([H])[C@]2([H])C1([H])[H] |(-0.0475,-0.2267,2.1065;0.271,0.7692,1.7669;-0.6166,1.2438,1.3257;0.5477,1.3498,2.6486;1.371,0.6624,0.7384;2.6249,1.1258,0.8929;3.1637,1.7869,2.1389;4.0949,1.2985,2.4582;2.4733,1.7616,2.9838;3.4178,2.838,1.9423;3.6715,1.0166,-0.2005;4.4104,0.2463,0.0663;4.2493,1.9455,-0.2653;3.1297,0.6652,-1.5807;2.3757,2.1997,-2.3295;4.2469,0.265,-2.5721;5.4171,0.3474,-2.2574;3.7883,-0.2844,-3.9122;3.343,0.536,-4.489;4.6729,-0.636,-4.4503;2.7414,-1.4033,-3.7265;2.3752,-1.7259,-4.7081;3.2255,-2.2781,-3.2704;1.5744,-0.9441,-2.8419;0.873,-1.7736,-2.6866;1.0149,-0.146,-3.3462;2.0611,-0.4388,-1.4759;2.5941,-1.2701,-0.9837;0.9218,-0.01,-0.5448;0.3232,-0.899,-0.2976;0.2385,0.6625,-1.0855)|",5.07764445033
"[H]OC(=O)[C@]1([H])[C@]([H])(C2([H])C([H])([H])C2([H])[H])[C@@]1([H])[C@@]([H])(C(=O)O[H])N([H])[H] |(1.6446,-0.2589,-1.8955;1.92,-0.8663,-2.6067;0.8253,-1.3703,-3.2061;0.9213,-2.1804,-4.1056;-0.5227,-0.8983,-2.7435;-1.0144,-0.2975,-3.5078;-0.96,-0.6613,-1.3123;-1.7108,0.1186,-1.1949;0.0085,-0.7623,-0.1676;0.6322,-1.655,-0.1778;-0.3773,-0.249,1.1939;-1.3587,0.2053,1.3006;-0.0495,-0.81,2.0641;0.6613,0.4965,0.3922;1.6964,0.4462,0.7204;0.3736,1.4535,-0.037;-1.41,-1.9055,-2.0394;-0.9012,-2.8175,-1.7214;-2.8188,-2.1275,-2.5447;-3.1599,-1.1963,-3.0236;-2.8783,-3.2056,-3.6671;-3.8751,-3.8753,-3.8168;-1.8318,-3.3488,-4.5006;-1.0307,-2.8343,-4.263;-3.745,-2.4089,-1.4503;-3.4079,-3.2266,-0.9398;-4.6225,-2.7165,-1.8722)|",6.182426683359999
"[H]C1=C(C([H])([H])[H])[C@]([H])(/C([H])=C(\[H])[C@]([H])(N(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])C1=O |(2.3978,2.0558,-0.8433;3.0793,1.2525,-0.5717;2.6477,-0.0196,-0.4571;1.2117,-0.3963,-0.6937;0.5848,0.4806,-0.8786;0.8063,-0.9434,0.1664;1.1291,-1.0677,-1.56;3.5873,-1.1495,-0.0427;3.2716,-2.061,-0.5706;3.3848,-1.3997,1.4411;3.6998,-0.5974,2.1102;2.8133,-2.4896,1.9611;2.4864,-3.3,1.3065;2.5161,-2.704,3.4288;3.0139,-1.898,4.008;3.0161,-4.0261,3.8506;4.4649,-4.1271,3.6986;4.7859,-5.1445,3.9457;5.0198,-3.4253,4.3533;4.7536,-3.9208,2.6649;2.6342,-4.3916,5.211;3.0801,-5.3617,5.4536;1.5506,-4.4984,5.2998;2.9762,-3.6639,5.9742;0.997,-2.5698,3.6472;0.6502,-1.6222,3.2242;0.7284,-2.5766,4.7078;0.4637,-3.3878,3.1491;5.0717,-0.8601,-0.4462;5.2064,-0.9505,-1.9815;6.2356,-0.7306,-2.2884;4.5464,-0.2477,-2.4992;4.9652,-1.9614,-2.3324;6.0172,-1.899,0.1806;7.0494,-1.7175,-0.1421;5.7449,-2.916,-0.1285;5.9946,-1.8657,1.2746;5.4627,0.5556,0.0326;5.5222,0.5757,1.1311;6.457,0.8374,-0.3312;4.4846,1.6509,-0.3797;4.8469,2.8116,-0.5209)|",4.598724073450001
"[H]OC(=O)[C@](O[H])(S[H])C([H])([H])C1=C([H])C([H])=C(O[H])C([H])=C1[H] |(-3.7447,3.5766,-0.7424;-3.2954,3.7282,-1.6025;-1.9898,3.5413,-1.3781;-1.1469,3.6418,-2.2406;-1.6447,3.2037,0.0943;-2.8387,2.9063,0.8102;-2.9258,1.9337,0.8279;-1.032,4.7708,0.8897;-0.1589,5.0685,-0.0957;-0.607,2.064,0.1823;0.2262,2.3316,-0.4731;-0.2315,2.0379,1.2097;-1.187,0.7162,-0.1993;-1.6696,-0.1592,0.7891;-1.5718,0.1128,1.8387;-2.2405,-1.3873,0.461;-2.6027,-2.0644,1.2279;-2.3354,-1.7674,-0.8811;-2.8974,-2.9806,-1.1534;-2.8969,-3.1244,-2.1127;-1.8557,-0.9117,-1.8807;-1.923,-1.2061,-2.9266;-1.2912,0.3147,-1.5401;-0.928,0.9716,-2.3251)|",5.5728916582400005
"[H]OC(=O)/C([H])=C([H])/C(=N\C([H])([H])[C@@]([H])(C(=O)O[H])N([H])[H])O[H] |(1.5384,2.225,0.7244;1.6526,1.8928,1.6305;2.5839,0.9102,1.6773;2.8309,0.3439,2.715;3.2622,0.5912,0.3802;3.0777,1.2242,-0.4854;4.0918,-0.4573,0.3092;4.2514,-1.0373,1.219;4.8508,-0.872,-0.898;4.4327,-1.0636,-2.0838;3.0532,-0.8732,-2.4964;2.7151,-1.8101,-2.9603;2.3631,-0.641,-1.679;2.9766,0.2627,-3.5412;3.5117,1.128,-3.1202;3.712,-0.0868,-4.8616;3.2507,0.2206,-5.9382;4.8962,-0.7059,-4.747;5.0692,-0.93,-3.7958;1.5869,0.6576,-3.7382;1.0755,-0.1408,-4.1189;1.57,1.3389,-4.4979;6.1687,-1.1211,-0.6744;6.4074,-0.8517,0.2274)|",4.100755727035001
"[H]OC(=O)[C@@]([H])(O[H])C([H])([H])[C@@]([H])(O[H])[C@@]([H])(O[H])[C@@]([H])(O[H])C([H])([H])C([H])([H])O[H] |(5.4629,-2.5642,-2.3184;6.011,-2.1704,-3.0426;6.8594,-1.2777,-2.5265;7.613,-0.6257,-3.2222;6.8652,-1.1095,-0.9979;6.9356,-2.1133,-0.5562;8.0047,-0.3667,-0.6278;8.3635,-0.0282,-1.4727;5.5972,-0.4109,-0.443;5.7045,-0.4094,0.6457;5.6359,0.6388,-0.7541;4.2501,-1.027,-0.8617;3.9767,-0.7047,-1.8687;4.3448,-2.4668,-0.9529;4.2956,-2.7902,-0.0333;3.0592,-0.7504,0.0817;2.187,-1.202,-0.3963;3.2673,-1.4692,1.3029;3.7349,-0.8486,1.8908;2.7106,0.7109,0.411;1.7409,0.6795,0.9334;3.7217,1.1541,1.3313;3.4098,1.963,1.7644;2.599,1.678,-0.7787;2.4748,2.6911,-0.3705;3.5345,1.6848,-1.348;1.431,1.4197,-1.7265;1.356,2.2556,-2.4385;0.4866,1.3857,-1.1576;1.6618,0.1913,-2.4089;0.9266,0.0336,-3.0202)|",7.325304855460001
"[H]OC(=O)[C@@]1([H])OC(=O)[C@]([H])(O[H])[C@]([H])(O[H])[C@@]1([H])O[H] |(0.1152,-3.2402,-0.2313;0.9378,-3.4578,0.2505;1.6705,-2.3454,0.3707;2.6987,-2.3086,1.0086;1.1487,-1.1113,-0.3724;1.6154,-1.0747,-1.3638;-0.2707,-1.3308,-0.5941;-1.1607,-0.3105,-0.6863;-2.2381,-0.4971,-1.1992;-0.8001,1.03,-0.0481;-1.0621,0.9555,1.0187;-1.5258,2.0568,-0.6959;-2.3428,1.6402,-1.0287;0.6886,1.3402,-0.1821;0.9222,2.2522,0.384;1.0204,1.4759,-1.5535;0.3255,2.0454,-1.9303;1.5006,0.1781,0.3836;2.5676,0.3658,0.209;1.2058,0.0921,1.7651;1.8502,-0.528,2.1477)|",7.099450359544999
"[H]OC(=O)[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[C@]1([H])C([H])=C([H])[C@@]([H])(C([H])([H])[H])C([H])=C1[H] |(7.4726,0.9404,5.2778;6.6798,0.4112,5.4907;6.5267,-0.4574,4.4582;7.2868,-0.4735,3.5131;5.3269,-1.3731,4.6353;4.8428,-1.1142,5.5837;5.8158,-2.8319,4.6924;4.9634,-3.5114,4.8008;6.4906,-2.9967,5.5397;6.3518,-3.0896,3.7738;4.3302,-1.1623,3.4766;4.838,-1.3947,2.5332;3.5193,-1.8936,3.5873;3.7385,0.2507,3.4113;4.5424,0.9826,3.2509;3.2792,0.5045,4.3774;2.6739,0.4463,2.2954;2.3739,1.5071,2.3655;1.4446,-0.3971,2.5326;0.9702,-0.2912,3.5086;0.9292,-1.2425,1.6367;0.035,-1.8135,1.8869;1.4895,-1.4547,0.2518;1.7587,-2.5208,0.1488;0.4235,-1.1681,-0.8309;-0.4605,-1.8014,-0.6893;0.8213,-1.3648,-1.8336;0.1042,-0.1211,-0.7892;2.7328,-0.6303,0.0232;3.2092,-0.7393,-0.951;3.2478,0.216,0.9185;4.1454,0.7823,0.6701)|",6.57971290509
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"[H]OC([H])([H])C1=NC([H])([H])[C@]2([H])[C@]([H])(C([H])([H])O[H])[C@@]2([H])C([H])([H])N1[H] |(-2.8471,-2.5311,-0.8141;-2.502,-3.3773,-0.4681;-1.5689,-2.9787,0.5151;-2.0768,-2.6883,1.4553;-0.9159,-3.8242,0.7406;-0.7262,-1.7887,0.063;0.4928,-1.7526,0.4294;1.4671,-0.6788,0.1979;1.874,-0.4224,1.1854;2.3005,-1.1201,-0.3652;0.8581,0.5257,-0.4875;0.6988,0.3586,-1.5529;0.7097,2.001,-0.1303;0.5554,2.6381,-1.0007;1.5011,2.6781,0.9677;1.5899,2.0104,1.8408;0.981,3.5812,1.3044;2.7766,3.1177,0.519;3.236,2.3405,0.1632;-0.3695,1.0098,0.2283;-0.3466,0.7556,1.2891;-1.6567,0.5572,-0.4256;-1.8781,1.0988,-1.3517;-2.5149,0.6942,0.2413;-1.5501,-0.8892,-0.7774;-1.2011,-0.9726,-1.7296)|",6.087186835684999
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"[H]O/N=C(\[H])C1=C(OC([H])([H])/C([H])=C(\[H])C([H])([H])[H])C([H])=C([H])C(Cl)=C1[H] |(6.9168,6.6385,3.1593;6.3463,5.8551,3.1155;7.2756,4.8107,3.0228;6.681,3.676,2.9568;5.594,3.6145,2.977;7.4503,2.4322,2.8548;6.7703,1.1925,2.7794;5.4094,1.2528,2.811;4.6498,0.0424,2.6747;4.9491,-0.46,1.7424;4.8658,-0.6345,3.5124;3.1992,0.4095,2.642;2.8986,1.0832,1.8397;2.2952,-0.0501,3.5099;2.6265,-0.7156,4.3095;0.8296,0.27,3.4836;0.2274,-0.6415,3.3723;0.5787,0.9475,2.6611;0.5147,0.7412,4.4241;7.5,0.0012,2.6851;6.9938,-0.955,2.6319;8.8945,0.0265,2.6607;9.4559,-0.8986,2.5866;9.5597,1.2484,2.7328;11.3199,1.2818,2.7014;8.852,2.4396,2.83;9.3709,3.3892,2.8876)|",4.590560657935001
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"[H]C(/C(C(=O)OC([H])([H])C([H])([H])[H])=C(/[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])=C(/[H])C([H])([H])C([H])([H])OC([H])([H])[H] |(8.2339,-2.3562,-0.6931;7.3851,-1.8444,-1.1439;6.2133,-2.6895,-1.4464;6.4583,-4.0539,-2.0224;5.5933,-4.8435,-2.3517;7.7835,-4.3149,-2.1405;8.1423,-5.6108,-2.6721;7.4346,-5.875,-3.4615;9.1331,-5.4585,-3.1079;8.1706,-6.6715,-1.5814;8.5187,-7.624,-1.9976;7.1701,-6.8227,-1.1669;8.8497,-6.3811,-0.7728;4.9185,-2.378,-1.2166;4.1937,-3.1344,-1.5149;4.3569,-1.1447,-0.5791;3.661,-1.4596,0.2136;5.1495,-0.5588,-0.1021;3.5756,-0.2537,-1.5698;4.255,0.088,-2.3634;2.803,-0.8544,-2.0703;2.9225,0.9582,-0.8934;2.2436,0.6079,-0.1031;3.6966,1.5526,-0.3878;2.1513,1.8488,-1.8727;1.6949,2.7032,-1.3601;1.3484,1.2902,-2.3691;2.8114,2.2435,-2.6548;7.4996,-0.5357,-1.4122;6.6872,-0.0244,-1.9299;8.7063,0.2991,-1.0853;9.4433,-0.2909,-0.5286;8.4211,1.1445,-0.4427;9.3723,0.8776,-2.3357;8.6374,1.446,-2.9338;9.751,0.0633,-2.9766;10.4308,1.722,-1.925;11.131,2.2876,-3.0111;11.921,2.9178,-2.5938;10.4748,2.9092,-3.6424;11.5902,1.5148,-3.6491)|",5.186489990529999
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"[H]C1=C(Cl)C([H])=C2C(=C1[H])C(/C([H])=C(/N(=O)=O)C([H])([H])[H])=C(/[H])N2[H] |(3.9611,6.9944,-0.7042;3.3604,6.2746,-1.2487;2.4159,6.7352,-2.1856;2.2481,8.4687,-2.4352;1.6138,5.8724,-2.9201;0.895,6.2489,-3.6398;1.7882,4.5075,-2.678;2.7259,4.0028,-1.7443;3.5199,4.9126,-1.0297;4.2561,4.5651,-0.3106;2.6298,2.5535,-1.7722;3.4035,1.6725,-0.9334;3.909,2.1394,-0.0932;3.5883,0.3391,-1.0495;4.3849,-0.3015,0.0091;4.8864,0.3963,0.8959;4.504,-1.53,-0.0459;3.0886,-0.5775,-2.121;3.0061,-0.0328,-3.0667;2.1027,-0.9969,-1.8787;3.7706,-1.4185,-2.2522;1.6442,2.2583,-2.7041;1.2267,1.3064,-2.9943;1.1478,3.4174,-3.2407;0.4038,3.465,-3.9206)|",3.76061341391
"[H]C1=C([H])C2=C(C([H])=C1F)C(/C([H])=C(/N(=O)=O)C([H])([H])[H])=C(/[H])N2[H] |(3.23,7.4967,-1.6959;3.0612,6.4279,-1.6209;2.1023,5.7915,-2.3985;1.4955,6.3536,-3.1026;1.9523,4.4118,-2.2392;2.7304,3.6586,-1.3253;3.6922,4.3162,-0.5417;4.313,3.7972,0.1808;3.8316,5.6816,-0.7151;4.7512,6.3431,0.0217;2.298,2.2755,-1.4241;2.8723,1.1883,-0.6731;3.8112,1.4014,-0.1708;2.3992,-0.0672,-0.508;3.2452,-0.9871,0.2672;2.8554,-2.1553,0.3718;4.2944,-0.5695,0.7675;1.1158,-0.6441,-1.0162;0.3437,0.1318,-1.0409;0.7817,-1.4585,-0.3727;1.2203,-1.0545,-2.03;1.2971,2.2604,-2.3888;0.7431,1.4297,-2.7981;1.0934,3.5236,-2.8691;0.4327,3.7654,-3.5921)|",3.795988214475
"[H]OC(=O)[C@@]([H])(N(O)C([H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])[H] |(3.9418,-1.3636,0.5933;3.4711,-1.9736,-0.0453;2.4534,-1.2992,-0.5806;1.6279,-1.795,-1.3131;2.416,0.2095,-0.2504;1.4118,0.5679,-0.4569;2.7108,0.4055,1.1993;3.9281,0.0901,1.5344;1.8549,0.874,2.0736;2.3105,1.0402,3.0453;0.4352,1.1835,1.8992;-0.4577,0.4229,1.1168;-0.1153,-0.4594,0.5851;-1.8055,0.7707,1.0494;-2.479,0.1712,0.4439;-2.2928,1.8653,1.7659;-3.3452,2.1287,1.7111;-1.4241,2.6086,2.5686;-1.7962,3.4546,3.1394;-0.0775,2.2656,2.6421;0.5953,2.8452,3.2691;3.4355,0.9886,-1.0921;3.2157,0.8223,-2.1507;4.4531,0.66,-0.8773;3.358,2.06,-0.8822)|",4.34837933099
"[H]ON([H])[C@@]([H])(C(=O)OC(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(5.5192,0.0142,-2.987;4.8904,-0.1212,-2.261;5.6988,-0.8341,-1.2908;5.1189,-1.6522,-1.0842;5.723,-0.0269,-0.0661;6.1439,0.9494,-0.3164;4.307,0.1486,0.5008;3.5661,-0.8029,0.6678;4.0356,1.4282,0.7731;2.7256,1.8562,1.3138;2.4841,1.2081,2.6802;1.5792,1.6337,3.1291;3.3264,1.407,3.3509;2.3536,0.1287,2.5874;1.6227,1.5247,0.3041;0.6733,1.9516,0.6471;1.5019,0.4462,0.1906;1.8593,1.9576,-0.6738;2.9056,3.3697,1.4517;3.1248,3.8223,0.4793;3.7301,3.5995,2.134;1.9901,3.8225,1.8468;6.6235,-0.7571,0.9582;6.1706,-1.7323,1.1734;7.5824,-0.9469,0.4629;6.8318,0.0078,2.2484;7.7428,1.0717,2.3093;8.3171,1.3353,1.4235;7.9278,1.7897,3.4905;8.6421,2.6085,3.5184;7.2026,1.4535,4.6358;7.3486,2.0093,5.5582;6.2952,0.3942,4.5897;5.7322,0.1189,5.4778;6.113,-0.3218,3.4045;5.4051,-1.1466,3.3744)|",6.465425087880001
"[H]C([H])=C([H])[C@@]1(C([H])([H])[H])OC(C([H])([H])[H])(C([H])([H])[H])O[C@]1([H])C([H])([H])C(=O)C([H])([H])[H] |(0.9808,-1.0307,1.3981;1.3206,-0.8293,0.3861;0.7447,-1.2519,-0.4307;2.4078,-0.096,0.1549;2.9819,0.3071,0.9899;2.9613,0.2388,-1.2097;4.409,-0.2616,-1.3411;5.0415,0.1055,-0.5248;4.8452,0.0592,-2.2925;4.4103,-1.3549,-1.3099;2.1657,-0.3773,-2.2351;1.9083,0.5564,-3.2963;2.4384,0.0016,-4.6131;2.2723,0.7188,-5.4233;1.9246,-0.932,-4.8613;3.51,-0.197,-4.5295;0.4083,0.8559,-3.3425;0.1855,1.5637,-4.1473;0.0729,1.2843,-2.3937;-0.154,-0.0665,-3.5187;2.6538,1.7297,-2.9711;2.7859,1.7493,-1.55;1.8553,2.1067,-1.0866;3.9091,2.7123,-1.169;4.0402,2.6865,-0.0814;4.8436,2.4084,-1.6498;3.5186,4.1431,-1.5579;2.6992,4.7537,-0.8987;4.1629,4.7169,-2.8009;5.2534,4.7594,-2.6827;3.7743,5.7176,-3;3.9566,4.0533,-3.6495)|",6.223243760935
"[H]C(=O)C([H])([H])[C@@]1([H])OC(C([H])([H])[H])(C([H])([H])[H])O[C@@]1(C([H])=C([H])[H])C([H])([H])[H] |(3.4198,4.9509,1.7347;2.5661,4.4516,2.2376;1.6663,5.097,2.7308;2.6222,2.9366,2.2467;1.7186,2.5678,2.7422;3.4955,2.6292,2.8374;2.744,2.368,0.8184;1.8712,2.6956,0.2418;3.8957,2.8862,0.159;4.937,1.9003,0.1366;6.0613,2.2718,1.1052;6.495,3.2376,0.8283;6.8498,1.5129,1.0764;5.6826,2.334,2.1289;5.4417,1.7821,-1.3008;5.9049,2.7237,-1.6131;4.6111,1.5599,-1.9732;6.1884,0.985,-1.3743;4.3434,0.6801,0.5914;2.9221,0.8209,0.6984;2.199,0.291,-0.5313;1.1431,0.5588,-0.5898;2.7338,-0.502,-1.4573;2.1443,-0.88,-2.2878;3.7767,-0.7999,-1.4098;2.4712,0.0069,1.9187;1.3862,0.0709,2.0564;2.9646,0.3497,2.832;2.7302,-1.0434,1.7589)|",6.043648619605
"[H]C(C#N)N(O)[C@]([H])(C(=O)OC(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H] |(4.2126,2.1195,-1.1465;3.8585,1.5236,-0.3152;4.7619,0.8595,0.541;5.5316,0.3267,1.2376;2.5431,1.4478,-0.1744;1.7415,2.0308,-0.9436;1.9289,0.62,0.9198;2.7092,0.424,1.6608;1.5401,-0.7381,0.2994;1.947,-1.1231,-0.7722;0.7529,-1.4004,1.1525;0.2752,-2.7799,0.8655;-0.5948,-2.7687,-0.3938;-1.0402,-3.7598,-0.5338;-0.009,-2.5217,-1.2806;-1.4085,-2.0429,-0.2907;1.4757,-3.7223,0.7448;1.1183,-4.7547,0.6632;2.109,-3.6531,1.6357;2.0768,-3.4919,-0.1363;-0.5565,-3.1038,2.1073;-0.9761,-4.1114,2.0213;-1.3826,-2.394,2.2179;0.0606,-3.0621,3.0105;0.7732,1.3869,1.5592;0.3479,0.782,2.3616;0.0013,1.6052,0.8197;1.1336,2.3307,1.9791)|",4.617772042985001
"[H]N(C([H])([H])C#N)[C@]([H])(C(=O)OC(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H] |(3.4507,1.0212,0.9172;3.3888,0.0108,0.7941;4.7174,-0.4923,0.4369;4.6459,-1.5747,0.2702;5.1375,-0.047,-0.48;5.6734,-0.2651,1.5313;6.4383,-0.0698,2.3814;2.3727,-0.268,-0.2144;2.3952,-1.3417,-0.4368;2.6229,0.4909,-1.5315;3.3297,1.4784,-1.6002;1.9503,-0.0742,-2.5445;2.015,0.4578,-3.9274;3.4544,0.3692,-4.4423;3.4808,0.6561,-5.4994;3.8299,-0.6568,-4.3607;4.1159,1.0354,-3.8857;1.0981,-0.4972,-4.6939;1.068,-0.2184,-5.7524;0.0785,-0.4581,-4.2974;1.46,-1.5274,-4.617;1.4707,1.8886,-3.9663;1.4156,2.2254,-5.0076;2.1139,2.5736,-3.4122;0.46,1.928,-3.5455;0.9821,0.0902,0.3339;0.2028,-0.1596,-0.3916;0.9179,1.1627,0.5528;0.8043,-0.4606,1.2611)|",6.6912795837950005
"[H]C([H])([H])C1(C([H])([H])[H])[C@]2([H])C(S=O)=C(S=O)[C@@]1(C([H])([H])[H])C([H])([H])C2([H])[H] |(0.4983,-0.9455,0.4097;0.8706,-0.0279,-0.0614;0.4409,0.0268,-1.0636;0.4732,0.8148,0.5152;2.4085,-0.0161,-0.0351;2.8466,-0.0475,1.4391;2.464,-0.9507,1.9295;2.4375,0.814,1.9784;3.9323,-0.0335,1.5661;3.0577,-1.1383,-0.9091;2.8529,-2.1705,-0.6115;4.5158,-0.7297,-0.8448;5.7977,-1.782,-0.8216;7.099,-0.995,-0.8074;4.5414,0.704,-0.8509;5.9479,1.6179,-0.91;5.6392,3.0924,-1.0009;3.0699,1.1516,-0.8817;2.7439,2.5765,-0.4514;3.1833,3.3099,-1.1291;3.1262,2.7947,0.5506;1.6565,2.7152,-0.4418;2.6021,0.7875,-2.3339;1.6073,1.2057,-2.5201;3.2747,1.2161,-3.0818;2.5888,-0.77,-2.3512;3.2574,-1.192,-3.1065;1.5886,-1.1705,-2.5419)|",2.8980125078250003
"[H]C(NN)C(=O)C(=O)OC([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(3.6758,-5.5962,1.1859;3.3004,-4.6276,1.491;2.7433,-4.5932,2.6804;2.2769,-4.6355,3.7138;3.4375,-3.5014,0.5866;3.964,-3.6156,-0.5078;2.8849,-2.1479,1.0799;2.3701,-2.0089,2.1728;3.0623,-1.1954,0.1695;2.5732,0.1398,0.5112;2.718,0.2958,1.5818;3.2337,0.8012,-0.0522;1.1324,0.3255,0.1105;0.8089,0.6699,-1.2083;1.6043,0.8048,-1.9377;-0.5214,0.8385,-1.5903;-0.7606,1.1083,-2.6154;-1.5431,0.6664,-0.6537;-2.5802,0.802,-0.9489;-1.2296,0.3223,0.6625;-2.0217,0.1876,1.3941;0.1018,0.1508,1.0432;0.3484,-0.1287,2.0633)|",4.710290752155
"[H]OC1=NSN([H])[C@@]1([H])N1C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C1([H])[H] |(1.6114,-1.5961,-2.2886;2.5243,-1.9164,-2.4366;3.0035,-2.1813,-1.2052;4.2475,-2.2814,-0.9512;4.4769,-2.5208,0.7315;2.7436,-2.665,1.1412;2.591,-3.6451,1.3693;1.9708,-2.3448,-0.0816;1.3236,-3.1978,-0.3242;1.1065,-1.1765,-0.0597;-0.1893,-1.378,0.6136;-0.3431,-2.4564,0.7326;-0.1825,-0.9597,1.6314;-1.3568,-0.8145,-0.2109;-1.2731,-1.2327,-1.2234;-2.2967,-1.199,0.2095;-1.4457,0.7166,-0.2899;-2.2399,0.9803,-1.0008;-1.7717,1.1065,0.6856;-0.1481,1.4443,-0.6808;-0.3838,2.5075,-0.8162;0.2172,1.0846,-1.6524;0.9548,1.3169,0.3861;0.4888,1.3853,1.3781;1.6369,2.1736,0.3227;1.8316,0.0575,0.3055;2.3553,-0.0755,1.2642;2.6018,0.2094,-0.46)|",5.175605436510001
"[H]OC([H])([H])[C@]1([H])C([H])([H])C([H])([H])N([H])[C@@]1([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H] |(6.5811,2.6348,-1.299;7.2137,1.9164,-1.1401;6.5275,0.688,-1.359;6.1792,0.6118,-2.4022;7.278,-0.0955,-1.2125;5.3386,0.4752,-0.4078;4.5977,1.2628,-0.6077;5.7667,0.4694,1.0711;6.552,1.204,1.2723;4.9086,0.7063,1.7115;6.2207,-0.9835,1.2934;6.0419,-1.3253,2.3201;7.301,-1.0802,1.1137;5.4737,-1.7998,0.3127;4.8727,-2.4759,0.7704;4.7054,-0.9271,-0.6021;4.8713,-1.2589,-1.6383;3.1842,-0.9156,-0.3452;2.9954,-0.4925,0.6525;2.7209,-0.2244,-1.0662;2.4571,-2.2726,-0.4525;2.9118,-2.9718,0.2671;0.9817,-2.1127,-0.0537;0.4545,-3.0733,-0.0913;0.4653,-1.4238,-0.7349;0.8814,-1.7125,0.9624;2.5766,-2.8978,-1.8505;2.0289,-3.8462,-1.9006;3.6175,-3.1022,-2.1222;2.1546,-2.2306,-2.6138)|",7.425986980145
"[H]OC(=O)[C@@]([H])(N(O)C([H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H] |(5.5502,1.2642,-2.4363;6.1039,0.5639,-1.9822;5.8037,0.5895,-0.6848;6.4189,-0.0288,0.1573;4.5789,1.4482,-0.3094;4.6152,1.6207,0.7614;4.7023,2.7661,-1.0044;4.5898,2.6934,-2.2991;4.8772,3.9119,-0.3932;4.8196,4.7541,-1.0765;5.1279,4.1734,1.0247;4.6008,5.3675,1.556;4.0025,6.0136,0.9186;4.8247,5.7192,2.8836;4.3992,6.6385,3.2761;5.5963,4.8945,3.7056;5.7758,5.1694,4.741;6.1513,3.7242,3.1843;6.7749,3.091,3.8088;5.9296,3.3648,1.8563;6.4092,2.4727,1.4657;3.2163,0.8244,-0.6942;2.4598,1.5746,-0.4283;3.1757,0.7105,-1.7806;2.8462,-0.513,-0.0066;1.7685,-0.6222,-0.1969;3.5325,-1.7402,-0.6308;3.0951,-2.6607,-0.2267;4.6044,-1.7666,-0.4153;3.4013,-1.7594,-1.7192;3.0398,-0.4801,1.5177;2.6068,-1.3755,1.9778;2.5524,0.3933,1.9695;4.103,-0.4571,1.7833)|",4.334773638465
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"[H]C1=C2/N[C@]([H])(N([H])[H])C(=O)N/C2=C([H])/C(Br)=C\1[H] |(-0.4155,1.4328,2.1466;-0.4127,1.141,1.1013;0.8444,1.286,0.3779;1.8941,1.6852,1.0134;3.1397,1.7118,0.2694;3.5559,0.6894,0.3834;4.0686,2.6417,0.8871;3.5832,3.525,1.0398;4.8023,2.8388,0.2066;2.9764,1.8612,-1.2567;3.8001,2.4484,-1.9289;1.8762,1.2178,-1.8605;0.8603,0.9559,-1.0855;-0.3339,0.3737,-1.6633;-0.3013,0.1013,-2.7116;-1.4426,0.2207,-0.8985;-3.0295,-0.5307,-1.6436;-1.5042,0.6211,0.4987;-2.4354,0.4848,1.0367)|",2.5878027182550003
"[H]C1=NC(=O)C2=C(C([H])=C([H])C(F)=C2F)C1=C([H])[H] |(2.6055,-2.0807,-0.0062;3.1416,-1.127,-0.0063;4.4233,-1.1833,-0.0053;5.1704,0.0284,-0.0075;6.3841,-0.0339,-0.0103;4.432,1.3334,-0.007;3.0149,1.3683,-0.0071;2.3455,2.5994,-0.0077;1.2617,2.6332,-0.0085;3.0533,3.7957,-0.0077;2.5502,4.7569,-0.0075;4.4409,3.7623,-0.0078;5.1453,4.9031,-0.0084;5.1297,2.5506,-0.0079;6.4601,2.6097,-0.0085;2.3049,0.0788,-0.0067;0.9668,-0.0907,-0.0054;0.5408,-1.0898,-0.0057;0.2595,0.7316,-0.004)|",4.027284987400001
"[H]C1=C([H])C(Cl)=C(OC(=O)[C@@]([H])(Cl)C([H])([H])[H])C([H])=C1[H] |(4.9484,-2.654,6.9801;4.331,-2.2807,6.1686;3.476,-3.1539,5.4979;3.417,-4.2013,5.7734;2.6779,-2.6781,4.4574;1.598,-3.7729,3.6204;2.742,-1.3312,4.084;1.9837,-0.9277,2.9913;1.1676,0.1736,3.0823;1.0473,0.8637,4.0554;0.501,0.3514,1.7176;0.1238,-0.6208,1.3946;-0.9352,1.4352,1.8659;1.4906,0.9042,0.688;2.3324,0.2126,0.5792;1.8688,1.8817,1.0011;0.9979,1.0143,-0.2813;3.6014,-0.4606,4.753;3.6386,0.581,4.4575;4.3925,-0.937,5.7966;5.0573,-0.2545,6.3171)|",6.05181203512
"[H]C#CC([H])([H])[C@@](C#N)(C([H])([H])[H])C([H])([H])[C@]([H])(O[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(6.2653,-3.8983,-4.1908;5.9841,-3.5897,-3.2092;5.694,-3.2512,-2.0858;5.3218,-2.852,-0.7288;6.2149,-2.5324,-0.1772;4.8995,-3.7161,-0.2094;4.2818,-1.678,-0.7029;3.9328,-1.4357,0.7108;3.6612,-1.2412,1.8231;4.9305,-0.3925,-1.2693;5.2277,-0.5617,-2.3089;5.8211,-0.1192,-0.6947;4.2302,0.4477,-1.2358;2.9816,-1.9898,-1.5013;2.3606,-1.0902,-1.4593;3.2734,-2.119,-2.5514;2.1579,-3.2049,-1.0384;2.0424,-3.1548,0.0519;2.8737,-4.4263,-1.2633;3.268,-4.3908,-2.1498;0.7158,-3.2787,-1.6377;0.7547,-3.3989,-3.1737;-0.259,-3.5148,-3.5743;1.3304,-4.2768,-3.4905;1.1929,-2.5124,-3.6478;0.0281,-4.5287,-1.0508;-0.9886,-4.6282,-1.449;-0.0457,-4.4596,0.0418;0.5886,-5.4357,-1.2912;-0.1014,-2.0358,-1.2316;-1.1535,-2.1714,-1.508;0.2453,-1.121,-1.7252;-0.0643,-1.8694,-0.1477)|",7.78789840131
"[H]O[C@]1([H])C([H])([H])C([H])([H])[C@]2(C([H])([H])[H])C(=O)N(C3=C([H])C([H])=C([H])C([H])=C3[H])O[C@@]21[H] |(5.5254,-0.2188,-2.0522;5.0043,0.4084,-2.5829;3.8986,0.7641,-1.7856;3.1703,1.2242,-2.4656;4.1798,1.7347,-0.6218;5.0767,1.4054,-0.0797;4.3619,2.756,-0.9682;2.9278,1.5914,0.2535;2.0956,2.1637,-0.1765;3.0591,1.9288,1.2857;2.5793,0.0726,0.2005;1.0826,-0.2085,0.3744;0.5023,0.3103,-0.3968;0.7519,0.1439,1.3561;0.8631,-1.2799,0.3078;3.3658,-0.6664,1.29;3.1974,-0.5455,2.4956;4.2903,-1.4726,0.6828;5.2107,-2.4039,1.2045;5.9288,-3.232,0.3294;5.768,-3.1605,-0.7383;6.8406,-4.1509,0.8468;7.3917,-4.7891,0.1614;7.0451,-4.2565,2.2224;7.7573,-4.9743,2.6188;6.3239,-3.4272,3.0838;6.4722,-3.4975,4.158;5.4092,-2.4986,2.5927;4.8473,-1.8584,3.2588;4.2501,-1.4058,-0.731;3.2143,-0.4461,-1.1042;2.5331,-0.9866,-1.7669)|",5.24635503764
"[H]O[C@]([H])(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[C@](C#N)(C([H])([H])[H])C([H])([H])C([H])=C([H])[H] |(5.8655,-0.0871,2.5349;4.9395,0.0636,2.2865;4.5151,-1.0585,1.4983;3.4431,-0.8825,1.3498;4.6409,-2.4006,2.2879;3.9588,-3.5412,1.5047;3.9138,-4.4458,2.1222;4.4954,-3.8045,0.5868;2.9315,-3.275,1.2283;6.1165,-2.7665,2.5455;6.1825,-3.6959,3.1226;6.638,-1.9972,3.1312;6.6726,-2.9191,1.6132;3.9128,-2.2348,3.6375;3.9765,-3.1598,4.2224;2.8511,-2.0071,3.4836;4.343,-1.421,4.2273;5.2147,-1.0432,0.1239;5.1635,-2.0353,-0.3356;6.281,-0.8223,0.2601;4.65,-0.0323,-0.921;3.2317,-0.3689,-1.1644;2.1168,-0.638,-1.3497;5.4158,-0.2015,-2.2539;6.4683,0.0585,-2.1088;5.0054,0.4564,-3.0267;5.3527,-1.2331,-2.6147;4.6961,1.4649,-0.4538;4.2344,2.0712,-1.2421;4.0798,1.5653,0.4451;6.0771,1.9849,-0.1599;6.5426,1.6086,0.7491;6.7297,2.8793,-0.9044;7.7184,3.2389,-0.6312;6.2947,3.2983,-1.8098)|",7.273603223865001
"[H]OC(=O)[C@]12N([H])C([H])([H])C([H])([H])C([H])([H])[C@]1([H])C([H])([H])C([H])([H])C2([H])[H] |(0.6112,0.3854,-2.1614;0.2939,-0.4652,-2.5033;-0.1505,-1.2465,-1.4845;-0.6158,-2.333,-1.7365;0.0026,-0.7631,-0.0092;-1.1257,-1.2558,0.7778;-1.2098,-2.2589,0.6154;-2.3907,-0.6201,0.3497;-2.646,-0.8739,-0.6942;-3.1918,-1.0219,0.9798;-2.3365,0.9085,0.4966;-3.269,1.3383,0.1118;-2.2779,1.1617,1.5633;-1.1172,1.5234,-0.2241;-1.0358,2.5924,0.0099;-1.2441,1.4453,-1.3137;0.1234,0.7595,0.2413;0.1222,0.8194,1.3382;1.5527,1.1523,-0.1926;1.6091,1.5517,-1.2177;1.9378,1.9596,0.4385;2.3805,-0.1659,-0.0416;3.227,-0.0397,0.6396;2.7984,-0.4687,-1.0074;1.391,-1.2424,0.481;1.3571,-1.2526,1.5763;1.6418,-2.253,0.144)|",6.530732412
"[H]O[C@]1(C([H])([H])[H])[C@@]2([H])C([H])=C([H])[C@]1([H])[C@]1([H])C([H])([H])OC([H])([H])[C@@]21[H] |(2.7734,0.5892,1.7808;2.7578,1.1015,0.9579;2.8617,0.1731,-0.1233;1.9323,-1.0051,0.1817;1.8872,-1.7346,-0.6271;0.9225,-0.6276,0.3766;2.2741,-1.5312,1.0845;4.345,-0.188,-0.4896;4.9062,-0.7623,0.2527;4.0864,-0.8681,-1.8348;4.6809,-1.6807,-2.2411;3.0978,-0.1989,-2.448;2.7147,-0.3735,-3.4483;2.6117,0.8998,-1.5157;1.5922,1.2608,-1.674;3.693,2.0022,-1.3273;3.374,2.5272,-0.423;4.3519,3.092,-2.1542;3.7771,4.0208,-2.233;4.6521,2.7702,-3.1619;5.5378,3.3785,-1.3505;5.7759,2.3201,-0.3733;6.8562,2.1426,-0.3499;5.4382,2.6352,0.623;4.9512,1.1861,-0.9663;5.4851,0.9559,-1.8969)|",6.628693398179999
"[H]C([H])([H])C([H])([H])O[C@@]1([H])OC(C([H])(C([H])([H])[H])C([H])([H])[H])(C([H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C1([H])[H] |(0.2304,2.6826,-1.0514;0.7951,1.8803,-0.5635;0.3387,1.679,0.4112;0.711,0.9747,-1.1732;2.2516,2.2815,-0.3992;2.3425,3.1914,0.2094;2.7099,2.4951,-1.3796;2.9442,1.2032,0.2242;4.3304,1.4161,0.3649;4.7354,1.7562,-0.6003;4.6427,2.4283,1.3056;5.0424,1.8845,2.5981;4.0177,2.3597,3.6785;4.2946,1.8405,4.6074;4.0727,3.8767,3.9394;3.2778,4.1673,4.6359;3.9177,4.4296,3.006;5.0208,4.2053,4.3752;2.5605,1.9824,3.3587;1.9261,2.222,4.2202;2.4215,0.922,3.132;2.1936,2.5504,2.5001;6.4767,2.4425,2.8723;6.3835,3.5338,2.8229;7.0221,2.0756,4.2635;8.0287,2.4902,4.3931;7.1019,0.9892,4.3945;6.4009,2.4633,5.0766;7.4975,2.0516,1.7909;8.4435,2.577,1.9666;7.1435,2.3268,0.7937;7.7212,0.9776,1.7996;5.0321,0.3413,2.4086;5.917,-0.1258,2.8493;4.1625,-0.1067,2.8941;4.9694,0.1287,0.8868;4.3793,-0.7426,0.5932;5.9716,0.0217,0.462)|",8.764787124605
"[H]O/C(=C([H])/C([H])=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1[H])[C@]([H])(O[H])C([H])([H])O[H] |(7.3036,-0.0956,-0.3575;7.2357,-0.9711,-0.7713;5.9152,-1.3232,-0.8914;4.905,-0.5135,-0.5059;5.1669,0.4717,-0.1148;3.4935,-0.8189,-0.5684;3.2134,-1.8086,-0.9224;2.5226,0.0533,-0.2122;2.8317,1.0443,0.1225;1.0758,-0.1655,-0.2279;0.4764,-1.3947,-0.5703;1.0974,-2.2454,-0.836;-0.9068,-1.5422,-0.5694;-1.3433,-2.5016,-0.8353;-1.7352,-0.4696,-0.2247;-2.8151,-0.5895,-0.2232;-1.1605,0.7544,0.1199;-1.7916,1.5972,0.3898;0.2249,0.9018,0.118;0.6644,1.8598,0.3869;5.784,-2.7003,-1.5133;4.9275,-3.2128,-1.0555;6.954,-3.4899,-1.2917;7.5976,-2.9281,-0.8301;5.5606,-2.6446,-3.0379;4.7109,-1.9985,-3.2784;5.3277,-3.6673,-3.3786;6.691,-2.1212,-3.7006;7.4415,-2.6317,-3.3503)|",4.05993864946
"[H]C1=C(OC([H])([H])C([H])([H])[H])C(OC(=O)C([H])([H])[H])=C([H])C(/C([H])=C(\[H])C([H])([H])[H])=C1[H] |(5.1343,-2.584,1.3245;5.0397,-1.7808,0.6029;6.1763,-1.1353,0.1112;7.4587,-1.4031,0.4719;7.7076,-2.5236,1.3221;7.2045,-2.3766,2.2884;7.2991,-3.4328,0.8597;9.2112,-2.6327,1.5047;9.4508,-3.4887,2.1447;9.6096,-1.7269,1.9728;9.7053,-2.7702,0.5381;5.9963,-0.1075,-0.8359;7.1074,0.5906,-1.3092;7.9615,-0.0626,-2.1645;7.764,-1.1677,-2.6002;9.1544,0.8135,-2.4573;9.745,0.3659,-3.2574;9.7692,0.9045,-1.555;8.8337,1.8207,-2.739;4.7376,0.2745,-1.2562;4.6694,1.0711,-1.9896;3.5822,-0.3574,-0.7541;2.2188,0.0129,-1.1569;1.4386,-0.6391,-0.7605;1.8404,1.0421,-1.9296;2.5918,1.7227,-2.3309;0.4189,1.3531,-2.2954;0.1284,2.356,-1.9526;-0.277,0.6305,-1.856;0.2751,1.3429,-3.3849;3.7696,-1.3933,0.1707;2.9006,-1.9082,0.5729)|",4.90349158601
"[H]OC([H])([H])C#CC([H])([H])[C@@]([H])(OC([H])([H])C1=C([H])C([H])=C(OC([H])([H])[H])C([H])=C1[H])C([H])([H])[H] |(1.6181,4.7852,2.7945;1.5679,5.1986,1.9182;0.7188,4.3803,1.1173;-0.3317,4.4582,1.4435;0.7705,4.8056,0.109;1.115,2.9669,1.0944;1.4486,1.8033,1.128;1.8703,0.4034,1.1406;1.0165,-0.2537,0.9248;2.2253,0.1234,2.1405;2.9949,0.0835,0.1338;3.8562,0.7312,0.3626;3.3435,-1.2774,0.3864;4.6746,-1.6262,0.0235;4.853,-1.3846,-1.0372;5.3972,-1.0385,0.6131;4.8856,-3.1006,0.2635;5.989,-3.5684,0.9746;6.7016,-2.8591,1.3902;6.2076,-4.9363,1.1733;7.0783,-5.2595,1.7324;5.2953,-5.8603,0.6555;5.3972,-7.2157,0.7914;6.507,-7.7341,1.5057;6.3885,-8.8192,1.4983;6.5204,-7.3788,2.5448;7.4577,-7.4701,1.0234;4.1759,-5.4025,-0.0583;3.4765,-6.1351,-0.4493;3.9804,-4.0432,-0.2515;3.1071,-3.6975,-0.7971;2.5645,0.2983,-1.3173;2.2702,1.3401,-1.479;1.7114,-0.3472,-1.5559;3.3766,0.0634,-2.0132)|",5.8831014478100006
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"[H]O[C@]([H])(C([H])([H])C(=O)C1=NC([H])=C([H])N1C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(3.8394,-0.9249,1.3145;3.8007,-0.8215,0.3452;3.3198,-2.0674,-0.1436;2.2856,-2.2333,0.2105;4.1891,-3.2194,0.4039;3.9364,-4.1875,-0.0331;5.2409,-3.0233,0.1521;4.1166,-3.3283,1.916;3.9359,-2.345,2.6427;4.2859,-4.663,2.4999;4.4269,-5.7884,1.7985;4.5596,-6.7808,2.7143;4.6893,-7.8143,2.4226;4.5003,-6.2607,3.9951;4.5643,-6.7265,4.968;4.3259,-4.9138,3.8585;4.2096,-3.962,4.9623;4.3185,-4.5143,5.8981;3.2391,-3.4641,4.9357;4.9861,-3.1992,4.8898;3.2551,-1.9841,-1.6939;2.3419,-0.7959,-2.0628;2.2705,-0.6935,-3.1524;2.7286,0.1382,-1.6483;1.3266,-0.9441,-1.673;2.6432,-3.2742,-2.2735;2.476,-3.1601,-3.351;1.6735,-3.5004,-1.8115;3.2934,-4.1454,-2.1366;4.6547,-1.7496,-2.2931;4.5847,-1.5995,-3.3772;5.3198,-2.6054,-2.1257;5.1205,-0.8632,-1.8524)|",5.0177794032200005
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"[H]O[C@]([H])(C([H])([H])[H])[C@@]([H])(C(=O)OC([H])([H])[H])[C@@]([H])(C(=O)OC([H])([H])[H])C([H])([H])[H] |(4.903,1.3318,1.8159;5.2569,0.8985,1.0214;4.7764,1.6201,-0.1141;5.3107,1.1935,-0.9665;5.1242,3.1055,-0.0034;4.8683,3.6442,-0.9208;6.1976,3.2203,0.1759;4.584,3.5752,0.8307;3.2594,1.3832,-0.3138;2.7158,1.8849,0.4957;2.7866,1.9811,-1.63;3.4357,2.0235,-2.6547;1.5194,2.4401,-1.5334;0.9434,2.9241,-2.7605;-0.0579,3.2658,-2.4979;0.8932,2.1203,-3.4991;1.54,3.7464,-3.1629;2.8859,-0.1366,-0.2401;3.2816,-0.4706,0.7247;1.371,-0.2905,-0.1886;0.6762,-0.7536,-1.0668;0.8778,0.1699,0.9858;-0.5527,0.1326,1.1069;-0.7733,0.5213,2.1018;-0.9218,-0.891,1.0036;-1.0164,0.7574,0.3388;3.4849,-0.9966,-1.357;4.5772,-0.9635,-1.3219;3.1572,-0.6656,-2.3453;3.1733,-2.0382,-1.2342)|",7.268160946855001
"[H]OC([H])([H])[C@]1(C([H])([H])[H])C2=C(C([H])=C([H])C([H])=C2[H])N([H])[C@@]1([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.9045,0.2492,-0.6211;4.4617,-0.436,-1.1489;3.1035,-0.0607,-1.2724;2.9915,0.8374,-1.9029;2.6136,-0.8925,-1.7928;2.3846,0.1974,0.0779;0.9029,0.474,-0.2207;0.8094,1.309,-0.9251;0.3507,0.7349,0.6853;0.4175,-0.3984,-0.6727;3.0657,1.3415,0.82;3.8184,0.8292,1.8909;4.5552,1.6777,2.7231;5.1339,1.2816,3.5535;4.5258,3.0512,2.4611;5.0898,3.7231,3.1031;3.7884,3.571,1.3942;3.7803,4.6406,1.2067;3.0549,2.708,0.5673;2.4739,3.1113,-0.2591;3.7256,-0.5568,1.938;3.8116,-1.0137,2.8372;2.6489,-1.0368,1.0514;3.0392,-1.8619,0.4449;1.4433,-1.5514,1.8233;0.7967,-2.7206,1.4032;1.1734,-3.2538,0.5327;-0.3137,-3.2148,2.09;-0.798,-4.1262,1.7491;-0.7916,-2.5467,3.2179;-1.6525,-2.9311,3.7581;-0.1512,-1.3837,3.6523;-0.5148,-0.858,4.5314;0.9571,-0.8919,2.9624;1.4476,0.0141,3.3083)|",4.95791435611
"[H]O[C@](C1NC([H])([H])C([H])([H])C1([H])[H])(C(=O)OC([H])([H])C([H])([H])[H])C([H])([H])/C([H])=C(\[H])C([H])([H])[H] |(5.5861,3.8016,1.4386;5.645,2.8317,1.5617;4.3463,2.3443,1.3403;3.4759,3.4374,0.724;3.963,4.612,0.614;2.9681,5.4911,-0.0215;2.9279,6.4473,0.5107;3.3086,5.7084,-1.0429;1.6238,4.7177,0.0022;1.0136,5.0497,0.8493;1.0327,4.864,-0.9063;2.0653,3.2508,0.2069;2.1015,2.6893,-0.7364;1.4382,2.6801,0.8972;3.6492,1.9284,2.6646;2.645,1.2421,2.6987;4.2597,2.437,3.742;3.6759,2.1206,5.0309;3.9886,2.947,5.6735;2.5871,2.1222,4.9364;4.184,0.7846,5.551;3.795,0.606,6.5602;5.2777,0.7763,5.5961;3.8493,-0.0319,4.9049;4.4344,1.0928,0.4251;5.1182,0.3997,0.9357;3.4567,0.6058,0.3754;4.9612,1.4118,-0.9461;5.8842,1.9905,-0.9735;4.3893,1.0246,-2.0903;3.4699,0.4362,-2.0481;4.9242,1.3143,-3.4643;5.1577,0.3862,-4.004;5.8366,1.9183,-3.4203;4.1882,1.8541,-4.0757)|",6.008273819039999
"[H]OC1=NC(=C([H])[H])[C@]2([H])C(C([H])([H])[H])=C([H])C([H])([H])[C@@]1(C(=O)OC([H])([H])[H])C2([H])[H] |(2.91,4.9519,2.055;2.7307,4.8948,1.0895;3.0465,3.6568,0.6673;3.1036,3.4562,-0.5938;3.3064,2.1702,-1.11;3.5642,2.0152,-2.418;3.7402,1.0339,-2.847;3.6124,2.8775,-3.0745;3.1987,0.9961,-0.1421;3.6544,0.1135,-0.6058;1.7387,0.6914,0.1756;0.9988,-0.169,-0.8127;1.4736,-1.1546,-0.9161;1.01,0.2939,-1.8079;-0.044,-0.3214,-0.5175;1.1549,1.2051,1.2661;0.1106,0.9761,1.4757;1.8362,2.1102,2.2596;1.9513,1.5895,3.2232;1.2181,2.9935,2.4629;3.2447,2.5748,1.7599;3.9668,3.1462,2.9766;3.7213,4.2391,3.4706;4.8624,2.3058,3.5018;5.5343,2.7623,4.695;6.206,1.9509,4.9734;4.8087,2.9574,5.4879;6.094,3.6763,4.4846;3.9639,1.3683,1.1344;5.0028,1.6197,0.8959;3.9772,0.5333,1.8414)|",5.07764445033
"[H]C([H])=C1OC(=O)[C@@]2(C(=O)OC([H])([H])[H])C([H])([H])/C([H])=C(/C([H])([H])[H])[C@]1([H])C2([H])[H] |(1.7322,-2.8003,1.3022;2.7531,-2.7839,0.9391;3.2412,-3.7279,0.7242;3.3866,-1.6246,0.7668;4.6949,-1.6807,0.2908;5.5493,-0.6168,0.2596;6.6344,-0.7664,-0.2428;5.0952,0.7351,0.8563;6.3046,1.2499,1.6519;6.5677,0.8771,2.7744;7.0301,2.1472,0.9668;8.2189,2.6166,1.629;8.8934,1.7805,1.8274;7.9637,3.1042,2.5733;8.6753,3.3252,0.9379;4.6911,1.6604,-0.317;4.6903,2.701,0.0357;5.456,1.6131,-1.0999;3.3373,1.3008,-0.874;3.0608,1.7852,-1.8098;2.4786,0.4464,-0.3027;1.1502,0.0894,-0.9143;1.0013,0.5931,-1.874;0.3192,0.3666,-0.2505;1.0744,-0.9931,-1.0809;2.8331,-0.2436,1.0151;1.9261,-0.3584,1.6192;3.8901,0.5511,1.7921;4.1932,0.0239,2.7011;3.4969,1.5313,2.085)|",6.174263267845
"[H]OC([H])([H])/C([H])=C(/C(=C=C([H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H] |(5.1023,4.509,-3.5163;5.2394,5.4688,-3.4769;5.9192,5.7367,-2.2471;6.9557,5.3733,-2.2839;5.9653,6.8308,-2.1869;5.1679,5.182,-1.0683;4.1244,5.4924,-1.0357;5.616,4.3504,-0.1117;4.6804,3.8804,0.9825;4.1013,2.7124,0.8213;3.5385,1.5453,0.6225;4.0506,0.648,0.9781;2.209,1.3285,-0.0624;2.3247,0.6927,-0.9503;1.7635,2.277,-0.3751;1.5023,0.8187,0.6056;4.4451,4.7572,2.2376;3.56,5.9657,1.8587;3.3888,6.602,2.7362;2.5844,5.633,1.4863;4.0278,6.5817,1.0842;3.7364,3.9465,3.3375;3.5662,4.5764,4.2188;4.3371,3.0836,3.6447;2.7651,3.5732,2.9971;5.791,5.2766,2.7874;5.6194,5.8863,3.6824;6.31,5.9028,2.0534;6.4551,4.4501,3.065;7.0095,3.7651,-0.0521;7.6374,4.0623,-0.8958;6.947,2.6697,-0.037;7.526,4.0605,0.869)|",6.400117763759999
"[H]OC([H])([H])/C([H])=C(/C(=C=C([H])C([H])=C(C([H])([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])[H] |(9.6809,1.6883,0.0582;10.2916,2.336,-0.3277;9.6258,3.6011,-0.2943;9.5418,3.9756,0.7354;10.2982,4.2828,-0.8296;8.2908,3.5394,-0.9849;8.3452,3.1251,-1.9906;7.0855,3.884,-0.4999;5.8456,3.7062,-1.3536;5.1267,2.6284,-1.1512;4.4261,1.5361,-0.9093;4.664,0.6395,-1.4822;3.3473,1.4606,0.0747;3.1527,2.3887,0.6108;2.5873,0.3876,0.3783;1.5088,0.4882,1.4278;1.6835,-0.2249,2.2466;0.523,0.2405,1.0085;1.4507,1.492,1.86;2.72,-0.9702,-0.2628;2.9694,-1.7318,0.4895;3.4843,-1.0105,-1.0421;1.7667,-1.2804,-0.7132;5.464,4.7819,-2.4025;5.5087,6.184,-1.7585;5.2462,6.9469,-2.5013;6.5085,6.4207,-1.3787;4.7955,6.2645,-0.9301;6.465,4.7407,-3.5792;6.1968,5.4982,-4.3262;6.4535,3.7616,-4.0715;7.487,4.9463,-3.2471;4.048,4.5259,-2.948;3.7861,5.293,-3.6863;3.3003,4.5523,-2.148;3.978,3.5481,-3.4359;6.8284,4.4044,0.8967;7.7379,4.4922,1.4961;6.1341,3.7333,1.418;6.3486,5.39,0.87)|",5.167442020995001
"[H]C1=C([H])C([H])=C([C@@]([H])(C([H])(C(=O)C([H])([H])[H])C(=O)C([H])([H])[H])C([H])([H])N(=O)=O)O1 |(6.3667,-6.6203,3.291;5.6753,-5.7918,3.2805;4.9612,-5.1456,4.2404;4.9614,-5.3629,5.2993;4.2154,-4.1239,3.5631;3.5407,-3.4033,4.0041;4.5289,-4.2239,2.2398;4.094,-3.4797,1.0147;3.4424,-2.664,1.3439;5.3004,-2.8104,0.3039;6.049,-3.5597,0.0347;4.8812,-2.0462,-0.9673;3.7518,-1.6143,-1.0915;5.9377,-1.8678,-2.0355;6.8763,-1.4862,-1.6158;5.5734,-1.1859,-2.8063;6.1536,-2.8459,-2.483;5.9381,-1.7848,1.2801;5.2444,-0.9667,1.8469;7.4338,-1.8771,1.4911;7.972,-1.8764,0.5357;7.6673,-2.83,1.9838;7.7765,-1.0492,2.1147;3.2025,-4.3458,0.094;2.7954,-3.7361,-0.7122;2.4054,-4.8171,0.6674;3.9491,-5.468,-0.5738;3.6732,-6.6162,-0.2526;4.7917,-5.1497,-1.4173;5.4298,-5.2431,2.0542)|",4.449061455675
"[H]C1C([H])=C([H])C2=C1[C@]([H])(C1=C([H])C3=C(OC([H])([H])O3)C([H])=C1[H])C([H])([H])C2[H] |(-0.2129,-1.8394,1.0473;0.0142,-0.8916,0.5726;1.3715,-0.4419,0.2077;2.2683,-1.0281,0.383;1.3327,0.7926,-0.3745;2.1705,1.3682,-0.7466;-0.0692,1.174,-0.4098;-0.8479,0.0882,0.2051;-2.311,0.4538,0.2485;-2.591,0.6891,1.285;-3.2972,-0.5872,-0.2624;-4.578,-0.6666,0.3278;-4.8545,-0.0317,1.164;-5.4689,-1.5835,-0.192;-5.1364,-2.4116,-1.2594;-6.2007,-3.2284,-1.5653;-7.2411,-2.8832,-0.6442;-7.504,-3.7627,-0.0436;-8.1145,-2.518,-1.1986;-6.7559,-1.8456,0.2167;-3.8919,-2.3581,-1.8538;-3.633,-3.0101,-2.6812;-2.9749,-1.4271,-1.3335;-1.9814,-1.3701,-1.7669;-2.3417,1.8084,-0.5668;-2.8918,1.6819,-1.5096;-2.8601,2.6069,-0.0182;-0.8951,2.1507,-0.8366;-0.6028,3.071,-1.3369)|",3.89122806215
"[H]C(=O)/C([H])=C(\[H])C([H])([H])OC1=C([H])C([H])=C(OC([H])([H])[H])C([H])=C1[H] |(5.9518,6.4209,6.0979;6.3639,5.3923,5.9749;7.282,5.0007,6.6697;5.7101,4.5905,4.9295;6.0697,3.5791,4.7699;4.7059,5.1043,4.2063;4.3754,6.1262,4.3993;3.956,4.4054,3.1176;4.0528,4.9813,2.181;2.8808,4.3904,3.3656;4.4522,3.0948,2.9543;3.8708,2.2892,2.003;4.3892,0.9963,1.9019;5.2004,0.7071,2.5627;3.877,0.0936,0.9718;4.3018,-0.9022,0.9179;2.8295,0.4802,0.1249;2.2526,-0.3228,-0.8217;2.733,-1.6495,-0.9522;2.1325,-2.1105,-1.7388;2.6084,-2.2198,-0.0215;3.7912,-1.6709,-1.2466;2.3124,1.7753,0.2275;1.5003,2.0638,-0.4324;2.8247,2.6784,1.1573;2.3987,3.674,1.2097)|",4.00551587936
"[H]OC1=NC(=O)N(C([H])([H])OC([H])([H])C([H])([H])O[H])N([H])[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])[H] |(4.1274,-4.4038,1.4824;5.039,-4.4888,1.1604;5.3136,-3.4815,0.3043;6.4821,-3.4537,-0.2121;6.7988,-2.4424,-1.143;7.7788,-2.5191,-1.8588;5.8943,-1.373,-1.2558;6.268,-0.1875,-1.9632;5.3548,0.303,-2.324;6.9093,-0.4774,-2.8009;6.9637,0.6772,-1.0702;7.3243,1.9254,-1.6529;7.8402,1.7626,-2.6118;6.4299,2.5434,-1.8373;8.2686,2.6021,-0.6716;7.734,2.7741,0.2776;8.5783,3.5761,-1.065;9.4439,1.8384,-0.4769;9.1446,0.9282,-0.3132;4.886,-1.1729,-0.273;5.3388,-0.8187,0.5741;4.2292,-2.4477,0.0272;3.6505,-2.2788,0.9465;3.2639,-2.8665,-1.0997;2.5986,-2.0122,-1.2746;3.8391,-3.014,-2.0211;2.4378,-4.1236,-0.7937;3.1033,-4.9835,-0.6421;1.8863,-3.9807,0.1491;1.4409,-4.4525,-1.91;0.8609,-5.3501,-1.6706;1.9592,-4.6326,-2.859;0.7352,-3.6281,-2.0664)|",5.404181070929999
"[H]O/C(=N/C1=C([H])C([H])=C([H])C([H])=C1[H])SC([H])([H])/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])[H] |(8.5856,-3.6986,2.6591;7.9563,-2.9521,2.6951;6.9781,-3.3621,3.5402;7.0563,-4.5116,4.0763;6.1136,-5.0112,4.9955;5.4694,-6.2258,4.7134;5.6797,-6.7219,3.7704;4.5737,-6.7759,5.6279;4.0752,-7.7126,5.3923;4.3219,-6.1364,6.8448;3.628,-6.571,7.559;4.9775,-4.9403,7.1389;4.7976,-4.4385,8.0862;5.8689,-4.3786,6.2251;6.3925,-3.4576,6.463;5.6754,-2.1547,3.7495;6.107,-0.8551,2.4888;6.9787,-0.2988,2.84;6.3696,-1.3696,1.5618;4.9117,0.0321,2.317;4.045,-0.4187,1.833;4.854,1.304,2.7246;5.7256,1.7365,3.2214;3.6662,2.2117,2.5697;3.9534,3.0888,1.969;2.8741,1.696,2.0108;3.1082,2.7062,3.9195;2.8068,1.8376,4.5192;3.9104,3.2039,4.4817;1.9244,3.6641,3.7558;1.5474,3.9998,4.7283;1.0941,3.1825,3.2251;2.2116,4.5547,3.1831)|",5.59466076628
"[H]O/C(=N/C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])SC([H])([H])/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])[H] |(9.9833,1.5685,1.6018;9.0286,1.713,1.7483;8.9528,2.9626,2.2808;10.0032,3.6425,2.4727;9.9315,4.9842,3.0388;9.2556,5.6152,2.4415;9.5088,4.9498,4.0565;11.3304,5.6068,3.0934;11.9856,4.9159,3.6391;11.7288,5.6669,2.0712;11.3795,6.9927,3.7543;10.9736,6.9241,4.7741;12.4318,7.2852,3.8679;10.6384,8.0937,2.9845;10.7737,9.0692,3.4656;9.5607,7.903,2.9296;11.0127,8.1751,1.9562;7.2782,3.4759,2.6459;6.2523,2.0376,2.0598;6.2686,2.0191,0.9677;6.7305,1.1287,2.4318;4.861,2.1949,2.5916;4.742,2.0796,3.6694;3.7931,2.462,1.8329;3.9324,2.5873,0.7566;2.3857,2.617,2.3365;1.7437,1.8598,1.8598;2.353,2.4194,3.4162;1.7924,4.0101,2.0474;2.4156,4.7714,2.534;1.8503,4.2106,0.9685;0.341,4.1457,2.5174;-0.0546,5.1446,2.3019;0.2567,3.98,3.5984;-0.3078,3.4149,2.019)|",6.29671450057
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C(Cl)C([H])=C(Cl)C([H])=C1[H] |(3.3118,-2.1861,3.583;3.0008,-1.5538,2.9183;1.6245,-1.2346,3.1907;0.9933,-2.1197,3.0235;1.5075,-0.9253,4.2381;1.1834,-0.1065,2.2533;0.141,0.1103,2.5189;1.2274,-0.5343,0.7785;0.6231,-1.4347,0.6174;2.2498,-0.7459,0.457;0.8291,0.2592,0.1367;1.9958,1.206,2.4689;1.5874,1.9539,1.7786;3.3577,1.0432,2.106;3.6101,0.1364,2.3654;1.8543,1.7687,3.8858;0.7012,2.4244,4.3402;-0.6758,2.6795,3.2617;0.583,2.9262,5.6362;-0.3226,3.4315,5.9496;1.6553,2.772,6.5106;1.5224,3.3941,8.1487;2.8291,2.1443,6.1005;3.6645,2.046,6.7853;2.9123,1.6585,4.7978;3.833,1.2005,4.4531)|",6.05181203512
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C([H])C([H])=C(Cl)C([H])=C1[H] |(2.8684,-2.8109,0.6492;2.2662,-2.5881,-0.0776;2.6118,-1.2826,-0.5354;3.6766,-1.2162,-0.7898;2.0412,-1.1395,-1.4611;2.2443,-0.1778,0.4727;2.7947,-0.3925,1.4048;0.7403,-0.1981,0.7757;0.4296,-1.2141,1.034;0.1631,0.112,-0.1057;0.4774,0.4714,1.6002;2.7173,1.197,-0.0518;2.2172,1.3753,-1.0118;4.1158,1.1855,-0.3846;4.6133,1.2792,0.4435;2.4034,2.3679,0.8684;1.7817,3.5179,0.3687;1.4868,3.5543,-0.6769;1.5356,4.6242,1.182;1.0507,5.5092,0.7839;1.9237,4.5806,2.5195;1.6198,5.9671,3.5577;2.5485,3.4518,3.0482;2.8412,3.4308,4.0926;2.7808,2.3551,2.2193;3.2574,1.4751,2.6457)|",6.12256163625
"[H]OC([H])([H])[C@]1([H])O[C@@]([H])(C([H])([H])[H])[C@@]2([H])OC3(O[C@@]21[H])C([H])([H])C([H])([H])C([H])([H])C3([H])[H] |(5.9447,0.4895,0.0298;6.4476,-0.3033,0.2859;5.4818,-1.2047,0.7974;5.9622,-2.1852,0.883;5.1508,-0.9068,1.8043;4.2956,-1.2946,-0.1588;4.6543,-1.6732,-1.121;3.7816,0.0596,-0.4224;2.4471,0.2909,0.1529;2.5097,1.2507,0.6759;1.378,0.3732,-0.9269;0.4099,0.6174,-0.4751;1.2718,-0.5671,-1.4756;1.636,1.1592,-1.6435;2.369,-0.8842,1.1192;2.9996,-0.6428,1.9877;1.1951,-1.5365,1.5641;1.661,-2.8883,1.8805;2.9505,-3.0637,1.2381;3.0284,-1.9818,0.3251;2.4134,-2.2101,-0.5572;0.5841,-3.8753,1.4273;0.1933,-3.6299,0.4357;1.036,-4.874,1.3869;-0.4553,-3.7853,2.5552;-1.0732,-2.8928,2.4067;-1.1264,-4.6493,2.585;0.3908,-3.64,3.8518;0.5081,-4.611,4.3451;-0.0958,-2.9759,4.5724;1.7745,-3.0971,3.3995;2.572,-3.8232,3.5865;2.0574,-2.1622,3.8921)|",8.547096044205
"[H]O/C(=N/C1=C([H])C([H])=C([H])C([H])=C1[H])S[C@]([H])(C([H])=C([H])[H])C([H])([H])C([H])([H])C([H])([H])[H] |(3.966,3.3682,2.4475;3.9582,2.9108,1.5842;4.177,3.89,0.6713;4.348,5.085,1.0658;4.5446,6.1744,0.1918;3.5286,6.6243,-0.6663;2.5816,6.0935,-0.6919;3.7322,7.7492,-1.465;2.9372,8.0858,-2.1256;4.944,8.4409,-1.419;5.0987,9.3173,-2.0421;5.9525,8.0016,-0.5581;6.8977,8.5361,-0.5092;5.7546,6.8824,0.2483;6.5295,6.5404,0.9279;4.2531,3.3037,-1.0141;3.8673,1.4746,-0.8645;4.4557,1.102,-0.0225;4.354,0.853,-2.146;3.8114,1.1395,-3.0482;5.3655,-0.0119,-2.2312;5.6674,-0.4429,-3.1817;5.9306,-0.3209,-1.3545;2.3673,1.2319,-0.6168;2.0549,1.8178,0.2541;1.7983,1.6114,-1.4767;2.0325,-0.2483,-0.3793;2.3478,-0.8427,-1.246;2.6191,-0.6173,0.4739;0.5411,-0.4754,-0.1109;0.3254,-1.5363,0.0589;-0.0699,-0.1427,-0.959;0.2074,0.079,0.7747)|",5.646362397875
"[H]O/C(=N\C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])S[C@@]([H])(C([H])=C([H])[H])C([H])([H])C([H])([H])C([H])([H])[H] |(4.2347,0.6704,2.001;4.6636,1.0558,2.7834;5.9624,0.6364,2.8231;6.576,-0.0809,1.9777;5.8717,-0.5413,0.7828;5.0278,-1.1948,1.0621;5.4446,0.3088,0.2199;6.8306,-1.3057,-0.1348;7.6744,-0.6417,-0.3605;7.2459,-2.1526,0.4277;6.1999,-1.8014,-1.4445;5.7754,-0.9476,-1.9927;6.9998,-2.1991,-2.0827;5.1251,-2.8822,-1.2686;4.7645,-3.2394,-2.2398;4.2548,-2.5126,-0.714;5.5221,-3.7468,-0.7224;6.6851,1.2655,4.327;8.4682,0.7733,4.077;8.4141,-0.1238,3.4555;9.207,1.8494,3.3316;9.2951,2.8145,3.8291;9.7422,1.68,2.1212;10.2778,2.4815,1.6189;9.6529,0.7379,1.5864;9.086,0.409,5.4426;8.5285,-0.4423,5.8546;10.1065,0.0519,5.2434;9.1438,1.5275,6.4932;9.7271,2.3732,6.1072;8.1316,1.9096,6.6838;9.7595,1.0502,7.813;9.7967,1.8598,8.5503;10.7838,0.6869,7.664;9.1767,0.2292,8.248)|",6.32936816263
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C([H])C([H])=C([H])C([H])=C1Cl |(2.4227,-1.0524,2.5254;1.9528,-1.5282,1.8228;2.5122,-1.1075,0.5799;3.5988,-1.2528,0.5659;2.0741,-1.7722,-0.1745;2.1723,0.356,0.2377;2.5831,0.9892,1.0405;0.6545,0.5619,0.1744;0.198,0.1739,1.0889;0.2175,0.0211,-0.6737;0.3897,1.6187,0.0722;2.8943,0.7758,-1.0616;2.5698,0.1053,-1.8686;4.2956,0.5955,-0.8146;4.7685,0.8315,-1.6282;2.6164,2.2177,-1.4715;3.2577,3.2517,-0.7699;3.9397,2.9751,0.0285;3.0551,4.5914,-1.0884;3.5655,5.3661,-0.5232;2.199,4.9326,-2.1372;2.0323,5.9739,-2.3982;1.5561,3.9304,-2.8599;0.8944,4.1744,-3.6841;1.7701,2.5919,-2.5239;0.9309,1.3762,-3.4972)|",6.206916929905
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C([H])N=C([H])C([H])=C1[H] |(3.2771,-0.5992,-2.5723;3.5393,-1.4827,-2.2692;2.339,-2.2443,-2.0385;1.6953,-2.212,-2.9291;2.6799,-3.2751,-1.9106;1.5532,-1.7796,-0.8011;0.6846,-2.4486,-0.7266;1.0418,-0.3395,-0.9546;0.4788,-0.2142,-1.8877;1.8692,0.3778,-0.9423;0.3774,-0.0713,-0.1261;2.382,-1.9373,0.5066;1.7654,-1.5546,1.3318;3.5395,-1.1129,0.488;3.915,-1.1885,-0.4119;2.7158,-3.3957,0.8198;1.7026,-4.3359,1.0582;0.656,-4.0301,1.0258;1.913,-5.6245,1.3449;3.1865,-6.036,1.4106;3.3378,-7.0889,1.6431;4.2752,-5.1894,1.2072;5.2894,-5.5708,1.285;4.0338,-3.8479,0.9123;4.8485,-3.1455,0.7706)|",6.239570591965001
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C(N(=O)=O)C([H])=C([H])C([H])=C1[H] |(3.5357,2.1206,0.1906;2.6129,1.8342,0.2861;2.3764,0.8028,-0.6875;1.2939,0.6377,-0.67;2.6378,1.1698,-1.6903;3.1134,-0.5182,-0.4042;4.1904,-0.2974,-0.3297;2.8983,-1.4708,-1.59;3.269,-1.0129,-2.5149;1.8407,-1.7132,-1.7262;3.4377,-2.4116,-1.4478;2.695,-1.0877,0.9871;1.605,-1.1806,1.0066;3.1356,-0.2123,2.0241;2.8423,0.6803,1.7529;3.3302,-2.442,1.282;2.6951,-3.6968,1.2395;1.2759,-3.8542,0.8717;0.6838,-4.8431,1.3026;0.7533,-3.0044,0.1455;3.3616,-4.8831,1.5678;2.8106,-5.8146,1.5336;4.7018,-4.8452,1.9276;5.2248,-5.7663,2.1661;5.3585,-3.6138,1.9889;6.4025,-3.5658,2.2862;4.675,-2.4407,1.6842;5.1693,-1.4808,1.7818)|",4.54430130335
"[H]C1C([H])C(C([H])([H])[H])(C([H])([H])[H])[C@@]2([H])C([H])=C([H])N(C([H])([H])[H])C(=O)[C@]2([H])C1([H])[H] |(1.5005,-4.9318,-4.8606;1.9038,-5.7621,-4.2769;3.2443,-5.8426,-4.1138;3.5858,-6.6666,-3.4757;3.9409,-4.551,-3.737;5.4608,-4.6992,-3.5761;5.9405,-4.7834,-4.5578;5.7113,-5.5989,-3.0015;5.9034,-3.8377,-3.0618;3.6285,-3.336,-4.6219;3.9701,-3.5206,-5.6464;4.1481,-2.4451,-4.2535;2.5617,-3.0996,-4.6624;3.2508,-4.4376,-2.2669;3.7241,-5.2445,-1.6811;3.5147,-3.1377,-1.544;4.3178,-2.4785,-1.8548;2.7696,-2.8087,-0.4815;2.9366,-1.9045,0.0962;1.719,-3.6129,-0.0048;1.2185,-3.3979,1.3495;1.9661,-3.6851,2.0986;0.329,-4.013,1.4797;0.9623,-2.342,1.4919;1.1861,-4.6382,-0.7759;0.3266,-5.3953,-0.3473;1.701,-4.693,-2.2194;1.2329,-3.8277,-2.7173;1.1887,-5.9925,-2.9732;1.5375,-6.8834,-2.44;0.095,-6.0062,-2.9971)|",4.628656597005
"[H]C([H])=C([H])C([H])([H])[C@]1([H])C(=O)N(C([H])([H])[H])C([H])=C([H])[C@]1([H])C(C([H])=C([H])[H])(C([H])([H])[H])C([H])([H])[H] |(1.1193,-0.0301,2.0591;1.3789,0.4316,1.1101;0.595,0.4657,0.3571;2.594,0.9316,0.8836;3.3429,0.8687,1.6768;3.0447,1.6299,-0.3712;3.2101,2.6919,-0.1483;2.2601,1.6131,-1.1317;4.3834,1.0922,-0.9129;5.073,1.0853,-0.0506;5.0305,2.1026,-1.8699;4.5482,3.1989,-2.1265;6.2624,1.7065,-2.366;7.0254,2.6151,-3.2142;6.9692,2.3185,-4.2691;8.0761,2.6193,-2.9038;6.604,3.6136,-3.1043;6.7246,0.3936,-2.1708;7.7765,0.2526,-2.4027;5.9437,-0.6036,-1.735;6.3801,-1.587,-1.598;4.4746,-0.3785,-1.4318;4.1928,-1.0172,-0.5857;3.5501,-0.8633,-2.6365;2.0926,-0.9343,-2.203;1.5672,0.0188,-2.1942;1.3965,-2.0253,-1.8772;0.3501,-1.9543,-1.5923;1.8247,-3.0237,-1.8711;3.6357,0.0875,-3.8507;3.0313,-0.3051,-4.6769;4.6667,0.1821,-4.2081;3.2601,1.0897,-3.6168;4.0363,-2.2605,-3.0792;3.3531,-2.6795,-3.8253;4.0876,-2.963,-2.2384;5.0327,-2.2046,-3.5266)|",5.61098759731
"[H]C([H])=C([H])C([H])([H])[C@]1([H])C(=S)N(C([H])([H])[H])C([H])=C([H])[C@]1([H])C(C([H])=C([H])[H])(C([H])([H])[H])C([H])([H])[H] |(2.7386,4.1858,1.194;2.6783,3.5211,0.3363;1.6783,3.2623,-0.0076;3.773,3.0497,-0.2632;4.7513,3.3326,0.1218;3.7504,2.1364,-1.4598;4.2271,2.6311,-2.3172;2.7111,1.9551,-1.7463;4.443,0.7566,-1.2272;4.0817,0.3557,-0.2798;5.9536,0.9539,-1.0524;6.6699,1.0198,0.4518;6.6558,1.1225,-2.2116;8.0843,1.4415,-2.1715;8.4159,1.6824,-3.1837;8.6602,0.5926,-1.7887;8.2573,2.2911,-1.5079;6.0678,0.8593,-3.4706;6.6429,1.2167,-4.3173;4.9108,0.202,-3.5962;4.5198,0.0175,-4.5904;4.2003,-0.286,-2.3542;4.7374,-1.1917,-2.0203;2.7283,-0.7903,-2.5836;1.8653,0.2177,-3.326;2.3368,0.6672,-4.201;0.5978,0.5448,-3.0655;0.0586,1.2428,-3.7003;0.0477,0.1445,-2.2185;2.8089,-2.0546,-3.484;1.8066,-2.4639,-3.6465;3.4259,-2.8285,-3.0109;3.2391,-1.8383,-4.4679;2.1076,-1.2077,-1.2381;1.1577,-1.7298,-1.3946;1.9141,-0.3497,-0.587;2.7754,-1.8944,-0.7051)|",4.231370375275
"[H]OC([H])([H])[C@]([H])(C([H])([H])[H])[C@]([H])(O[H])C1=C([H])C([H])=C(C#N)C([H])=C1[H] |(3.6562,1.7189,1.5748;3.8511,2.0763,0.6943;2.5987,2.3673,0.0459;1.9653,2.9766,0.7059;2.865,2.9805,-0.8191;1.8386,1.1064,-0.399;0.9233,1.4636,-0.8911;1.4342,0.2294,0.7955;0.8958,0.8125,1.5527;2.3107,-0.2319,1.2623;0.7783,-0.5873,0.4754;2.6457,0.2794,-1.4429;2.0555,-0.6182,-1.6756;3.8651,-0.1967,-0.8963;4.2263,0.5293,-0.349;2.8495,1.0369,-2.7563;1.7474,1.3806,-3.5548;0.7438,1.0986,-3.2442;1.9131,2.0683,-4.7511;1.0546,2.3273,-5.3625;3.2039,2.4265,-5.1776;3.3836,3.1381,-6.4091;3.5273,3.7175,-7.4078;4.3134,2.0749,-4.3932;5.3115,2.341,-4.7268;4.131,1.383,-3.1987;4.9878,1.0854,-2.6049)|",5.640920120865001
"[H]C1=C([H])[C@@]([H])(C#N)C2=C(C1=O)C(=O)C1=C([H])\C([H])=C([H])/C([H])=C\1N2[H] |(4.5581,-1.4254,-0.3945;3.6452,-0.8492,-0.2766;3.6508,0.4831,-0.3133;4.561,1.0572,-0.4624;2.3874,1.2811,-0.0843;2.464,1.7404,0.9175;2.2833,2.4069,-1.0313;2.172,3.3103,-1.7517;1.1132,0.4391,-0.0852;1.1229,-0.9358,-0.0092;2.4185,-1.664,-0.0438;2.5312,-2.8729,0.0936;-0.1642,-1.6749,0.0695;-0.256,-2.8947,0.1329;-1.3863,-0.8226,0.0615;-2.6561,-1.4164,0.1326;-2.6961,-2.4988,0.1984;-3.8044,-0.6387,0.1136;-4.7828,-1.1064,0.1691;-3.7009,0.759,0.0208;-4.5983,1.3708,0.0036;-2.4578,1.3714,-0.0488;-2.3737,2.4532,-0.1211;-1.301,0.5762,-0.0259;-0.0403,1.1611,-0.0865;0.0113,2.1616,-0.2364)|",4.100755727035001
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"[H]OC([H])([H])[C@@]12C([H])=C([H])C(=O)C([H])([H])[C@]1(C([H])([H])[H])C([H])([H])C([H])([H])[C@]2([H])OC([H])([H])OC([H])([H])[H] |(5.5842,-1.8506,1.2982;5.3954,-0.9742,1.664;4.4193,-1.1184,2.6914;4.6372,-1.9987,3.308;4.534,-0.2352,3.3275;2.9579,-1.196,2.1532;2.8232,-2.441,1.3142;3.6587,-2.6732,0.6535;1.7732,-3.2753,1.3457;1.7499,-4.1925,0.7625;0.5712,-2.983,2.157;-0.3506,-3.7809,2.2485;0.535,-1.6077,2.805;-0.2022,-1.6258,3.6147;0.1706,-0.9159,2.0349;1.9148,-1.1319,3.3278;2.2845,-1.9785,4.56;3.2133,-1.645,5.0345;2.3929,-3.0403,4.3103;1.4917,-1.8995,5.313;1.8649,0.3791,3.6751;0.8854,0.6566,4.0801;2.597,0.6046,4.4596;2.2081,1.1555,2.3784;2.9709,1.919,2.5646;1.3337,1.6754,1.9747;2.6984,0.1132,1.3369;3.6278,0.4233,0.8421;1.7124,-0.1404,0.3373;1.6924,0.8061,-0.7183;1.3235,1.7817,-0.3729;2.7217,0.9236,-1.1124;0.8129,0.3903,-1.7025;1.2163,-0.7985,-2.3724;0.4994,-0.9566,-3.1811;1.208,-1.6617,-1.6984;2.2246,-0.6866,-2.8008)|",5.00145257219
"[H]O[C@@]([H])(C([H])([H])[H])C([H])([H])C#CC([H])([H])OC([H])([H])C1=C([H])C([H])=C(OC([H])([H])[H])C([H])=C1[H] |(1.9002,-2.6737,-2.1015;2.1937,-1.9501,-2.6823;2.965,-1.0661,-1.8805;3.39,-0.3439,-2.587;2.0987,-0.3246,-0.8608;2.6794,0.4338,-0.3216;1.6864,-1.0235,-0.1238;1.2676,0.1714,-1.371;4.1521,-1.8191,-1.224;4.785,-2.2276,-2.0222;4.7704,-1.1121,-0.6543;3.7031,-2.9097,-0.3558;3.2741,-3.8182,0.3191;2.7782,-4.9007,1.1665;2.0655,-4.5018,1.9098;3.6137,-5.3424,1.7381;2.1531,-5.8924,0.3649;1.7012,-6.9975,1.1383;2.5349,-7.3648,1.7645;0.8996,-6.6853,1.829;1.2111,-8.09,0.2222;0.0514,-8.8072,0.5119;-0.5393,-8.5434,1.3865;-0.3749,-9.8654,-0.2978;-1.2811,-10.4021,-0.0417;0.3694,-10.2062,-1.4316;0.0523,-11.2148,-2.2967;-1.1022,-11.9922,-2.0269;-1.1526,-12.7393,-2.8212;-2.0152,-11.382,-2.0451;-1.029,-12.4997,-1.0557;1.5311,-9.482,-1.7421;2.0907,-9.7559,-2.6311;1.9451,-8.4429,-0.9218;2.8388,-7.8795,-1.1712)|",5.869495755285
"[H]C1=C(/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])SC(C([H])([H])[H])=N1 |(7.003,-2.8377,3.1213;7.8859,-2.2079,3.1194;8.2779,-1.4246,2.0545;7.6803,-1.2335,0.7504;8.1858,-0.5565,0.0645;6.5495,-1.812,0.2978;5.958,-2.5007,0.8924;6.0653,-1.5056,-1.0625;6.6109,-0.7598,-1.8566;4.9088,-2.1667,-1.3158;4.3145,-1.9497,-2.6144;3.2456,-2.1224,-2.4585;4.4746,-0.9083,-2.9073;4.8819,-2.9047,-3.6607;5.9595,-2.7236,-3.7542;4.7556,-3.9369,-3.3079;4.2047,-2.7315,-5.0271;3.1216,-2.8892,-4.9223;4.3322,-1.6944,-5.3671;4.7571,-3.6874,-6.0898;4.248,-3.5506,-7.0505;4.6257,-4.734,-5.7884;5.8288,-3.5216,-6.2529;9.7516,-0.5909,2.5094;9.695,-1.3746,4.08;10.7328,-1.122,5.13;10.538,-1.7847,5.9765;10.7017,-0.0844,5.4837;11.7439,-1.3145,4.7539;8.6735,-2.1766,4.2358)|",4.247697206305
"[H]C1=C(/C([H])=C(\[H])C(=O)N(C([H])([H])[H])C([H])([H])[H])SC(C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])=N1 |(6.8759,-1.9521,1.6761;6.2451,-1.5597,0.8857;6.6934,-0.7426,-0.1302;8.013,-0.2188,-0.4154;8.1246,0.4203,-1.2888;9.1313,-0.4428,0.3033;9.0984,-1.0541,1.1977;10.4039,0.1992,-0.1317;10.418,0.9958,-1.0729;11.5568,-0.1216,0.5562;12.7782,0.6057,0.241;13.5738,-0.0898,-0.0564;13.1267,1.175,1.114;12.5675,1.2903,-0.5787;11.6542,-1.0663,1.6579;12.6388,-1.5453,1.624;10.9087,-1.8574,1.5751;11.5508,-0.579,2.6389;5.3243,-0.3605,-1.1573;4.2885,-1.3346,-0.1224;2.8291,-1.5219,-0.4166;2.3626,-1.884,0.5071;2.3761,-0.5501,-0.6522;2.525,-2.5107,-1.5732;3.0731,-2.1603,-2.4601;2.9985,-3.9327,-1.2458;2.8254,-4.6036,-2.0951;2.4518,-4.3355,-0.3836;4.0653,-3.9602,-1.0023;1.0254,-2.4858,-1.8999;0.7946,-3.1643,-2.7291;0.6912,-1.4809,-2.1842;0.4304,-2.8049,-1.0346;4.9175,-1.8841,0.8856)|",4.26130289883
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"[H]OC(=O)C([H])([H])[C@@]([H])(O[H])/C(=C(\[H])C1=C([H])C([H])=C([H])C([H])=C1[H])C([H])([H])[H] |(4.6176,-1.6619,-2.3164;5.479,-1.2573,-2.5735;5.7457,-0.2285,-1.7454;6.7381,0.4472,-1.875;4.7401,0.0046,-0.6088;5.0197,-0.6647,0.2177;4.8952,1.0278,-0.2599;3.2698,-0.2238,-0.9874;3.0516,0.3512,-1.8961;3.0772,-1.6024,-1.4231;3.0654,-2.1531,-0.6211;2.221,0.1249,0.0567;2.5599,0.358,1.3407;3.6112,0.2696,1.6079;1.6985,0.7011,2.4874;0.5424,1.4963,2.3863;0.2547,1.9101,1.4261;-0.2171,1.8005,3.5156;-1.1028,2.422,3.4138;0.1603,1.322,4.7712;-0.4332,1.5612,5.6492;1.3166,0.548,4.8928;1.6286,0.1816,5.8671;2.0793,0.2517,3.7661;2.9819,-0.3465,3.8696;0.8116,0.1136,-0.4881;0.0684,-0.0749,0.2902;0.7165,-0.6627,-1.2546;0.5593,1.0697,-0.9683)|",5.25179731465
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"[H]OC([H])([H])[C@@]1([H])S[C@]1([H])[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(6.2211,-2.4769,-3.5634;6.7112,-2.5567,-2.7281;5.7431,-2.6148,-1.6974;5.0847,-3.4917,-1.7998;6.3091,-2.7248,-0.7638;4.8951,-1.3609,-1.6172;5.4683,-0.4489,-1.4555;3.5984,-1.1571,-2.9185;3.5157,-1.4195,-1.0714;3.1757,-2.4322,-0.8393;2.7305,-0.0448,-0.012;0.8776,-0.0017,-0.3854;0.3729,0.7684,0.2101;0.401,-0.9625,-0.1572;0.6958,0.2188,-1.4431;3.5375,1.6163,-0.4122;3.0415,2.4233,0.1401;3.4628,1.844,-1.4809;4.5981,1.6339,-0.1356;3.0153,-0.5004,1.8071;2.5537,0.2408,2.4709;4.0835,-0.5423,2.0505;2.5795,-1.4768,2.0503)|",6.106234805220001
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"[H]O[C@@]([H])(C([H])([H])/C(OC([H])([H])C([H])([H])[H])=C(/[H])C(=O)OC([H])([H])C([H])([H])[H])C([H])(C([H])([H])[H])C([H])([H])[H] |(4.1761,-0.3513,-2.8108;4.8871,-1.01,-2.7552;6.0275,-0.3454,-2.2179;6.5057,0.2666,-3.0037;5.6441,0.6081,-1.0576;5.0041,0.0616,-0.3545;6.5439,0.9387,-0.5385;4.8927,1.8212,-1.5296;3.6758,1.4534,-2.0238;2.7751,2.442,-2.551;2.554,3.1742,-1.7643;3.2605,2.9682,-3.3828;1.5193,1.7209,-3.0052;0.7983,2.4434,-3.402;1.0534,1.19,-2.1693;1.7462,0.996,-3.794;5.3175,3.1078,-1.5136;4.6851,3.901,-1.8902;6.6168,3.5615,-1.002;7.5227,2.8899,-0.5343;6.6933,4.9173,-1.1186;7.9186,5.5321,-0.6651;7.6286,6.5522,-0.3992;8.2671,5.0101,0.2295;8.9827,5.5247,-1.7533;9.8726,6.0642,-1.408;8.6148,6.0135,-2.6615;9.2757,4.4998,-1.9971;7.0469,-1.4216,-1.7899;7.9236,-0.8781,-1.4082;6.5135,-2.333,-0.6754;7.2485,-3.1074,-0.4264;5.5903,-2.8286,-0.9932;6.3019,-1.7772,0.2451;7.489,-2.2439,-3.0099;8.2518,-2.9794,-2.7282;7.9144,-1.6003,-3.7894;6.638,-2.7792,-3.4428)|",5.845005508739999
"[H]ONC([H])C1=C(\C([H])([H])[H])C2=C(OC(C([H])([H])[H])(C([H])([H])[H])C2([H])[H])/C(C([H])([H])[H])=C\1C([H])([H])[H] |(2.1834,-5.2469,1.151;1.7396,-4.3985,0.9972;2.8306,-3.5333,0.7569;2.3954,-2.3419,0.5372;1.317,-2.1935,0.5762;3.2356,-1.1774,0.2451;4.6413,-1.2828,0.0284;5.3882,-2.593,0.0719;6.4376,-2.4494,-0.2009;4.9471,-3.3289,-0.608;5.3448,-3.049,1.0665;5.3389,-0.1121,-0.2441;4.684,1.1158,-0.3112;5.515,2.1583,-0.5974;6.9054,1.6771,-0.5689;7.5329,2.219,0.717;8.5865,1.9241,0.7858;7.0077,1.8264,1.5944;7.4752,3.3121,0.7409;7.5885,2.2262,-1.817;8.6345,1.9009,-1.8542;7.5677,3.321,-1.8206;7.0825,1.8688,-2.7197;6.8003,0.1225,-0.5528;7.0831,-0.3039,-1.5251;7.4774,-0.3082,0.1938;3.3109,1.2645,-0.114;2.6939,2.6429,-0.2053;1.9385,2.7042,-0.9986;3.4653,3.3838,-0.4252;2.2062,2.9389,0.7317;2.586,0.0904,0.1681;1.0894,0.2009,0.3997;0.751,1.2359,0.3436;0.7978,-0.1802,1.3854;0.5173,-0.3637,-0.347)|",4.57695496541
"[H]C1=C(C([H])([H])C(=O)OC([H])([H])[H])[C@]([H])(C#N)C(=O)N=C1OC([H])([H])[H] |(3.9463,1.2255,2.5701;3.8244,0.1616,2.3957;4.4008,-0.7677,3.171;5.268,-0.4196,4.3482;5.4704,0.6555,4.3802;6.2412,-0.9228,4.2691;4.6573,-0.8411,5.6836;3.6689,-1.5291,5.8226;5.3835,-0.3481,6.6993;4.9348,-0.711,8.0203;4.9464,-1.7968,8.1404;3.9214,-0.3405,8.1933;5.6399,-0.2394,8.7044;4.1061,-2.2294,2.874;3.3669,-2.545,3.6293;5.2882,-3.0766,3.0605;6.2533,-3.6949,3.2422;3.4438,-2.505,1.4861;3.4735,-3.6251,1.0268;2.7803,-1.459,0.8524;2.9761,-0.2512,1.2767;2.3835,0.783,0.6789;1.5274,0.4925,-0.4434;1.1455,1.4614,-0.765;0.7134,-0.1695,-0.1402;2.0993,0.011,-1.2399)|",4.85995336993
"[H]C(=O)C(=O)N([H])[C@]([H])(C(=O)OC([H])([H])[H])C([H])([H])[H] |(4.0514,0.6162,4.1248;3.6976,1.0981,3.1923;3.5454,2.2932,3.0754;3.4249,0.0959,2.0671;3.6072,-1.1041,2.2509;2.9882,0.6708,0.927;2.9259,1.6836,0.8774;2.7262,-0.0898,-0.2809;3.5412,-0.8089,-0.4274;2.732,0.8932,-1.4467;2.7429,2.1005,-1.3256;2.7008,0.2495,-2.6236;2.6685,1.0916,-3.7925;2.6243,0.4086,-4.6404;3.5689,1.7091,-3.8401;1.7894,1.7402,-3.7707;1.3988,-0.8702,-0.2121;1.2423,-1.4356,-1.1348;0.5569,-0.1859,-0.0647;1.4413,-1.5664,0.6288)|",4.76743466076
"[H]O[C@@]([H])(C([H])([H])C#CC(=C([H])[H])C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(6.3189,3.6865,-3.7488;6.6991,2.8519,-3.426;5.8681,2.4029,-2.362;5.9311,3.1077,-1.5149;4.3878,2.3804,-2.8271;4.27,1.6408,-3.6282;3.7366,2.0694,-2.0006;3.969,3.6979,-3.3086;3.6708,4.8034,-3.714;3.3142,6.1226,-4.1456;2.3857,6.3138,-5.0981;2.1103,7.3155,-5.4157;1.8814,5.4815,-5.5782;4.0288,7.2696,-3.4628;3.6989,8.2321,-3.8651;5.1145,7.192,-3.5986;3.8403,7.2604,-2.3826;6.4245,1.0393,-1.8718;5.6056,0.537,-0.6665;6.0704,-0.3636,-0.2494;4.5772,0.2743,-0.9396;5.5628,1.288,0.1327;6.3997,-0.0147,-2.996;6.8863,-0.9375,-2.6586;6.9295,0.3483,-3.8815;5.3773,-0.2773,-3.2925;7.8838,1.2583,-1.4204;8.3094,0.322,-1.0402;7.9419,2.0036,-0.6168;8.5027,1.609,-2.25)|",5.815072985185
"[H]C1=C(C(=O)/C([H])=C(\[H])C2=C([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H])C([H])([H])C([H])([H])C1([H])[H] |(-2.8169,-5.4583,-0.4568;-2.9029,-5.4617,0.6261;-2.1326,-4.7554,1.4718;-1.0191,-3.8292,1.146;-0.4353,-3.2499,2.0646;-0.6377,-3.6258,-0.2716;-1.1849,-4.1648,-1.0381;0.367,-2.7838,-0.5952;0.8591,-2.2774,0.2346;0.8517,-2.4695,-1.9312;1.8402,-1.5574,-2.0691;2.2632,-1.1103,-1.1697;2.4082,-1.1009,-3.384;2.5345,-0.0093,-3.3687;3.4273,-1.5074,-3.493;1.5408,-1.5282,-4.5773;2.0967,-1.3997,-5.5136;0.6597,-0.8747,-4.6393;1.0807,-2.9809,-4.4119;1.9629,-3.6337,-4.3536;0.5048,-3.3073,-5.2861;0.2366,-3.1517,-3.1395;0.1011,-4.2198,-2.9266;-0.7748,-2.7496,-3.3041;-2.5242,-4.9897,2.9157;-1.6728,-5.3735,3.4904;-2.8031,-4.0453,3.3981;-3.7052,-5.9995,2.8421;-3.4704,-6.9205,3.3842;-4.6069,-5.5861,3.3039;-3.9416,-6.2925,1.33;-4.9549,-6.0194,1.0019;-3.8282,-7.3584,1.0851)|",4.318446807435
"[H]OC([H])([H])[C@@]1([H])N([H])C(C2=C([H])C([H])=C([H])C([H])=C2[H])=C([H])C(=O)C1([H])[H] |(4.0235,-2.5674,1.4218;4.7403,-2.7272,0.7922;5.4293,-1.4935,0.5834;5.2771,-0.8035,1.4233;6.5003,-1.7218,0.5289;4.9945,-0.839,-0.7434;5.3057,-1.5105,-1.552;3.5336,-0.7318,-0.8171;3.0719,-1.6293,-0.7271;2.8949,0.316,-0.1875;1.4359,0.1455,0.0286;0.6504,-0.5593,-0.8995;1.1103,-0.9541,-1.8009;-0.719,-0.7209,-0.6919;-1.3146,-1.2568,-1.4258;-1.3244,-0.1875,0.4471;-2.3913,-0.3145,0.6086;-0.552,0.5115,1.3774;-1.0144,0.9247,2.2696;0.8158,0.6775,1.171;1.4168,1.2038,1.9061;3.5634,1.453,0.1844;3.0257,2.2849,0.6237;4.9787,1.6363,-0.0866;5.6168,2.6244,0.2641;5.6306,0.5404,-0.9393;5.5202,0.8462,-1.989;6.7036,0.5114,-0.7249)|",4.3973598240800005
"[H]C([H])([H])C#CC(=O)[C@@]1([H])OC(C([H])([H])[H])(C([H])([H])[H])OC1([H])[H] |(3.4819,-0.7834,-4.4429;2.8767,-0.89,-3.5345;2.039,-0.1871,-3.6122;2.4709,-1.9077,-3.5154;3.6784,-0.6124,-2.3505;4.3416,-0.382,-1.3614;5.1375,-0.0611,-0.1986;5.5565,1.0653,0.0191;5.4181,-1.1885,0.8135;4.7851,-0.9899,1.6885;5.1278,-2.4841,0.3175;6.3708,-3.1613,0.0185;6.5118,-4.3486,0.9731;7.4431,-4.8874,0.7723;5.6709,-5.0374,0.8453;6.5199,-4.0071,2.0126;6.4004,-3.5669,-1.4484;7.357,-4.0415,-1.6877;6.2759,-2.6816,-2.0766;5.5933,-4.2741,-1.6617;7.4014,-2.2014,0.2359;6.9061,-1.2634,1.1822;7.0419,-1.617,2.2142;7.426,-0.3141,1.0466)|",5.07764445033
"[H]OC([H])([H])[C@@]([H])(O[H])C([H])([H])C([H])([H])O[C@]1([H])OC([H])([H])C([H])([H])C([H])([H])C1([H])[H] |(-4.501,-3.5844,2.3722;-5.2669,-4.0231,1.9704;-5.1448,-3.9116,0.5484;-4.1395,-4.1992,0.2098;-5.8628,-4.6277,0.139;-5.5282,-2.4993,0.0819;-5.6205,-2.5085,-1.0128;-6.821,-2.1933,0.5899;-6.8191,-2.5387,1.5009;-4.5208,-1.4097,0.4909;-4.9799,-0.4368,0.2869;-4.3517,-1.4463,1.5765;-3.1776,-1.4878,-0.2251;-3.3156,-1.4069,-1.3168;-2.6614,-2.4356,-0.0281;-2.3751,-0.4027,0.2393;-1.1306,-0.3,-0.3743;-1.2469,-0.2883,-1.4787;-0.357,-1.4461,-0.0261;0.9319,-1.4373,-0.6337;1.4051,-2.3816,-0.3482;0.829,-1.4288,-1.7336;1.7503,-0.229,-0.1741;1.9382,-0.3219,0.9034;2.7247,-0.2252,-0.679;0.9778,1.0683,-0.4586;0.9345,1.2334,-1.5452;1.4996,1.9332,-0.0329;-0.4519,0.9806,0.1009;-1.053,1.8443,-0.2052;-0.4375,0.9561,1.1973)|",8.236886254635
"[H]OC([H])([H])[C@@]([H])(O[H])C([H])([H])C([H])([H])O[Si](C([H])([H])[H])(C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(7.4354,-3.2677,3.6236;6.8021,-3.3423,2.8951;5.9402,-2.2007,2.9231;5.6016,-1.9875,3.9476;6.4501,-1.309,2.5278;4.7329,-2.5597,2.0604;5.0966,-2.7115,1.0279;4.1471,-3.7614,2.5485;4.897,-4.3585,2.7164;3.6684,-1.4676,2.0649;3.3138,-1.3088,3.0908;4.1107,-0.5247,1.7178;2.473,-1.8035,1.1766;1.9911,-2.7197,1.5404;2.8165,-1.9928,0.1476;1.5628,-0.709,1.2019;0.0964,-0.6888,0.3683;-1.0509,-2.0372,1.0398;-2.0253,-2.0176,0.5368;-1.225,-1.9192,2.1151;-0.6233,-3.0342,0.8808;0.3915,-1.0007,-1.4764;-0.5429,-0.913,-2.044;0.7823,-2.0101,-1.6521;1.1085,-0.2879,-1.8989;-0.6065,1.0621,0.6892;-0.7633,1.2959,2.2066;-1.1534,2.3067,2.3975;0.1922,1.2015,2.7342;-1.4668,0.5864,2.6601;-1.9847,1.2075,0.0103;-2.3983,2.2107,0.1898;-2.7133,0.4845,0.3977;-1.9256,1.0727,-1.0771;0.3566,2.1241,0.1176;-0.0358,3.1354,0.3009;0.4862,2.0179,-0.9666;1.3471,2.0615,0.5817)|",8.476346443075
"[H]C1=C2C(=O)C([H])([H])[C@@]([H])(C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])[C@]2([H])C([H])([H])C([H])([H])C1([H])[H] |(10.6535,1.6883,8.4825;10.381,1.2388,7.5279;9.3144,1.7123,6.8703;8.3876,2.7599,7.3788;8.5596,3.4796,8.3456;7.152,2.74,6.4697;6.2393,2.8898,7.0534;7.2276,3.5867,5.7694;7.2291,1.3944,5.7071;6.803,0.6131,6.3555;6.4946,1.3665,4.3603;6.9076,2.1581,3.7155;6.7197,0.4161,3.8585;4.9727,1.5452,4.4669;4.555,0.7411,5.0887;4.7582,2.479,5.0035;4.2462,1.5955,3.1104;3.1921,1.8526,3.2877;4.6652,2.4236,2.5209;4.3024,0.3101,2.2645;3.8376,0.5183,1.2901;5.3477,0.0504,2.0442;3.6014,-0.902,2.8931;4.0703,-1.1498,3.8549;2.5596,-0.6357,3.1232;3.6252,-2.1406,1.991;3.1106,-2.9874,2.4592;4.6544,-2.4542,1.7758;3.1345,-1.942,1.0301;8.7575,1.1256,5.5933;9.1325,1.7123,4.7363;9.223,-0.3252,5.4189;8.7162,-0.9612,6.159;8.9555,-0.7107,4.4274;10.7478,-0.4061,5.6242;11.104,-1.4287,5.4522;11.2373,0.2247,4.8699;11.1757,0.0619,7.0304;12.2486,0.3015,7.0415;11.0595,-0.7583,7.7572)|",5.036827372755001
"[H]C([H])=C1C(=O)C([H])([H])[C@@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])[C@@]1([H])C([H])([H])[H] |(1.7371,-2.651,-0.3354;2.4197,-2.2423,-1.0754;2.6981,-2.8794,-1.9112;2.9165,-1.0046,-0.999;3.8351,-0.4562,-2.0528;4.2653,-1.0592,-3.0174;4.119,1.0053,-1.7014;5.0977,1.0398,-1.2012;4.189,1.6266,-2.598;2.9989,1.415,-0.715;3.3755,2.1411,0.013;1.7889,2.0401,-1.3922;1.2405,1.5326,-2.5812;1.6891,0.6724,-3.0698;0.1187,2.1246,-3.1635;-0.2851,1.7144,-4.0854;-0.4786,3.2391,-2.5725;-1.3496,3.7009,-3.0295;0.0576,3.7583,-1.3939;-0.392,4.6304,-0.9262;1.1783,3.1623,-0.8142;1.5923,3.5786,0.1018;2.6902,0.0682,0.0464;3.4958,-0.0345,0.7925;1.3568,0.0079,0.7902;1.2709,-0.9252,1.3585;0.5121,0.0652,0.0971;1.2687,0.8373,1.501)|",4.99056801817
"[H]OC1=N[C@]([H])(C([H])([H])[C@]([H])(C#CC2=C([H])C([H])=C([H])C([H])=C2[H])O[H])C([H])([H])O1 |(7.4531,3.0473,3.1994;7.5813,3.8164,2.618;6.8975,3.57,1.5013;6.2225,2.5201,1.2319;5.641,2.741,-0.1091;4.5514,2.8163,-0.0044;5.9542,1.5857,-1.0669;7.0402,1.5085,-1.204;5.5054,1.8033,-2.0455;5.4502,0.2105,-0.5694;5.6915,-0.5296,-1.3435;3.9846,0.2025,-0.3792;2.7807,0.1902,-0.225;1.3635,0.1291,-0.0458;0.6905,-1.1047,-0.1395;1.2635,-2.0033,-0.3465;-0.6891,-1.1707,0.0363;-1.1945,-2.1298,-0.0378;-1.4213,-0.0119,0.3067;-2.4976,-0.0667,0.445;-0.7641,1.2171,0.4003;-1.3285,2.1215,0.6111;0.6158,1.2909,0.2259;1.1283,2.2452,0.301;6.1515,-0.2196,0.5884;6.1575,0.5446,1.2001;6.2477,4.1052,-0.5546;5.5023,4.8591,-0.8174;6.9688,4.0062,-1.3729;6.9657,4.5792,0.6149)|",5.3497583008300005
"[H]OC1=N[C@]([H])(C([H])([H])C(=O)C#C[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])O1 |(2.7026,-1.3328,9.2502;2.8891,-2.2626,9.0346;3.5094,-2.2479,7.8516;3.7717,-1.2151,7.149;4.4795,-1.7184,5.9623;5.5134,-1.3515,5.9722;3.8116,-1.2201,4.6781;3.744,-0.1247,4.6893;2.7724,-1.5769,4.6207;4.5438,-1.6652,3.4238;5.4926,-2.4366,3.4585;4.0449,-1.1301,2.1695;3.6063,-0.6833,1.1216;2.952,-0.0176,-0.4808;1.0655,-0.0394,-0.3862;0.6263,0.3212,-1.3243;0.699,0.6005,0.4241;0.6889,-1.0532,-0.2095;3.5688,-1.124,-1.8802;3.2132,-0.7548,-2.85;3.2126,-2.1534,-1.7622;4.6635,-1.1508,-1.911;3.5984,1.7461,-0.6809;3.2061,2.1915,-1.6033;4.6923,1.7674,-0.737;3.2915,2.3828,0.1561;4.4599,-3.2692,6.1248;5.4514,-3.7229,6.122;3.8328,-3.7765,5.3853;3.8624,-3.4781,7.4332)|",4.70756961365
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"[H]OC([H])([H])[C@@]1([H])N([H])C(C([H])([H])C([H])([H])C([H])([H])[H])=C([H])C(=O)C1([H])[H] |(6.5859,-2.4628,1.5355;6.5923,-2.3601,0.5726;7.2319,-1.1223,0.2593;6.7572,-0.2832,0.7878;8.2984,-1.1416,0.5316;7.1023,-0.9295,-1.2567;7.6066,-1.7777,-1.7343;5.6937,-0.9833,-1.6611;5.2492,-1.8616,-1.4211;4.9056,0.1326,-1.5043;3.4204,-0.1293,-1.445;2.8821,0.7878,-1.7065;3.156,-0.8813,-2.2022;2.9585,-0.6185,-0.0544;3.5493,-1.4959,0.2418;3.1822,0.1629,0.6828;1.4662,-0.9593,-0.0178;1.1619,-1.2944,0.9799;1.2226,-1.7592,-0.7277;0.8538,-0.0875,-0.2778;5.4354,1.3878,-1.3857;4.7821,2.2476,-1.2832;6.8669,1.6217,-1.4269;7.3907,2.723,-1.2849;7.7226,0.385,-1.7423;7.8353,0.3439,-2.8345;8.7222,0.5307,-1.3215)|",5.0395485112600005
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"[H]OC(/C([H])=C(\[H])C([H])([H])C([H])([H])C([H])([H])[H])(C([H])([H])OC([H])([H])[H])C([H])([H])OC([H])([H])[H] |(7.2537,3.0892,2.8584;7.2792,3.2715,1.9024;6.866,2.0538,1.2883;5.3629,1.8522,1.3611;5.0049,0.8967,0.9789;4.4979,2.7593,1.8183;4.8925,3.7098,2.1742;3.0072,2.5621,1.8867;2.5069,3.3605,1.3159;2.7421,1.6157,1.398;2.436,2.563,3.3222;1.3682,2.3107,3.2678;2.9177,1.7602,3.8959;2.5986,3.8939,4.0645;2.1384,3.849,5.0583;2.1216,4.715,3.5143;3.6537,4.1557,4.2021;7.605,0.8889,1.972;8.6871,0.9811,1.7822;7.2663,-0.0796,1.5787;7.3439,0.9959,3.3636;8.0658,0.0621,4.1391;7.8039,0.2396,5.1855;9.1536,0.184,4.0143;7.8003,-0.9731,3.8743;7.3271,2.1769,-0.1732;8.4129,2.3679,-0.1955;6.8211,3.0419,-0.6294;7.0117,0.9791,-0.8572;7.3665,1.0204,-2.2228;7.0874,0.0575,-2.6588;8.4493,1.1751,-2.3577;6.8341,1.824,-2.756)|",7.145709714130001
"[H]C1=C([H])C([H])=C(N([H])C([H])([H])/C([H])=C(\[H])N2C(=O)OC([H])([H])C2([H])[H])C([H])=C1[H] |(6.0233,4.6605,1.5945;5.8149,3.6459,1.2682;6.6124,3.0326,0.297;7.4494,3.5711,-0.1408;6.3489,1.7336,-0.1208;6.981,1.2647,-0.8726;5.2716,1.004,0.4219;5.0701,-0.3135,0.0054;5.4429,-0.5079,-0.9161;3.8208,-1.0223,0.2658;3.6698,-1.0478,1.3534;2.9441,-0.505,-0.1622;3.9,-2.4272,-0.2591;4.6724,-3.0608,0.1726;3.0794,-2.8872,-1.2134;2.3051,-2.2717,-1.6622;3.0968,-4.1747,-1.7397;2.1092,-4.6231,-2.61;1.2058,-3.9794,-3.0808;2.3181,-5.9484,-2.8585;3.5547,-6.3747,-2.2622;3.39,-7.3421,-1.7843;4.3014,-6.4862,-3.0551;3.9354,-5.2598,-1.2672;4.9978,-4.9996,-1.3218;3.6958,-5.5139,-0.2253;4.4654,1.6288,1.3906;3.6169,1.1056,1.8187;4.7454,2.932,1.8062;4.1079,3.3919,2.5574)|",5.249076176145
"[H]/C(OC([H])([H])C([H])([H])[H])=C(\C(=O)C(=O)OC([H])([H])[H])C([H])([H])C([H])([H])[H] |(2.6721,3.3123,0.1226;2.2394,2.3131,0.1053;0.9065,2.2216,0.2227;0.1978,3.4581,0.4357;0.5345,3.9043,1.3803;0.4385,4.1534,-0.379;-1.2854,3.1444,0.4725;-1.8573,4.064,0.6355;-1.5128,2.4458,1.2836;-1.6096,2.6962,-0.4718;3.038,1.2278,-0.028;4.4709,1.5276,-0.1811;4.9577,2.6511,-0.2498;5.4575,0.3462,-0.1708;5.5608,-0.4364,0.7501;6.2324,0.3464,-1.2658;7.2651,-0.6567,-1.2907;7.756,-0.5439,-2.2572;6.8331,-1.6556,-1.189;7.975,-0.4896,-0.4765;2.4981,-0.1854,-0.0747;3.1185,-0.8322,0.5534;1.4928,-0.1886,0.3581;2.4376,-0.76,-1.5025;2.0636,-1.7901,-1.4911;3.4267,-0.7686,-1.9759;1.7734,-0.1624,-2.1368)|",4.78376149179
"[H]/C(OC([H])([H])C([H])([H])[H])=C(\C(=O)C(=O)OC([H])([H])[H])C([H])([H])[H] |(4.7868,-0.8034,-0.4723;4.1145,-1.422,-1.0645;2.8825,-0.9432,-1.2911;2.5775,0.3367,-0.7042;2.6901,0.2669,0.3856;3.2931,1.0807,-1.0774;1.156,0.6991,-1.0886;0.8866,1.6661,-0.6509;0.4525,-0.0554,-0.7231;1.055,0.7694,-2.176;4.5346,-2.6184,-1.5402;5.9153,-2.9748,-1.171;6.6515,-2.2994,-0.461;6.5019,-4.2625,-1.783;6.3589,-4.5868,-2.9434;7.2424,-4.9329,-0.8895;7.9102,-6.1008,-1.3984;8.4394,-6.5272,-0.5463;8.6123,-5.8233,-2.1889;7.1851,-6.8138,-1.8002;3.6438,-3.5186,-2.3618;2.6118,-3.1619,-2.3213;3.6624,-4.5503,-1.9928;3.9621,-3.5535,-3.4083)|",4.61505090448
"[H]C([H])([H])OC(=O)[C@@]12C(=O)[C@]3([H])C([H])([H])[C@@]4([H])C([H])([H])[C@]3([H])[C@]1([H])[C@]42[H] |(4.9664,0.5937,-1.4021;4.7763,0.1807,-0.4114;4.3307,-0.8147,-0.4876;5.7034,0.1119,0.164;3.8611,1.0971,0.2115;3.4826,0.7757,1.4572;3.8783,-0.2077,2.0561;2.5398,1.7668,2.0564;1.7495,2.7946,1.293;1.776,3.0289,0.1051;0.8666,3.4658,2.3359;0.0352,4.0116,1.8885;1.8698,4.3563,3.1453;2.7049,4.7056,2.5281;1.3776,5.2354,3.5756;2.2826,3.3571,4.2378;2.9685,3.7523,4.9909;0.8809,2.9081,4.7289;0.2563,3.7407,5.0732;0.9179,2.14,5.509;0.4747,2.3305,3.3538;-0.542,1.9505,3.2401;1.5962,1.2763,3.1602;1.4601,0.2131,3.3278;2.7944,2.0465,3.5785;3.6731,1.5411,3.9646)|",6.15521529831
"[H]O[C@@]1([H])C(C(=O)C2=C([H])C([H])=C(F)C([H])=C2[H])=C([H])C([H])([H])C([H])([H])C1([H])[H] |(1.9097,0.9696,1.7642;2.4155,0.6589,0.9974;1.5109,0.6338,-0.129;1.9743,-0.0647,-0.8325;0.1345,0.1331,0.2777;-0.2334,-1.3327,0.3932;-1.4095,-1.6384,0.5737;0.7647,-2.4426,0.2224;2.1228,-2.3649,0.5741;2.5305,-1.4456,0.9808;2.956,-3.4748,0.4348;4.0041,-3.4339,0.7131;2.4252,-4.6561,-0.0676;3.2313,-5.7258,-0.2145;1.0832,-4.7715,-0.4204;0.7078,-5.7146,-0.8041;0.259,-3.6649,-0.2598;-0.7961,-3.7269,-0.5032;-0.8651,1.0132,0.4939;-1.8344,0.6039,0.7709;-0.7706,2.501,0.322;-1.3983,2.7697,-0.5438;-1.2434,3.0015,1.1785;0.6644,3.0018,0.1093;1.1769,3.0851,1.0776;0.6587,4.0081,-0.3236;1.4401,2.024,-0.7785;2.4631,2.3761,-0.9488;0.9523,1.9381,-1.7596)|",4.783761491790001
"[H]C(=O)C([H])([H])C([H])([H])C([H])([H])/C([H])=C(\[H])C(=O)C1=C([H])C([H])=C(F)C([H])=C1[H] |(4.131,-3.7587,-2.3556;3.8319,-3.1502,-1.4708;4.6807,-2.7045,-0.7267;2.3449,-2.9295,-1.3241;1.8516,-3.9117,-1.3478;2.1532,-2.4661,-0.3526;1.7734,-2.0648,-2.4713;1.9588,-2.5584,-3.4343;0.6844,-2.0156,-2.3511;2.358,-0.6317,-2.5317;1.919,-0.1239,-3.4008;3.4397,-0.686,-2.7061;2.0873,0.1663,-1.2888;1.0773,0.5487,-1.1396;2.9928,0.3862,-0.3248;4.0005,-0.0034,-0.4342;2.6443,1.1265,0.9183;1.641,1.8328,0.9761;3.5448,0.9837,2.1062;3.3853,1.8978,3.1612;2.6306,2.6708,3.0613;4.1725,1.8172,4.303;4.0704,2.5212,5.1225;5.1163,0.7963,4.3874;5.8826,0.7084,5.492;5.2911,-0.1379,3.3728;6.0287,-0.9248,3.4889;4.5032,-0.0378,2.2271;4.623,-0.783,1.4464)|",4.84906881591
"[H]OC([H])([H])[C@@]1([H])N2C(=O)C([H])([H])C([H])([H])[C@]2(C([H])([H])C([H])([H])C([H])([H])[H])C([H])([H])C([H])([H])C1([H])[H] |(8.14,-1.3528,0.7633;7.8,-2.2249,0.4701;6.5144,-1.9689,-0.0326;6.5308,-1.3764,-0.9651;6.0836,-2.9467,-0.2797;5.5608,-1.2645,0.969;5.8795,-1.5787,1.9733;5.6412,0.2167,0.9108;6.8094,0.8871,1.1288;7.934,0.39,1.2039;6.5099,2.3728,1.2703;7.1166,2.7916,2.0774;6.8064,2.881,0.3455;4.9991,2.4251,1.527;4.5099,3.2899,1.0701;4.8077,2.4763,2.6045;4.4309,1.085,0.9682;3.7682,1.2962,-0.4207;3.3267,0.3548,-0.7663;2.9228,1.9829,-0.2657;4.6637,1.8465,-1.5387;5.048,2.8367,-1.262;5.5386,1.1953,-1.6607;3.9214,1.9571,-2.8754;4.5757,2.352,-3.6604;3.0542,2.6247,-2.7973;3.5557,0.978,-3.2084;3.4391,0.44,1.9531;3.9206,0.4319,2.9388;2.5491,1.0742,2.0469;3.0416,-1.0033,1.5668;2.8511,-1.5681,2.4864;2.0974,-1.0049,1.0109;4.1156,-1.7262,0.7242;3.897,-1.6117,-0.3448;4.0783,-2.8029,0.92)|",6.843663340075
"[H]OC([H])([H])C([H])([H])C([H])([H])[C@@]1([H])OC(=O)[C@@]([H])(C([H])([H])[H])[C@]([H])(O[H])[C@]1([H])C([H])([H])[H] |(7.6985,1.0275,0.5704;6.7845,1.1877,0.2919;6.6581,0.7748,-1.0674;7.3226,1.3614,-1.7215;6.9341,-0.2869,-1.184;5.2113,0.9818,-1.4955;4.9685,2.0482,-1.3934;5.1167,0.7325,-2.5584;4.2211,0.1479,-0.6723;4.3329,0.4009,0.3884;4.4744,-0.9169,-0.7707;2.76,0.3787,-1.0749;2.5596,1.4555,-1.0356;2.5984,0.071,-2.4886;2.2815,-1.1701,-2.9425;2.4866,-1.4328,-4.1041;1.5663,-2.1466,-1.9986;0.4976,-1.9698,-2.2085;1.886,-3.5996,-2.3712;1.3151,-4.2802,-1.7338;2.952,-3.8109,-2.2277;1.6449,-3.7904,-3.4187;1.7832,-1.8585,-0.5024;2.7734,-2.2287,-0.2154;0.8949,-2.6195,0.3118;-0.0127,-2.4088,0.0401;1.7227,-0.3488,-0.2042;2.0212,-0.2124,0.8429;0.3201,0.2574,-0.3757;0.3199,1.3063,-0.0601;-0.4172,-0.2635,0.2455;-0.0213,0.2317,-1.4164)|",7.243670700310001
"[H]OC1=N[C@]([H])(C([H])([H])[H])[C@]([H])(OC([H])([H])C2=C([H])C([H])=C([H])C([H])=C2[H])C1([H])[H] |(6.5857,-2.1302,-0.4977;5.8697,-2.3937,0.0997;4.8498,-1.5083,-0.0193;3.758,-1.6698,0.608;2.8848,-0.5239,0.3038;2.9492,0.174,1.1534;1.4277,-0.9494,0.1432;0.7893,-0.0846,-0.0688;1.0825,-1.4217,1.0679;1.3201,-1.6634,-0.6761;3.5044,0.1908,-0.9399;3.3882,1.2827,-0.8829;2.8736,-0.2917,-2.1167;3.092,0.5089,-3.2656;2.6813,1.5203,-3.1085;4.1749,0.6344,-3.4438;2.4503,-0.1356,-4.4735;1.9985,0.6599,-5.5323;2.0682,1.7436,-5.46;1.4534,0.0775,-6.6772;1.1043,0.7088,-7.4902;1.346,-1.3111,-6.7714;0.9169,-1.7668,-7.6598;1.7868,-2.11,-5.7146;1.7007,-3.1917,-5.7777;2.3394,-1.5272,-4.5736;2.6729,-2.1453,-3.7465;4.9808,-0.2563,-0.8694;5.6137,0.4832,-0.3591;5.4155,-0.4422,-1.8589)|",6.503521026950001
"[H]OC([H])([H])[C@@]([H])(O[H])[C@@]1([H])O[C@]([H])(OC([H])([H])[H])C([H])([H])C1([H])[H] |(3.8735,-3.2674,5.4279;4.6389,-2.6742,5.322;4.5708,-2.2454,3.9748;3.81,-1.4582,3.841;5.5478,-1.8173,3.7251;4.2305,-3.4338,3.0661;5.0263,-4.1821,3.1616;3.049,-4.0637,3.5798;2.3079,-3.5194,3.2579;4.0262,-3.1099,1.5829;3.8463,-4.0573,1.0617;2.8179,-2.3121,1.4856;3.0389,-1.194,0.6349;2.3631,-0.4029,0.9899;2.7733,-1.4895,-0.7123;1.4134,-1.8071,-0.972;1.3284,-1.9684,-2.0491;0.7488,-0.9794,-0.679;1.103,-2.714,-0.4397;4.5271,-0.8839,0.7645;4.8994,-0.3233,-0.0956;4.6966,-0.2918,1.6697;5.14,-2.2911,0.8922;6.0772,-2.2986,1.4562;5.3432,-2.7018,-0.1002)|",8.66954727693
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"[H]C1=C([H])C([H])=C(/C([H])=C(\[H])C2=C(C([H])([H])[H])N=C(C([H])([H])[H])O2)C([H])=C1[H] |(2.6757,-8.575,-0.1477;2.5846,-7.4924,-0.134;1.4165,-6.8769,-0.5855;0.5898,-7.4783,-0.9546;1.3054,-5.4883,-0.5653;0.3919,-5.0152,-0.9185;2.355,-4.6759,-0.0949;2.1748,-3.2242,-0.099;1.2184,-2.8761,-0.4847;3.0587,-2.2912,0.3206;4.0273,-2.5868,0.716;2.8376,-0.8726,0.2977;3.5906,0.2128,0.6732;4.9645,0.2656,1.2556;5.3919,-0.7342,1.3752;4.9524,0.7526,2.2381;5.6352,0.85,0.6146;2.8672,1.3745,0.4266;1.7382,0.9709,-0.0733;0.5788,1.7883,-0.5205;0.8193,2.8449,-0.392;-0.3192,1.5529,0.0628;0.3459,1.5991,-1.5748;1.6372,-0.3814,-0.1851;3.5267,-5.3161,0.3572;4.3595,-4.7248,0.7269;3.638,-6.7024,0.3375;4.5523,-7.1717,0.6916)|",3.945650832250001
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"[H]OC(=O)C1=C([H])C2=C(C([H])=C1[H])N([H])C([H])([H])[C@@]1([H])C([H])([H])/C([H])=C(/[H])[C@@]21[H] |(8.7517,0.3794,-1.423;8.5892,-0.5762,-1.4704;7.3104,-0.8482,-1.0728;6.985,-1.9966,-0.8704;6.3933,0.3145,-0.9133;5.2154,0.1239,-0.1767;5.0446,-0.8594,0.2533;4.2865,1.1392,0.0225;4.5375,2.4094,-0.5504;5.7158,2.6102,-1.2961;5.9006,3.5837,-1.7451;6.6262,1.5805,-1.4752;7.4994,1.7678,-2.0971;3.6472,3.4494,-0.3467;3.7744,4.2399,-0.9662;2.2599,3.1255,-0.0377;1.7367,4.0571,0.2019;1.7513,2.6665,-0.9021;2.1851,2.163,1.1537;2.5344,2.6904,2.0471;0.7395,1.622,1.3127;0.4836,1.465,2.371;-0.0108,2.3197,0.9155;0.7814,0.3073,0.5656;-0.1135,-0.2504,0.3042;2.0325,-0.0824,0.3144;2.3252,-1.0002,-0.1871;3.0523,0.8973,0.8785;3.4067,0.4978,1.8427)|",4.879001339465
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"[H]O/N=C(C(=C(/[H])C1=C(C([H])([H])[H])C([H])=C([H])N=C1[H])\C([H])([H])[H])/C([H])([H])C([H])([H])[H] |(4.9077,-1.6961,3.2031;4.3968,-1.9816,2.4301;3.278,-1.1249,2.4941;2.4436,-1.3175,1.5342;2.6252,-2.2822,0.4039;1.6011,-3.1118,0.1059;0.6909,-3.0088,0.695;1.5502,-4.1669,-0.9213;0.3707,-4.3939,-1.6694;-0.8626,-3.5452,-1.4832;-1.6421,-3.8237,-2.1983;-0.64,-2.4801,-1.617;-1.281,-3.6589,-0.4745;0.3896,-5.4281,-2.6068;-0.4897,-5.6326,-3.2124;1.5407,-6.1976,-2.7703;1.5655,-7.0015,-3.5044;2.656,-6.0143,-2.0552;2.6386,-5.0254,-1.1564;3.5399,-4.9156,-0.558;3.9231,-2.2077,-0.3712;3.7988,-2.6001,-1.3843;4.723,-2.7653,0.1246;4.2633,-1.1684,-0.4386;1.2151,-0.4302,1.5835;0.3122,-1.0555,1.5875;1.2336,0.1089,2.5357;1.1348,0.5617,0.4117;0.2305,1.1755,0.4891;1.107,0.0397,-0.5509;2,1.2338,0.4088)|",4.95791435611
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"[H]OC1=NN([H])[C@]([H])(OC2=C([H])C([H])=C([H])C([H])=C2[H])C([H])([H])C1([H])[H] |(5.0697,-5.0573,1.7129;5.195,-4.1809,2.106;4.8931,-3.2346,1.1621;4.9814,-2.0217,1.5382;4.7353,-1.0131,0.5916;4.43,-0.1933,1.1038;3.9101,-1.3208,-0.5459;3.9479,-0.4657,-1.2288;2.5271,-1.6187,-0.2358;1.6987,-0.6248,0.2142;2.0044,0.7427,0.2044;2.9564,1.1063,-0.1677;1.0596,1.665,0.666;1.3079,2.7231,0.6525;-0.1822,1.245,1.1364;-0.9081,1.969,1.4946;-0.4805,-0.1215,1.1432;-1.444,-0.4676,1.5084;0.4496,-1.0503,0.6891;0.2339,-2.1142,0.6919;4.4495,-2.5811,-1.2139;3.8403,-2.8327,-2.0856;5.4674,-2.3677,-1.5554;4.4585,-3.7313,-0.1966;5.1274,-4.5339,-0.5356;3.4538,-4.1662,-0.1051)|",5.4966997801
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"[H]O[C@]1(C([H])([H])[H])C([H])([H])C([H])([H])[C@]2([H])C(=C([H])[H])C([H])([H])C([H])([H])[C@]([H])(C([H])([H])[H])/C(C([H])([H])[H])=C(/[H])[C@]12[H] |(6.666,0.9055,1.2158;6.9214,0.078,1.6595;6.7569,-0.9856,0.7123;7.1722,-2.2506,1.4582;7.0978,-3.1291,0.808;6.5434,-2.4177,2.3392;8.208,-2.156,1.8014;7.6107,-0.7336,-0.5682;8.3107,0.0819,-0.3591;8.2164,-1.6086,-0.8268;6.6194,-0.3833,-1.701;6.4401,-1.2705,-2.3204;6.9855,0.3955,-2.3756;5.2859,-0.0141,-1.0138;4.4625,-0.248,-1.6968;5.1113,1.4505,-0.6259;6.0795,2.3664,-0.784;5.9124,3.4108,-0.5304;7.062,2.1261,-1.1769;3.7757,1.8925,-0.0319;3.8362,1.7862,1.058;3.6632,2.9666,-0.2238;2.4811,1.1946,-0.4974;2.3606,1.3004,-1.5845;1.651,1.7586,-0.0498;2.3008,-0.3119,-0.122;2.8114,-0.911,-0.8819;0.8148,-0.6963,-0.2062;0.4199,-0.4747,-1.2052;0.2072,-0.1402,0.5166;0.665,-1.7663,-0.0208;2.9558,-0.6322,1.2151;2.1123,-0.5324,2.4653;2.7201,-0.6833,3.3628;1.3042,-1.2754,2.4774;1.6292,0.4516,2.5509;4.2676,-0.9026,1.3052;4.6804,-1.0528,2.3011;5.2809,-1.0301,0.188;5.1521,-2.0183,-0.2861)|",6.44365597984
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"[H]C#CC([H])([H])OC1=C([H])C(=O)N=C2C([H])=C([H])C([H])=C([H])[C@]21[H] |(10.7294,-10.9562,-3.3234;10.9226,-10.0015,-2.8882;11.135,-8.922,-2.395;11.4342,-7.6254,-1.8018;11.8107,-6.9266,-2.5613;12.205,-7.721,-1.0251;10.2327,-7.0925,-1.2193;10.3206,-5.8863,-0.6136;11.4209,-5.1234,-0.4727;12.3965,-5.4069,-0.8478;11.3713,-3.8438,0.2629;12.394,-3.2384,0.5423;10.1092,-3.2955,0.6126;9.0403,-3.9958,0.4222;7.7441,-3.3759,0.6535;7.7501,-2.4052,1.1384;6.609,-3.9325,0.1657;5.6591,-3.4197,0.2944;6.6268,-5.1774,-0.588;5.7061,-5.519,-1.0528;7.7594,-5.8904,-0.7154;7.7932,-6.8246,-1.2652;9.015,-5.4651,0.0076;8.9622,-6.0087,0.9756)|",4.149736220125
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"[H]O[C@]1(C([H])([H])[H])/C(=C(/[H])C([H])=C(C([H])([H])[H])C([H])([H])[H])O[C@]2([H])C([H])=C(C([H])([H])[H])C([H])([H])C([H])([H])[C@]21[H] |(2.9517,-4.1354,3.4748;3.3634,-3.8985,2.6282;4.4377,-2.9994,2.9209;3.9525,-1.9068,3.8872;4.7481,-1.1978,4.1376;3.5969,-2.3533,4.8262;3.1306,-1.3462,3.4335;4.9691,-2.402,1.6056;4.3051,-1.8697,0.562;4.9318,-1.4807,-0.2367;2.8627,-1.7935,0.4191;2.287,-2.3049,1.186;2.1761,-1.1822,-0.5723;0.6681,-1.2085,-0.5873;0.2857,-1.6765,-1.5068;0.2492,-0.1913,-0.5646;0.2609,-1.7613,0.2654;2.8029,-0.4407,-1.7256;2.4486,0.5999,-1.7575;2.5127,-0.8934,-2.6849;3.8943,-0.4172,-1.6851;6.3536,-2.4453,1.5801;6.8056,-2.8306,2.8901;6.847,-1.9125,3.5034;8.1375,-3.5108,2.9272;8.9065,-3.1847,2.2297;8.3878,-4.4518,3.8506;9.7297,-5.1295,3.9559;9.6316,-6.2183,3.8431;10.1866,-4.9584,4.9406;10.4256,-4.7704,3.1915;7.3374,-4.9302,4.8474;7.1463,-5.9953,4.6429;7.7666,-4.9066,5.8592;5.9942,-4.1543,4.8293;6.0666,-3.2651,5.4692;5.2002,-4.7852,5.2484;5.7034,-3.7487,3.3906;5.741,-4.649,2.7621)|",4.857232231425001
"[H]OC([H])([H])[C@]1(C([H])([H])[H])O[C@]1([H])C([H])([H])C1(C([H])([H])[H])OC([H])([H])C([H])([H])O1 |(4.3169,1.3654,-1.1061;4.2472,2.0402,-0.4127;4.3003,1.3674,0.842;5.2272,0.7921,0.9633;4.3075,2.1586,1.5994;3.0901,0.4724,1.0686;1.7661,1.2037,1.1306;1.6204,1.7986,0.2232;1.7541,1.8883,1.9875;0.9325,0.504,1.2394;3.28,-0.5413,2.0771;3.115,-0.9639,0.7082;2.1452,-1.4284,0.5177;4.2645,-1.6918,0.0474;5.2271,-1.2544,0.3282;4.1662,-1.6097,-1.0433;4.3017,-3.1941,0.3834;4.4642,-3.4907,1.8703;3.6109,-3.1108,2.4377;5.3719,-3.0149,2.2529;4.5381,-4.5719,2.0198;3.1033,-3.8367,-0.0766;3.422,-4.5679,-1.2567;2.7547,-5.4322,-1.3171;3.2922,-3.9497,-2.1579;4.8891,-4.9207,-1.0336;5.4629,-5.0606,-1.954;4.9937,-5.8164,-0.4037;5.3817,-3.7616,-0.3693)|",8.39743342643
"[H]OC([H])([H])/C(=C(\[H])C([H])([H])C1(C([H])([H])[H])OC([H])([H])C([H])([H])O1)C([H])([H])[H] |(3.2317,-1.0635,1.5952;4.16,-1.2886,1.4223;4.7579,-0.1308,0.8317;4.7878,0.7066,1.5459;5.7937,-0.4295,0.6281;4.0664,0.296,-0.4457;3.4954,1.5093,-0.5142;3.5747,2.1557,0.3603;2.7645,2.1195,-1.6785;1.8573,2.6203,-1.3157;2.4451,1.3613,-2.4003;3.5907,3.1693,-2.4504;2.7519,3.9145,-3.4883;1.97,4.5016,-2.9964;2.2821,3.2086,-4.1807;3.3868,4.5977,-4.0605;4.1556,4.1389,-1.5546;5.5707,3.9676,-1.5396;6.0393,4.9494,-1.4204;5.8761,3.3149,-0.7104;5.8438,3.3097,-2.8898;6.7239,2.661,-2.9027;5.9364,4.0589,-3.6906;4.6882,2.4999,-3.0723;4.0855,-0.7324,-1.548;3.4357,-0.4769,-2.3876;3.7884,-1.7106,-1.1536;5.104,-0.8452,-1.9444)|",6.865432448115
"[H]OC1=C([H])C([H])=C(/C([H])=C(\OC([H])([H])[H])C(=O)N([H])[H])C([H])=C1[H] |(-2.0284,-2.4537,0.6949;-1.1993,-2.8808,0.428;-0.2965,-1.92,0.0749;-0.5884,-0.5525,0.1185;-1.5712,-0.2137,0.4415;0.3804,0.3714,-0.258;0.1422,1.4316,-0.2257;1.6636,-0.0305,-0.6752;2.6204,1.004,-1.052;2.2289,2.016,-1.1247;3.9368,0.9202,-1.3283;4.6596,-0.2656,-1.3172;5.3142,-0.5261,-0.0662;4.5807,-0.6511,0.7387;6.0053,0.2856,0.1909;5.8731,-1.4557,-0.1989;4.691,2.1496,-1.7421;4.2048,3.275,-1.7138;5.9826,1.9047,-2.1392;6.2413,0.9619,-2.3939;6.4656,2.6711,-2.587;1.9299,-1.4172,-0.7184;2.8981,-1.7573,-1.0651;0.968,-2.3473,-0.3513;1.1734,-3.4124,-0.3921)|",4.427292347635
"[H]/N=C(\C1=C([H])C([H])=C([H])C([H])=C1[H])N([H])OC(=O)C([H])=C([H])[H] |(8.3365,2.6252,1.2944;8.0122,1.8372,0.7306;8.7162,0.8045,0.9892;9.9018,0.7112,1.8938;10.8404,1.7537,1.9122;10.7087,2.6022,1.2461;11.9442,1.6935,2.7603;12.6683,2.5036,2.7618;12.123,0.591,3.5996;12.9832,0.5454,4.2618;11.1936,-0.4505,3.5866;11.3237,-1.305,4.245;10.0888,-0.3956,2.7364;9.3518,-1.1925,2.7381;8.368,-0.4505,0.4498;9.1564,-0.9624,0.0642;7.416,-0.3828,-0.593;6.1166,-0.5353,-0.1485;5.8015,-0.7418,0.9948;5.2112,-0.4098,-1.3176;5.669,-0.2082,-2.2811;3.892,-0.5408,-1.1651;3.2094,-0.4533,-2.0048;3.4682,-0.738,-0.1846)|",5.54023799618
"[H]C(=O)/C1=C(\[H])N(/C([H])=C(\[H])C([H])([H])[H])C2=C([H])C([H])=C(Cl)C([H])=C21 |(8.6924,2.4842,0.1691;7.6538,2.878,0.2063;7.4481,4.0821,0.2126;6.6057,1.8649,0.2456;5.2559,2.1436,0.2973;4.7852,3.1145,0.3184;4.5231,0.9843,0.3257;3.1197,0.861,0.3779;2.7886,-0.17,0.4295;2.2353,1.8631,0.3689;2.5768,2.8948,0.3129;0.7516,1.6519,0.4349;0.2517,2.0746,-0.4466;0.319,2.1513,1.3119;0.4955,0.5888,0.4917;5.4148,-0.0985,0.2932;5.1634,-1.4724,0.303;4.1571,-1.8771,0.3383;6.2535,-2.334,0.2639;6.1024,-3.4076,0.2707;7.5606,-1.819,0.2144;8.9079,-2.952,0.1652;7.8231,-0.4563,0.2026;8.8449,-0.0953,0.1636;6.7316,0.4238,0.2427)|",4.38103299305
"[H]O[C@@]([H])(C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])[C@@]([H])(C([H])([H])[H])[C@]([H])(O[H])C([H])=C([H])[H] |(7.3886,3.7667,-2.8143;7.8277,3.3907,-2.0347;7.0447,2.2645,-1.6091;7.5428,1.9333,-0.69;5.6212,2.715,-1.2341;5.0886,1.8755,-0.7782;5.7309,3.489,-0.4628;4.7758,3.275,-2.3882;5.3047,4.1106,-2.8738;4.6317,2.5001,-3.1495;3.4002,3.7787,-1.9295;2.8582,2.9511,-1.4489;3.5291,4.5486,-1.1546;2.5479,4.3464,-3.072;3.0904,5.1742,-3.5508;2.4243,3.5761,-3.8462;1.1701,4.8373,-2.6149;0.5878,5.235,-3.4542;1.2618,5.6333,-1.8658;0.5896,4.0238,-2.1627;7.1615,1.1158,-2.6457;6.6739,1.4406,-3.5761;8.6399,0.8333,-2.9656;8.7376,0.0688,-3.7454;9.1852,0.4848,-2.0799;9.1334,1.7452,-3.3068;6.4411,-0.2016,-2.2513;6.7702,-0.9516,-2.9933;5.0347,-0.0106,-2.3887;4.6098,-0.8606,-2.1923;6.8307,-0.7328,-0.8899;7.8808,-1.0035,-0.7832;5.9988,-0.9246,0.1349;6.3457,-1.336,1.0783;4.9452,-0.6654,0.0736)|",7.15659426815
"[H]C([H])=C(C(=O)N1C(=O)OC([H])([H])C1([H])[H])C([H])([H])/C([H])=C(\[H])Cl |(0.1392,-1.6722,-2.0464;0.0452,-1.1174,-1.1178;-0.8317,-1.3201,-0.514;0.97,-0.2245,-0.7503;0.9051,0.4531,0.5803;1.9117,0.6666,1.2411;-0.3427,0.7855,1.1408;-1.4936,1.2501,0.4623;-1.7163,1.2673,-0.7175;-2.3638,1.7281,1.3938;-1.871,1.4704,2.7218;-2.1109,2.3324,3.3463;-2.3775,0.5823,3.1156;-0.3696,1.2449,2.5324;0.0454,0.489,3.2017;0.2165,2.1636,2.6459;2.228,0.0841,-1.5468;2.1208,1.0748,-2.0108;3.0558,0.1739,-0.8321;2.5718,-0.942,-2.5898;2.8067,-1.9459,-2.2411;2.6084,-0.6718,-3.8922;2.3815,0.2997,-4.3179;3.0417,-1.8562,-5.1082)|",5.417786763455
"[H]O[C@]1(C([H])([H])[H])C(=O)C2=C(C([H])([H])[H])/C(C([H])([H])[H])=C(C([H])([H])[H])/C([H])=C\2C1([H])[H] |(7.8191,3.0942,-1.7003;7.503,2.1766,-1.7192;6.9096,1.9052,-0.4492;7.849,2.3185,0.6858;7.4157,2.0958,1.6676;8.0473,3.3984,0.647;8.7939,1.7763,0.5888;6.7022,0.359,-0.4085;7.6284,-0.4267,-0.496;5.2602,0.0868,-0.2196;4.5906,-1.1476,-0.0969;5.3417,-2.4557,-0.1717;4.977,-3.0697,-1.0059;5.1922,-3.049,0.7399;6.4094,-2.2873,-0.307;3.1954,-1.1337,0.0952;2.4655,-2.4535,0.2261;2.5935,-3.0702,-0.6729;1.394,-2.327,0.3867;2.8562,-3.0442,1.0645;2.4971,0.0978,0.1626;0.9987,0.147,0.3689;0.6448,1.182,0.3835;0.7009,-0.3184,1.3165;0.458,-0.3763,-0.4291;3.1936,1.3066,0.0376;2.6468,2.2458,0.0881;4.5679,1.303,-0.1575;5.4806,2.4985,-0.3234;5.3871,3.1949,0.52;5.2377,3.0576,-1.2348)|",4.9742411871400005
"[H]O[C@@]1([H])C2=C([H])C(C([H])([H])[H])=C(C([H])([H])[H])C(C([H])([H])[H])=C2C(=O)C1(C([H])([H])[H])C([H])([H])[H] |(4.9029,3.1307,-1.6474;5.1302,3.4597,-0.7631;5.6021,2.3542,-0.0032;5.6881,2.7567,1.0138;4.6684,1.1605,0.0021;3.2824,1.1803,0.068;2.7479,2.1264,0.0964;2.5794,-0.0288,0.0998;1.0708,-0.0043,0.1759;0.6956,1.0228,0.2007;0.7016,-0.5188,1.0723;0.6129,-0.5085,-0.6849;3.2764,-1.2642,0.0696;2.475,-2.5473,0.1154;1.7132,-2.5725,-0.6738;1.9392,-2.6497,1.0687;3.1003,-3.4325,-0.0045;4.684,-1.2901,0.0018;5.4559,-2.5914,-0.0308;5.1757,-3.2015,-0.8987;5.2537,-3.1947,0.863;6.5276,-2.3997,-0.0755;5.3617,-0.054,-0.0426;6.814,0.2378,-0.174;7.7513,-0.5433,-0.13;6.9822,1.7565,-0.4268;8.1629,2.3217,0.37;8.3094,3.3835,0.1417;9.08,1.7773,0.1249;7.9964,2.2261,1.4495;7.2257,1.9389,-1.9398;7.3067,3.0028,-2.1849;6.417,1.5044,-2.5419;8.1549,1.4384,-2.229)|",4.955193217605
"[H]OC([H])([H])C([H])([H])C1=C(C([H])([H])[H])C([H])=C2C(=O)[C@]([H])(C([H])([H])[H])C([H])([H])C2=C1[H] |(2.8474,-1.8977,-2.1263;2.1681,-2.5747,-1.9749;1.0989,-1.9509,-1.2828;0.6626,-1.1334,-1.8788;0.3299,-2.7213,-1.1666;1.511,-1.4284,0.1105;1.8901,-2.2813,0.6849;0.6161,-1.0628,0.6264;2.5758,-0.3544,0.034;2.2352,1.0245,-0.0478;0.7949,1.4855,0.0062;0.7331,2.5708,-0.1159;0.3229,1.2339,0.9648;0.1829,1.0285,-0.7803;3.2572,1.9667,-0.1626;3.0297,3.0283,-0.2212;4.5901,1.5516,-0.1989;5.8095,2.3817,-0.3131;5.8653,3.5891,-0.4634;7.0279,1.439,-0.2019;7.4713,1.6422,0.7836;8.0811,1.7323,-1.2741;8.3476,2.7934,-1.2547;7.6981,1.4993,-2.275;8.9882,1.1399,-1.1121;6.4357,0.0041,-0.2116;6.8128,-0.6097,0.6152;6.7042,-0.5252,-1.1361;4.9342,0.2019,-0.1278;3.9223,-0.7504,-0.0113;4.1626,-1.8093,0.0595)|",5.009615987705
"[H]OC([H])([H])C([H])([H])C1=C(C([H])([H])[H])C2=C(C([H])=C1C([H])([H])[H])C([H])([H])[C@]([H])(C([H])([H])[H])C2([H])[H] |(1.4578,1.819,-5.1459;1.7683,2.3932,-5.8653;2.8651,1.725,-6.4762;2.5315,0.7803,-6.9336;3.1977,2.3812,-7.2872;4.0393,1.4585,-5.5115;4.453,2.4222,-5.1999;4.83,0.9522,-6.0787;3.6455,0.6329,-4.2969;3.2706,1.2776,-3.091;3.3004,2.789,-2.9597;2.7885,3.2784,-3.7929;4.3311,3.1697,-2.9433;2.8213,3.1135,-2.0327;2.9006,0.4863,-1.9953;2.8767,-0.9095,-2.0871;3.2451,-1.5385,-3.2694;3.2427,-2.6246,-3.3396;3.6393,-0.7801,-4.3789;4.0807,-1.5057,-5.6334;3.9836,-2.5882,-5.5054;5.1314,-1.2986,-5.8761;3.4912,-1.2254,-6.5138;2.405,-1.523,-0.7876;1.3408,-1.7996,-0.8583;2.948,-2.4344,-0.5098;2.5975,-0.3762,0.2385;3.6389,-0.4257,0.5873;1.6724,-0.4421,1.4528;1.817,-1.373,2.0141;0.6192,-0.3966,1.1473;1.8566,0.3919,2.1405;2.4623,0.9187,-0.6075;3.0549,1.7415,-0.1917;1.4149,1.2614,-0.6195)|",6.00827381904
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"[H]OC([H])([H])C([H])([H])C1=C(C([H])([H])[H])C([H])=C2C(=C1[H])C(=O)[C@@]([H])(C([H])([H])O[H])[C@]2([H])O[H] |(2.6684,-0.9197,2.5331;1.9653,-1.5879,2.5698;2.2865,-2.5926,1.6161;3.2922,-3.0011,1.802;1.5659,-3.3998,1.7814;2.1896,-2.109,0.1554;1.1497,-1.8465,-0.0606;2.4469,-2.9553,-0.4947;3.1171,-0.9458,-0.1426;2.673,0.4053,-0.0628;1.234,0.7367,0.2663;1.1015,1.8158,0.3858;0.9111,0.241,1.187;0.557,0.406,-0.5325;3.5732,1.4522,-0.3126;3.2327,2.4821,-0.2518;4.8976,1.1725,-0.628;5.3331,-0.1526,-0.6875;4.4516,-1.2102,-0.4601;4.8132,-2.2339,-0.5243;6.7745,-0.1852,-0.9938;7.4542,-1.1679,-1.2636;7.2905,1.2605,-0.9426;7.7574,1.4022,0.0424;8.3485,1.5287,-2.0235;8.6788,2.5739,-1.9781;7.896,1.3758,-3.0198;9.4971,0.7286,-1.842;9.1697,-0.1882,-1.7685;6.0109,2.1381,-0.9783;5.8519,2.512,-2.0048;6.0049,3.2289,-0.0648;6.615,3.9029,-0.4023)|",4.993289156675
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"[H]OC1=C([H])C([H])=C(N([H])C(SC([H])([H])[H])C([H])N(=O)=O)C([H])=C1[H] |(6.2715,5.9042,6.1448;6.4333,5.0104,6.4845;5.6936,4.115,5.7664;4.8521,4.492,4.7141;4.7716,5.5389,4.4285;4.1231,3.5345,4.0134;3.4939,3.8413,3.1858;4.2113,2.1829,4.3694;3.5421,1.1549,3.6556;4.0066,0.2558,3.6422;2.325,1.1881,3.02;2.2313,-0.1125,1.8135;0.5484,-0.8012,1.9979;0.3389,-1.0203,3.0471;0.5588,-1.7346,1.4287;-0.1878,-0.1098,1.5892;1.389,2.1308,3.3526;1.5114,2.7413,4.2352;0.2393,2.4977,2.6107;0.0346,2.021,1.4803;-0.5106,3.329,3.1477;5.0555,1.812,5.4262;5.1298,0.7655,5.7121;5.7972,2.7643,6.1151;6.4521,2.4787,6.9317)|",3.88578578514
"[H]C([H])([H])O[C@@]1([H])C(=O)N2C([H])([H])[C@]3(C([H])([H])[H])OC([H])([H])[C@@]([H])(O3)[C@@]21[H] |(0.7238,-0.0136,-7.1376;1.4474,0.0584,-6.3225;2.3428,-0.5279,-6.5816;1.7392,1.1057,-6.1794;0.8068,-0.4678,-5.1652;1.6274,-0.4356,-4.0315;2.5716,-0.9771,-4.2048;1.8612,0.9463,-3.3326;2.4562,1.9694,-3.5873;1.1185,0.5446,-2.2421;1.1423,0.9066,-0.8392;1.828,1.7474,-0.7045;0.1423,1.2006,-0.4959;1.6234,-0.3306,-0.0388;1.5223,-0.1607,1.4626;0.4872,0.0336,1.7584;2.1477,0.6759,1.7881;1.8684,-1.0739,1.9529;2.9706,-0.6281,-0.4167;2.9345,-1.6636,-1.4015;3.5477,-1.3713,-2.2599;3.3403,-2.5912,-0.979;1.4375,-1.8247,-1.719;1.1494,-2.8544,-1.9444;0.8582,-1.467,-0.4469;0.8712,-0.8251,-2.7324;-0.1892,-1.0344,-2.9099)|",6.642299090705
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"[H]C1=C2C([H])([H])C([H])([H])C([H])([H])C3(OC([H])([H])C([H])([H])O3)[C@]2(C([H])([H])[H])C([H])([H])C1([H])[H] |(0.9846,-0.2839,-1.4571;2.011,-0.6389,-1.3997;2.5464,-1.261,-0.3444;1.996,-1.4827,1.0298;0.9354,-1.2153,1.0862;2.0755,-2.5461,1.2914;2.8348,-0.6503,2.052;2.9523,-1.226,2.9758;2.2933,0.2656,2.3146;4.2262,-0.256,1.5042;4.9025,0.0071,2.325;4.1449,0.6333,0.8683;4.8587,-1.3775,0.6662;4.9654,-2.5657,1.4554;6.2674,-2.5723,2.0266;6.3001,-1.9834,2.9554;6.5379,-3.6075,2.2513;7.109,-1.93,0.9247;7.4527,-2.6773,0.1955;7.9758,-1.3768,1.3012;6.2036,-1.0162,0.3129;4.0017,-1.6603,-0.6077;4.0994,-3.1403,-1.0363;3.5584,-3.2891,-1.9774;3.6776,-3.8137,-0.2863;5.1447,-3.4293,-1.1958;4.3607,-0.734,-1.8058;4.7649,0.2143,-1.4367;5.1228,-1.1752,-2.4559;3.0106,-0.4707,-2.5222;2.9707,0.5265,-2.9787;2.8311,-1.1884,-3.3378)|",6.849105617085001
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"[H]C1=C2C(=C([H])\C([H])=C/1OC([H])([H])[H])/[C@]1([H])C([H])([H])C(=O)O[C@]([H])(C/2([H])[H])C1([H])[H] |(2.7351,2.4545,-0.8348;3.2575,2.2824,0.1007;3.7342,3.3896,0.8206;4.4125,3.1991,2.032;4.5999,1.887,2.4971;5.1283,1.728,3.4351;4.1344,0.7902,1.7896;4.2846,-0.2235,2.1472;3.4541,0.9855,0.5774;3.0331,-0.1489,-0.0538;2.3467,-0.0173,-1.2881;2.1096,-1.0335,-1.6084;2.9738,0.4653,-2.0498;1.4158,0.5538,-1.1743;4.9082,4.3811,2.8537;5.8235,4.0846,3.3801;3.8665,4.8198,3.9031;4.3294,5.5153,4.6179;3.4864,3.9769,4.4863;2.6569,5.5478,3.3293;1.625,5.6776,3.9417;2.7735,6.1376,2.1083;3.8921,5.9088,1.2094;3.972,6.8499,0.6578;3.5378,4.7791,0.2387;2.502,4.9158,-0.0934;4.1672,4.8905,-0.6568;5.1821,5.5957,1.9595;5.974,5.3817,1.2317;5.5026,6.4569,2.5577)|",5.673573782925
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"[H]OC(=O)[C@@]1(O[H])C([H])([H])C(=O)[C@@]([H])(O[H])[C@@]([H])(O[H])[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])[H] |(4.1444,2.5433,3.891;4.5279,1.9942,4.6043;5.4628,1.2127,4.0375;6.2205,0.5315,4.681;5.5013,1.2666,2.4903;4.9825,2.566,2.1554;4.6937,2.5588,1.2286;6.9756,1.1655,2.0412;7.0134,1.3874,0.9643;7.5969,1.8972,2.5617;7.5366,-0.2238,2.2479;8.613,-0.4681,2.7584;6.7031,-1.3665,1.67;6.8171,-1.2933,0.572;7.1925,-2.6021,2.1397;8.0567,-2.3861,2.5498;5.1938,-1.2708,1.9904;4.6785,-1.9182,1.2625;4.936,-1.7228,3.2991;5.6151,-2.3999,3.4777;4.6293,0.1563,1.8184;4.6922,0.3754,0.7365;3.1434,0.2336,2.2322;3.0558,-0.0279,3.2921;2.8065,1.2737,2.1325;2.2052,-0.6642,1.413;2.3538,-0.4763,0.3391;2.4598,-1.7168,1.5839;0.732,-0.4409,1.7713;0.0778,-1.0965,1.1861;0.4265,0.5949,1.5778;0.5495,-0.648,2.8323)|",6.098071389705
"[H]OC([H])([H])C([H])([H])C1=C([H])C([H])=C(O/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])C([H])=C1[H] |(2.5337,-4.048,-4.5572;2.2961,-4.7657,-5.1661;3.04,-5.9095,-4.7771;2.8149,-6.1974,-3.7374;2.6993,-6.722,-5.4269;4.5611,-5.7209,-4.9445;4.7642,-5.5197,-6.0029;5.0599,-6.6651,-4.6903;5.104,-4.5972,-4.09;5.5957,-4.8383,-2.7974;5.6332,-5.8578,-2.4208;6.0531,-3.8011,-1.9901;6.4458,-3.9878,-0.9957;6.0148,-2.4911,-2.4698;6.4512,-1.5016,-1.6028;7.0835,-0.4149,-2.1038;7.4495,-0.4868,-3.1251;7.2885,0.6837,-1.3623;6.9231,0.7635,-0.3451;8.0328,1.8068,-1.9498;8.4865,1.8451,-3.0813;8.1447,2.823,-1.0572;8.8592,3.9963,-1.5054;8.6221,4.175,-2.5571;8.4527,4.8098,-0.8989;10.3597,3.8455,-1.3;10.8691,4.7763,-1.5756;10.7531,3.0399,-1.9262;10.589,3.6249,-0.2522;5.5141,-2.2144,-3.7418;5.4542,-1.1921,-4.1015;5.0698,-3.2701,-4.5416;4.6914,-3.0559,-5.5378)|",5.298056669235
"[H]OC([H])([H])[C@@]1([H])O[C@]([H])(C([H])([H])[C@@]([H])(O[H])C([H])([H])[H])[C@@]([H])(O[H])[C@@]1([H])O[H] |(4.0914,-3.5805,-3.9964;3.6053,-4.3483,-4.3412;2.2499,-4.1468,-3.9789;1.7817,-3.3668,-4.6025;1.7217,-5.0856,-4.1667;2.1331,-3.7841,-2.4976;2.5799,-4.5889,-1.9058;2.9061,-2.6039,-2.206;2.0947,-1.4256,-2.4348;2.2144,-1.1032,-3.4771;2.5426,-0.3167,-1.4835;1.9696,0.5851,-1.7396;2.278,-0.5929,-0.4547;4.0517,0.0055,-1.5031;4.1972,0.8922,-0.873;4.8047,-1.0221,-0.867;4.4959,-1.8618,-1.2497;4.6032,0.3177,-2.8992;4.0616,1.1456,-3.3741;4.5366,-0.5566,-3.5574;5.6589,0.594,-2.8209;0.6308,-1.8728,-2.2596;0.1532,-1.3667,-1.4133;-0.0897,-1.5856,-3.4645;-1.0253,-1.4806,-3.2322;0.7098,-3.4157,-2.0268;0.6223,-3.6301,-0.9577;-0.3354,-4.112,-2.6694;-0.4711,-3.6471,-3.5154)|",7.733475631210001
"[H]C([H])([H])C(OC(=O)N1C([H])([H])[C@]([H])(F)C([H])([H])[C@@]1([H])C(=O)F)(C([H])([H])[H])C([H])([H])[H] |(2.3168,-2.8479,-3.0356;3.01,-2.1196,-2.6001;3.8601,-2.6545,-2.1738;3.3663,-1.4671,-3.4047;2.2836,-1.2977,-1.5303;3.1601,-0.2091,-1.0527;4.3238,-0.4747,-0.4262;4.8142,-1.573,-0.2086;4.9399,0.6905,-0.05;4.3926,2.0425,-0.1628;4.4746,2.4324,-1.1857;3.3417,2.0702,0.1346;5.3046,2.8595,0.7653;4.8865,2.9161,1.7759;5.4579,4.1573,0.3012;6.6343,2.0977,0.7557;7.2751,2.319,1.6128;7.1772,2.3568,-0.1593;6.1873,0.6203,0.6913;6.9177,-0.0184,0.1891;5.9735,0.067,2.0957;4.9711,0.037,2.7352;7.1586,-0.3478,2.6129;1.108,-0.5287,-2.138;0.6198,0.0937,-1.381;0.3686,-1.2292,-2.5394;1.4498,0.1191,-2.9516;1.8244,-2.1539,-0.3463;1.086,-2.8867,-0.6902;1.3517,-1.5257,0.4165;2.6634,-2.6854,0.1049)|",6.449098256849999
"[H]C([H])(Cl)C(=O)N1C([H])([H])[C@]([H])(F)C([H])([H])[C@@]1([H])C(=O)F |(2.2512,2.4527,0.0765;1.3866,2.7682,-0.5132;1.2275,3.8379,-0.3936;1.7949,2.4694,-2.2555;0.1081,2.0663,-0.0658;-0.8375,2.7168,0.3657;0.0562,0.7047,-0.1298;1.1374,-0.2399,-0.4272;1.2542,-0.4007,-1.5052;2.0942,0.1004,-0.0223;0.6439,-1.5373,0.2274;0.9528,-1.5919,1.2764;1.1567,-2.6456,-0.4252;-0.8785,-1.4512,0.0962;-1.4178,-2.1053,0.7855;-1.1535,-1.7272,-0.9272;-1.1731,0.0484,0.325;-2.0391,0.397,-0.2418;-1.4879,0.3301,1.7894;-2.5593,0.3626,2.3011;-0.3456,0.4359,2.5258)|",6.43277142582
"[H]OC(=O)N1C([H])([H])[C@@]2(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])OC(=O)[C@]1([H])C2([H])[H] |(5.6765,3.5058,1.3142;5.8475,3.6693,2.2552;4.8063,3.2582,3.0342;4.7822,3.4943,4.2194;3.793,2.6053,2.3582;3.8711,1.9771,1.0275;3.747,2.7121,0.2226;4.8096,1.4303,0.8814;2.6775,0.9851,1.102;2.2217,0.3058,-0.2064;1.6248,1.3551,-1.1675;1.3,0.8623,-2.0903;2.3496,2.127,-1.4511;0.7494,1.8527,-0.7355;1.1452,-0.7553,0.1062;0.8446,-1.2578,-0.8196;0.2454,-0.3074,0.5427;1.5225,-1.5112,0.7998;3.426,-0.3859,-0.8802;3.0946,-0.935,-1.7686;3.9044,-1.0975,-0.2004;4.1819,0.3379,-1.208;3.1232,-0.0425,2.0521;3.107,0.5121,3.3178;3.4059,-0.096,4.3053;2.6821,1.9731,3.0988;2.4252,2.5217,3.9996;1.6688,1.7807,1.9682;1.3464,2.7243,1.5213;0.8033,1.181,2.2586)|",6.691279583795
"[H]OC1=NN([H])[C@@]2([H])C([H])([H])N(C([H])([H])C3=C([H])C([H])=C([H])C([H])=C3[H])C([H])([H])C([H])([H])[C@@]12[H] |(4.7607,-6.6198,-0.9524;5.6964,-6.3706,-0.9627;5.8523,-5.2437,-0.2185;7.009,-4.7449,-0.0234;6.8611,-3.5088,0.6616;7.4862,-3.5315,1.4636;5.4308,-3.306,1.0469;5.3163,-3.3065,2.1375;4.9261,-1.9763,0.4542;5.2916,-1.9233,-0.5784;5.3828,-1.1325,0.9778;3.4679,-1.8032,0.4736;2.932,-1.1109,1.6429;1.8373,-1.1346,1.5453;3.1584,-1.6354,2.5931;3.3745,0.3368,1.7556;3.3609,1.1781,0.6341;3.0802,0.7628,-0.3296;3.7184,2.5205,0.7497;3.7044,3.1601,-0.1293;4.0944,3.0451,1.9895;4.3743,4.0914,2.0785;4.1141,2.2157,3.1106;4.4121,2.6117,4.078;3.7595,0.8698,2.9903;3.7812,0.226,3.8674;2.7001,-3.015,0.1511;2.2897,-3.4839,1.0666;1.8364,-2.7464,-0.4733;3.5668,-4.0655,-0.5506;3.9871,-3.6565,-1.4762;2.9367,-4.9178,-0.8353;4.6862,-4.5117,0.4118;4.2267,-5.1519,1.1815)|",5.232749345115
"[H]OC(=O)SC([H])([H])[C@@]([H])(C(=O)O[H])N([H])C([H])([H])[H] |(5.8999,-5.177,-0.6598;5.4145,-5.466,0.1326;4.4834,-4.5584,0.489;3.7965,-4.6958,1.4626;4.3917,-3.1592,-0.6677;3.0703,-2.1856,0.166;2.1262,-2.3668,-0.3526;2.9919,-2.5897,1.1786;3.3753,-0.683,0.2322;2.5073,-0.2267,0.7357;4.5881,-0.4348,1.156;4.5097,-0.4999,2.3575;5.7323,-0.1572,0.5064;5.52,-0.2128,-0.4543;3.6483,-0.0875,-1.0847;3.7068,0.9225,-0.9579;2.6657,-0.3559,-2.1418;2.9223,0.2526,-3.0134;1.6282,-0.1264,-1.8496;2.7165,-1.4044,-2.4472)|",6.941624326255001
"[H]C1=C([H])C([C@@]([H])(N([H])[H])C([H])([H])[H])=C(C([H])([H])[H])S1 |(3.5433,-1.5865,4.2188;3.3929,-0.8537,3.4374;2.9537,-1.052,2.1631;2.6943,-2.0331,1.7767;2.8712,0.1546,1.3832;2.4161,0.1569,-0.0698;2.5039,1.1783,-0.457;3.2347,-0.6857,-0.9632;3.1589,-1.6604,-0.6704;4.2181,-0.4416,-0.8458;0.9466,-0.2635,-0.2099;0.2934,0.3948,0.3718;0.7934,-1.2876,0.1537;0.6478,-0.2285,-1.2623;3.26,1.2631,2.0996;3.2989,2.7098,1.6934;2.4333,3.2651,2.0776;3.2973,2.8115,0.6043;4.1992,3.209,2.0691;3.7257,0.8239,3.7324)|",5.79602501565
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"[H]OC(=O)[C@@]1([H])C([H])([H])N([H])C([H])([H])[C@@]1([H])C1=C([H])C(C([H])([H])C([H])([H])[H])=C([H])C([H])=C1[H] |(6.1771,0.4543,-3.3219;6.2662,-0.3849,-2.8395;5.4729,-1.3301,-3.4064;5.3183,-2.3898,-2.848;4.8683,-0.9589,-4.7618;5.6819,-0.523,-5.3559;4.2564,-2.1965,-5.4884;4.0761,-2.9736,-4.7395;4.9212,-2.6142,-6.2495;2.9753,-1.7898,-6.0813;3.1511,-1.3384,-6.979;2.459,-0.7887,-5.1497;1.6591,-0.1958,-5.6033;2.0388,-1.305,-4.2777;3.6739,0.0832,-4.7189;3.8619,0.7938,-5.5332;3.4763,0.8918,-3.4523;3.0879,0.2901,-2.2446;2.9564,-0.7885,-2.1999;2.8953,1.0353,-1.0766;2.4506,0.3613,0.2047;2.8179,-0.672,0.2197;2.9099,0.8695,1.062;0.9209,0.3601,0.3787;0.6358,-0.127,1.3181;0.4326,-0.1746,-0.4438;0.5249,1.382,0.3921;3.0946,2.4223,-1.1273;2.9571,3.017,-0.2269;3.4761,3.0408,-2.3158;3.629,4.1166,-2.3446;3.6651,2.2807,-3.4715;3.9537,2.7707,-4.3994)|",5.836842093225
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"[H]OC(=O)C1=C([C@]([H])(C([H])([H])[H])[C@]2([H])N([H])C([H])([H])C([H])([H])N([H])C2([H])[H])C([H])=C([H])C([H])=C1[H] |(5.6559,2.5509,4.7406;4.8261,2.1023,4.9887;4.1701,1.8929,3.8136;4.6658,2.2247,2.7541;2.8284,1.273,4.0118;2.1829,0.5363,2.9878;2.8224,0.2193,1.6373;3.8937,0.4023,1.7022;2.2643,1.1919,0.5768;2.424,2.2262,0.8954;1.1879,1.0431,0.4209;2.7683,1.0648,-0.3865;2.6301,-1.2773,1.2252;1.5832,-1.4309,0.928;2.9178,-2.2711,2.2738;2.349,-2.0801,3.095;4.3317,-2.3445,2.6556;4.724,-1.4115,3.0972;4.4409,-3.1367,3.4059;5.1639,-2.6868,1.4209;4.8939,-3.7067,1.0868;6.2303,-2.6794,1.674;4.9099,-1.6728,0.3958;5.4879,-1.8495,-0.4227;3.4969,-1.6609,0.0113;3.361,-0.9476,-0.8068;3.1469,-2.6486,-0.3423;0.8779,0.0874,3.25;0.35,-0.4692,2.4808;0.2341,0.3318,4.4616;-0.7795,-0.0293,4.6155;0.8919,1.0355,5.4703;0.4038,1.2281,6.4213;2.1827,1.497,5.2414;2.7048,2.0533,6.0114)|",4.481715117735
"[H]OC(=O)C1=C([H])C([H])=C([C@]([H])(C([H])([H])[H])[C@]2([H])N([H])C([H])([H])C([H])([H])N([H])C2([H])[H])C([H])=C1[H] |(4.9496,5.5956,4.7081;4.8261,5.01,3.9386;3.9262,4.0721,4.3382;3.4511,4.0765,5.4571;3.6085,3.0815,3.2799;4.1785,3.1363,2.0015;4.8824,3.9252,1.76;3.8403,2.1792,1.0474;4.2853,2.2323,0.0568;2.9295,1.1513,1.3331;2.5527,0.1329,0.2677;3.1487,0.3462,-0.6262;1.0651,0.2878,-0.1118;0.8473,1.3192,-0.409;0.4084,0.0401,0.7312;0.7949,-0.3616,-0.9504;2.8889,-1.3303,0.7129;2.1419,-1.6339,1.4602;4.2061,-1.5204,1.3379;4.303,-0.8986,2.1371;5.3368,-1.3385,0.423;5.4202,-0.3142,0.019;6.2598,-1.551,0.9752;5.1983,-2.3145,-0.7445;5.3024,-3.3454,-0.3572;5.9973,-2.1407,-1.4743;3.9034,-2.0853,-1.3912;3.8061,-2.6952,-2.2;2.8044,-2.3314,-0.4538;1.8558,-2.2282,-0.9891;2.8372,-3.3475,-0.0196;2.3655,1.1105,2.621;1.653,0.3294,2.8739;2.699,2.0584,3.5819;2.2624,2.0264,4.5749)|",4.5715126884
"[H]C1=C(C([H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C([H])([H])[C@@]1([H])B1OC(C([H])([H])[H])(C([H])([H])[H])C(C([H])([H])[H])(C([H])([H])[H])O1 |(3.2939,1.0872,-2.2367;3.1796,0.0071,-2.21;2.854,-0.6973,-1.1178;2.5859,-0.1878,0.279;1.5992,-0.5774,0.5802;3.6119,-0.7525,1.2848;3.3846,-0.411,2.3016;4.6252,-0.4177,1.0321;3.6132,-1.8479,1.2927;2.5264,1.3429,0.3686;2.2674,1.6601,1.3852;1.7767,1.7533,-0.3167;3.4951,1.7921,0.1169;2.8162,-2.1836,-1.4401;3.7086,-2.6909,-1.0412;1.9494,-2.6914,-0.9956;2.7879,-2.2154,-2.985;1.7446,-2.2525,-3.3213;3.3026,-3.0829,-3.4113;3.4257,-0.86,-3.4337;4.5116,-0.9932,-3.5718;2.8296,-0.318,-4.7873;1.534,0.1218,-4.9284;1.4032,0.712,-6.251;1.552,2.2298,-6.0701;1.4046,2.7677,-7.0124;2.5388,2.488,-5.673;0.8004,2.5772,-5.3544;0.0124,0.3862,-6.7922;-0.1122,0.7792,-7.8078;-0.7464,0.8514,-6.1548;-0.1758,-0.6896,-6.8089;2.6056,0.0544,-7.0362;2.2411,-1.2733,-7.7132;1.5839,-1.1197,-8.5758;1.7433,-1.9539,-7.0151;3.158,-1.7589,-8.0611;3.3144,0.9715,-8.0322;2.6289,1.2926,-8.825;4.1446,0.4328,-8.5002;3.7227,1.8593,-7.5444;3.5378,-0.2646,-5.9644)|",6.873595863630001
"[H]O[C@]([H])(C1=C([H])C([H])=C([H])C([H])=C1[H])[C@]1(C([H])([H])[H])C([H])=C([H])C([H])([H])C1([H])[H] |(4.5681,-0.12,0.711;4.0059,0.2135,1.4313;2.7095,-0.3413,1.2285;2.0278,0.3159,1.783;2.5935,-1.7423,1.8216;3.6686,-2.6399,1.7908;4.6194,-2.3254,1.371;3.5453,-3.9244,2.3218;4.3933,-4.6037,2.2897;2.3437,-4.3331,2.9031;2.2491,-5.3316,3.3214;1.2699,-3.4432,2.956;0.3348,-3.7426,3.4224;1.3986,-2.1601,2.4227;0.5611,-1.4684,2.4831;2.3048,-0.2327,-0.2883;0.8683,-0.7316,-0.517;0.5942,-0.6159,-1.5714;0.151,-0.1536,0.0799;0.7522,-1.7867,-0.2511;3.3144,-0.9679,-1.1546;3.3655,-2.0532,-1.1838;4.1295,-0.1426,-1.821;4.9378,-0.4619,-2.4749;3.8065,1.3129,-1.5729;3.7,1.8749,-2.5099;4.6084,1.8071,-1.0074;2.4813,1.2496,-0.768;1.6427,1.5145,-1.4213;2.4765,1.9502,0.0714)|",6.37834865572
"[H]C1=C([H])C([H])([H])[C@]([H])(/C([H])=C(\[H])C(F)(F)F)OC1=O |(0.0364,-2.4933,-0.4173;-0.0844,-1.4439,-0.1717;-1.2642,-0.8146,-0.2181;-2.1675,-1.3484,-0.5055;-1.3707,0.6349,0.1595;-1.6896,0.7202,1.2082;-2.1385,1.1422,-0.4365;-0.0181,1.3436,-0.0314;-0.0233,2.2728,0.5516;0.2547,1.706,-1.4711;-0.5528,2.2288,-1.9802;1.3938,1.4705,-2.1176;2.2357,0.9706,-1.649;1.6161,1.8745,-3.5397;1.8832,0.7979,-4.314;0.5495,2.5047,-4.0798;2.6758,2.7077,-3.6481;1.0672,0.5859,0.5331;1.1403,-0.7669,0.3076;2.1733,-1.3448,0.5528)|",5.8776591708
"[H]C1=C([H])C([H])=C(C(=O)N(/N=C([H])/C([H])=C(\[H])C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(4.1884,7.7663,-2.1175;4.7624,6.855,-1.9714;4.1167,5.6174,-1.9448;3.0398,5.5622,-2.0802;4.8448,4.4459,-1.7405;4.3334,3.4923,-1.7124;6.2369,4.5011,-1.5782;7.1369,3.3047,-1.4709;8.2514,3.3203,-1.9823;6.7019,2.1585,-0.7922;5.5845,2.2438,-0.0187;5.2087,1.2319,0.6892;5.7451,0.2798,0.7098;4.0172,1.3406,1.5117;3.4981,2.2971,1.4765;3.5574,0.3407,2.2829;4.1041,-0.6041,2.2949;2.3348,0.4101,3.146;2.5828,0.2252,4.2003;1.6032,-0.3581,2.8599;1.8469,1.3877,3.077;7.5538,0.9747,-0.8005;8.3875,1.1794,-1.4682;6.9906,0.1044,-1.1571;7.9339,0.7647,0.207;6.8817,5.7461,-1.6376;7.9625,5.7764,-1.5484;6.1487,6.9167,-1.8159;6.6598,7.8753,-1.8424)|",4.666752536075
"[H]C1C(C#N)=C(C2C(=O)C([H])=C([H])C(C([H])([H])[H])=C2[H])N(C([H])([H])[H])N1[H] |(5.6546,5.4807,0.3592;5.6739,4.4134,0.1921;4.6446,3.5033,0.2134;3.2904,3.8662,0.4362;2.1985,4.2213,0.6261;5.2273,2.1786,0.0685;4.5777,0.9223,0.0624;5.2466,-0.2859,0.5776;6.4305,-0.307,0.9944;4.4131,-1.4743,0.626;4.8938,-2.3721,1.0034;3.1102,-1.4763,0.2209;2.534,-2.3995,0.2728;2.4646,-0.2973,-0.2752;1.0282,-0.3577,-0.7333;0.9002,-1.0421,-1.5832;0.6663,0.628,-1.0425;0.3664,-0.7179,0.0656;3.1989,0.859,-0.3279;2.7257,1.7527,-0.7238;6.588,2.3909,-0.0386;7.549,1.5632,-0.7674;7.5049,0.5652,-0.3254;8.5442,1.9932,-0.6266;7.3041,1.5453,-1.8355;6.8341,3.7635,-0.0525;7.7285,4.0613,0.3234)|",2.85175315324
"[H]C1C(C#N)=C(C2C(=O)C([H])=C([H])C([H])=C2[H])N(C([H])([H])[H])N1[H] |(5.2585,-1.5433,0.4;4.4538,-0.8498,0.2025;4.4787,0.5215,0.1088;5.6662,1.2902,0.2278;6.6691,1.8705,0.3353;3.1051,0.9764,-0.0301;2.6183,2.3023,-0.1356;1.2947,2.6771,0.4005;0.5049,1.8587,0.9327;0.97,4.0896,0.3201;-0.0047,4.3709,0.708;1.8262,5.0124,-0.2123;1.532,6.0593,-0.2566;3.104,4.6303,-0.7148;3.7658,5.3743,-1.147;3.4841,3.3159,-0.6591;4.4497,3.0304,-1.0645;2.3408,-0.1715,-0.016;1.044,-0.3783,-0.6602;0.3616,0.3633,-0.2376;0.7016,-1.3881,-0.4187;1.1375,-0.271,-1.7466;3.1832,-1.2787,0.0373;2.808,-2.1104,0.481)|",2.9116182003500004
"[H]OC1=C(C2=NN(C([H])([H])[H])C([H])=C2C#N)C([H])=C(Cl)C([H])=C1[H] |(3.4609,2.6814,-0.0768;4.1279,3.4058,-0.0755;5.364,2.8634,-0.0447;5.6167,1.4644,-0.0179;4.5105,0.4997,-0.0191;3.2412,0.9302,-0.0555;2.449,-0.164,-0.0488;1.0049,-0.0069,-0.0565;0.5459,-0.9928,-0.1454;0.7096,0.612,-0.9073;0.6738,0.47,0.8704;3.1694,-1.2986,-0.0057;2.7101,-2.276,0.0091;4.5116,-0.9364,0.0137;5.5853,-1.8613,0.0551;6.4488,-2.6419,0.0893;6.9548,1.025,0.0106;7.1828,-0.0334,0.0312;7.9993,1.9361,0.0145;9.66,1.3544,0.0491;7.7516,3.3112,-0.0104;8.5778,4.0142,-0.0071;6.4408,3.762,-0.0393;6.2137,4.8229,-0.0593)|",4.5633492728850005
"[H]OC1=C(C2=NN(C([H])([H])[H])C([H])=C2C#N)C([H])=C(C([H])([H])[H])C([H])=C1[H] |(6.8597,0.6294,-0.1903;6.5379,-0.3001,-0.1753;5.1851,-0.2922,-0.1548;4.3971,0.8884,-0.1353;5.0255,2.2134,-0.1344;6.3611,2.3311,-0.1794;6.6465,3.6526,-0.1629;8.031,4.0888,-0.1903;8.0542,5.1793,-0.2194;8.5256,3.6891,-1.0794;8.5526,3.7342,0.703;5.5316,4.4014,-0.1073;5.559,5.481,-0.088;4.4483,3.5298,-0.0872;3.0956,3.9496,-0.0278;1.9939,4.3246,0.0213;2.9934,0.7521,-0.1135;2.3788,1.6455,-0.1012;2.3581,-0.485,-0.1093;0.8508,-0.5941,-0.0955;0.5016,-1.2338,0.7245;0.4698,-1.0308,-1.0279;0.3817,0.3873,0.0247;3.1665,-1.6351,-0.1246;2.7005,-2.6183,-0.1176;4.5489,-1.542,-0.1483;5.1761,-2.428,-0.1593)|",4.590560657935001
"[H]OC1=C(C2=NN(C([H])([H])[H])C([H])=C2C#N)C([H])=C([H])C([H])=C1[H] |(3.3655,2.702,0.0039;4.0316,3.426,0.0244;5.2692,2.8823,0.0483;5.5181,1.4817,0.062;4.4106,0.5199,0.0538;3.14,0.9473,0.0137;2.3491,-0.1492,0.0176;0.9058,0.0059,-0.0119;0.4476,-0.9839,-0.0439;0.6139,0.5731,-0.8996;0.5689,0.5369,0.8828;3.0711,-1.2824,0.0607;2.6138,-2.2609,0.071;4.4126,-0.9177,0.0846;5.4855,-1.8431,0.1309;6.347,-2.6261,0.1675;6.8583,1.0466,0.085;7.0727,-0.0158,0.0946;7.9166,1.9444,0.0974;8.9376,1.5761,0.1157;7.6543,3.3192,0.0861;8.4745,4.0323,0.0952;6.3459,3.7808,0.0617;6.1161,4.8416,0.0513)|",4.737502137205
"[H]C1=C([H])C2=C(C(=O)/C(=C(\[H])N([H])N([H])[H])C(=O)O2)C([H])=C1[H] |(3.4047,-1.1018,1.0233;2.5114,-0.6104,0.6477;1.3331,-1.3402,0.5254;1.2791,-2.3899,0.7946;0.1874,-0.7048,0.0423;0.2094,0.6488,-0.3186;-1.0175,1.3002,-0.8269;-1.0493,2.4883,-1.1549;-2.1846,0.4145,-0.9063;-3.3706,0.9603,-1.3741;-3.3511,2.0123,-1.6544;-4.5279,0.3221,-1.5137;-4.5617,-0.6641,-1.2575;-5.7191,0.9036,-1.9889;-5.5658,1.2627,-2.9303;-5.9758,1.6842,-1.3862;-2.1386,-0.9818,-0.5161;-3.0827,-1.7584,-0.5637;-0.943,-1.4856,-0.0541;1.4082,1.3658,-0.1865;1.3995,2.4132,-0.4714;2.5542,0.7456,0.2924;3.4791,1.3058,0.3916)|",4.639541151025
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"[H]C1C2=C(NC(=O)[C@]1([H])C(=O)OC([H])([H])[H])C([H])=NC([H])=C2[H] |(4.3537,0.8197,0.5589;3.3166,0.6008,0.8073;2.7812,-0.6217,0.5734;1.3692,-0.847,0.9386;0.5596,0.0163,1.4866;1.0339,1.3056,1.7644;0.3241,2.133,2.3004;2.4869,1.6926,1.403;2.9672,2.0409,2.3274;2.4771,2.8963,0.4408;1.6998,3.0414,-0.4704;3.4873,3.7357,0.7335;3.5997,4.8864,-0.1294;3.7394,4.5733,-1.167;4.4702,5.4338,0.2314;2.6989,5.5004,-0.0566;0.8419,-2.1863,0.6563;-0.2011,-2.3616,0.9174;1.5259,-3.1498,0.1313;2.8626,-2.9177,-0.1992;3.3744,-3.7708,-0.6345;3.4974,-1.7376,-0.007;4.5401,-1.6111,-0.2855)|",3.0313482945699994
"[H]C([H])=C([H])C([H])([H])O/N=C(/C1=C([H])C([H])=C(N(=O)=O)C([H])=C1[H])C([H])([H])[H] |(5.0365,2.0822,-2.1174;5.6051,1.5049,-1.3943;6.5287,1.0522,-1.7399;5.1787,1.3633,-0.1406;4.2458,1.8307,0.1744;5.8636,0.5948,0.949;5.2336,-0.2386,1.2909;6.0431,1.2409,1.8214;7.1106,0.0783,0.4765;7.646,-0.7588,1.4428;8.8103,-1.2244,1.138;9.4073,-2.1436,2.1409;10.6459,-2.7635,1.9011;11.1809,-2.5775,0.977;11.2101,-3.6293,2.8343;12.163,-4.1113,2.6557;10.5263,-3.8756,4.0209;11.1169,-4.7873,5.0093;12.2036,-5.2998,4.7384;10.4917,-4.9864,6.0514;9.2941,-3.2774,4.2926;8.792,-3.4927,5.2277;8.7433,-2.4184,3.3543;7.7878,-1.9456,3.546;9.5568,-0.8985,-0.1299;10.5612,-0.5239,0.0972;9.0243,-0.1426,-0.7048;9.6733,-1.7931,-0.7537)|",4.046332956935
"[H]OC(=O)C([H])([H])/N=C(O[H])\C([H])=C(/[H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(0.9035,-3.5505,-3.7794;1.2241,-3.5184,-4.7151;1.9145,-2.3751,-4.8158;2.4506,-2.0035,-5.8331;1.976,-1.5601,-3.5116;1.5781,-0.5611,-3.7346;3.0413,-1.4325,-3.2701;1.237,-2.2482,-2.4697;1.1059,-1.7559,-1.3005;0.3187,-2.4544,-0.4388;0.4806,-2.1371,0.4637;1.7091,-0.501,-0.8078;2.5847,-0.1492,-1.3477;1.2176,0.2215,0.2105;0.3155,-0.0996,0.7305;1.8122,1.4906,0.7378;1.0978,2.3112,0.5704;2.7356,1.7397,0.1989;2.0889,1.3995,2.2606;2.8268,0.6056,2.4306;0.9142,1.0379,2.9643;0.4826,1.8988,3.1495;2.6347,2.7202,2.8375;3.4993,3.0718,2.2538;2.9815,2.5203,3.8562;1.5323,3.6914,2.9243;1.4427,4.2284,2.0652;1.6926,4.3595,3.6728)|",5.1184615279050005
"[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(-0.584,-0.9656,-2.2714;0.1736,-1.4376,-2.6983;1.0409,-1.849,-1.7503;2.0669,-2.4198,-2.0424;0.6555,-1.501,-0.3137;-0.4212,-1.6423,-0.165;1.1831,-2.2044,0.3359;1.066,-0.0633,0.0785;0.8262,0.0752,1.1416;2.1567,0.0172,-0.0059;0.4273,1.0892,-0.7203;0.7408,1.044,-1.7713;0.8265,2.0287,-0.3131;-1.1105,1.146,-0.6585;-1.4362,0.9083,0.3625;-1.7186,0.1742,-1.5278;-1.9292,0.7057,-2.3335;-1.6897,2.5235,-1.0476;-1.1552,3.3259,-0.518;-2.7389,2.5598,-0.7363;-1.6683,2.6405,-2.5133;-0.7463,2.9082,-2.8512;-2.3254,3.3435,-2.8391)|",7.455919503700001
"[H]C1=C([H])C([H])=C(C(NN)C(=O)OC([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(3.4467,6.3707,7.1208;3.2834,5.5143,6.4726;4.3639,4.7812,5.982;5.3791,5.0641,6.2479;4.1667,3.6796,5.1503;5.0121,3.1185,4.776;2.8638,3.2903,4.7902;2.6306,2.1264,3.9073;1.3839,1.8006,3.6325;0.2984,1.5383,3.4135;3.6493,1.2653,3.2856;4.8515,1.3977,3.4189;3.0724,0.2948,2.5306;3.9681,-0.6293,1.8677;3.4141,-0.9532,0.981;4.8644,-0.0862,1.5587;4.3297,-1.8233,2.7549;4.8283,-1.4218,3.6471;3.0836,-2.6035,3.194;3.3549,-3.4238,3.8682;2.5689,-3.0399,2.3276;2.371,-1.9574,3.7163;5.3275,-2.723,2.0108;5.6226,-3.5728,2.636;6.2377,-2.1766,1.7374;4.8869,-3.1277,1.0902;1.7785,4.0357,5.2884;0.7593,3.7634,5.0284;1.9881,5.1327,6.1191;1.1321,5.6908,6.4889)|",3.888506923645
"[H]NC(O[H])C([H])([H])/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(1.3498,-0.6392,-6.2354;1.7122,0.3174,-6.2022;2.1042,0.5791,-5.0195;2.6278,1.7787,-4.7257;2.8025,1.7493,-3.7532;2.0654,-0.391,-3.821;2.7718,-1.2071,-4.0372;1.061,-0.8362,-3.7788;2.4389,0.3234,-2.6168;2.2738,-0.1592,-1.4487;2.6219,0.616,-0.4062;2.53,0.1149,0.439;1.7082,-1.5219,-1.0869;1.6085,-2.1274,-1.9901;2.449,-2.0259,-0.4552;0.3435,-1.4462,-0.3342;0.045,-0.3987,-0.2029;-0.4276,-1.8988,-0.9666;0.326,-2.1451,1.0386;0.8571,-3.1053,0.9642;-0.7116,-2.3833,1.3105;0.9141,-1.3022,2.1818;0.2922,-0.4073,2.309;2.2349,-0.8342,1.8891;2.8201,-1.5405,2.2476;0.9871,-2.0622,3.5215;0.033,-2.5684,3.729;1.1591,-1.3362,4.3222;2.1495,-2.966,3.4806;1.9049,-3.867,3.0771;2.5071,-3.146,4.4144)|",6.424608010305
"[H]O/C(=N/C([H])([H])C(=O)OC([H])([H])C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(5.009,-1.9406,0.633;4.556,-1.3823,-0.0169;5.4577,-0.9662,-0.958;4.9745,-0.2837,-1.9117;5.8213,0.2073,-2.9713;5.3145,1.03,-3.4832;6.7912,0.6082,-2.6276;6.152,-0.8703,-4.0049;6.3235,-2.0434,-3.752;6.2801,-0.3265,-5.2329;6.6483,-1.2254,-6.3091;7.151,-0.5826,-7.036;7.3556,-1.9641,-5.9243;5.4213,-1.8949,-6.9088;5.7155,-2.5098,-7.7673;4.6989,-1.1471,-7.2516;4.9374,-2.5421,-6.172;6.895,-1.3628,-0.6661;6.9331,-2.4572,-0.5658;7.5268,-1.1281,-1.525;7.4577,-0.6931,0.6042;7.4261,0.3965,0.4926;6.8211,-0.9224,1.4705;8.8959,-1.1272,0.9126;9.536,-0.9345,0.0366;8.9315,-2.2134,1.0791;9.5002,-0.4158,2.1297;8.8536,-0.5803,3.0026;9.501,1.0088,1.9643;10.0409,1.2054,1.1799;10.9183,-0.9102,2.4675;11.5618,-0.7442,1.589;10.9028,-1.9944,2.6361;11.5461,-0.2648,3.619;11.4057,0.7418,3.5286;11.0493,-0.5323,4.4686)|",6.451819395355
"[H]OC(=O)C([H])([H])N(C(=O)C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H])C([H])([H])[H] |(3.1406,2.032,0.0608;3.9303,2.6212,0.1829;4.9787,1.8732,0.5554;6.0771,2.3396,0.7493;4.6736,0.3723,0.7225;3.8537,0.2484,1.438;5.5702,-0.1085,1.116;4.2983,-0.2906,-0.5328;3.014,-0.1588,-0.9718;2.228,0.599,-0.3766;2.5646,-0.9628,-2.1834;3.0805,-0.5684,-3.0721;2.8761,-2.0076,-2.0971;1.048,-0.8801,-2.3991;0.5345,-1.2503,-1.5027;0.7627,0.1733,-2.4882;0.5476,-1.6484,-3.6324;1.1204,-1.3377,-4.5194;-0.4963,-1.3555,-3.8159;0.5868,-3.1826,-3.5145;0.0528,-3.4725,-2.5993;1.9113,-3.6935,-3.3934;2.1858,-3.8294,-4.3251;-0.0861,-3.8816,-4.7144;-1.0878,-3.4628,-4.8966;-0.2085,-4.9413,-4.4678;0.8169,-3.7995,-5.876;0.6909,-2.9195,-6.3711;0.6228,-4.5422,-6.5421;5.3661,-0.9932,-1.2377;6.2093,-0.3145,-1.4094;5.0157,-1.3592,-2.201;5.7203,-1.8469,-0.647)|",6.7130486918350005
"[H]OC(=O)[C@@]([H])(/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H])C([H])([H])[H] |(1.629,-3.0463,0.2756;1.6539,-3.9388,-0.151;2.9507,-4.1807,-0.3855;3.3541,-5.1952,-0.9055;3.9102,-3.0639,0.0866;4.5301,-2.8179,-0.7869;3.1096,-1.941,0.5484;3.5231,-0.7406,0.5534;2.641,0.1867,1.0191;3.0389,1.0687,0.9612;4.8732,-0.1987,0.1222;5.3916,0.1779,1.0168;5.485,-1.018,-0.2638;4.7896,0.9161,-0.9411;4.2316,0.558,-1.812;4.2342,1.7842,-0.5543;6.1763,1.3861,-1.3979;6.7359,0.5306,-1.7996;6.7454,1.7731,-0.5402;6.1054,2.4646,-2.478;5.5822,3.348,-2.0587;5.3824,1.9517,-3.5818;5.663,2.5152,-4.3317;7.4951,2.9183,-2.9522;8.0551,2.0339,-3.2783;8.0522,3.3823,-2.1222;7.3202,3.7889,-4.1248;7.0697,4.7332,-3.8366;8.1832,3.8622,-4.6574;4.8137,-3.6267,1.198;5.2845,-4.5516,0.8545;4.2213,-3.8412,2.0935;5.5939,-2.9068,1.4679)|",6.522568996485001
"[H]OC(=O)C([H])([H])C([H])([H])/N=C(O[H])\C([H])=C(/[H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(-0.5756,-2.1497,3.6741;-0.7203,-2.8867,4.3266;0.4604,-3.2444,4.8572;0.5547,-4.1552,5.6487;1.672,-2.4017,4.435;1.667,-1.4858,5.0418;2.5653,-2.9693,4.7071;1.7229,-2.0087,2.9481;1.7348,-2.9189,2.3316;2.6681,-1.4828,2.753;0.5402,-1.2323,2.6078;0.5269,-0.3235,1.7127;-0.6873,0.2334,1.4525;-0.5559,1.0604,0.9622;1.6724,0.1702,0.9203;2.6595,0.0282,1.3516;1.5527,0.7342,-0.2913;0.5727,0.8415,-0.7549;2.6847,1.2651,-1.1158;2.7523,0.6763,-2.0435;3.6386,1.157,-0.5834;2.4556,2.7475,-1.5066;2.4178,3.346,-0.5878;1.2098,2.9079,-2.1614;1.4231,2.7465,-3.1049;3.5744,3.2963,-2.4134;4.5634,3.1116,-1.9665;3.441,4.3803,-2.4866;3.4099,2.7341,-3.7634;3.9092,1.8533,-3.861;3.784,3.3627,-4.4686)|",4.95791435611
"[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(6.4443,-0.1611,-1.3067;7.2744,0.0233,-1.8406;7.3303,1.3003,-2.2316;8.2379,1.7128,-2.924;6.1877,2.2233,-1.7813;6.5112,3.232,-2.0478;5.3061,2.0001,-2.4005;5.8,2.1614,-0.2844;5.4956,3.1631,0.0432;6.6756,1.8916,0.3191;4.6492,1.2007,0.0431;3.7422,1.5481,-0.4775;4.4373,1.2589,1.1219;4.9859,-0.1552,-0.3699;4.2594,-1.1615,-0.0674;4.6629,-2.3651,-0.5062;4.0034,-3.0545,-0.2402;2.9545,-1.1489,0.709;2.9293,-2.0236,1.3684;2.9202,-0.2643,1.3512;1.7046,-1.1356,-0.2045;0.8207,-1.0888,0.446;1.7054,-0.2062,-0.7875;1.5344,-2.3175,-1.1786;2.3596,-2.3374,-1.902;0.6162,-2.1333,-1.7532;1.4153,-3.6974,-0.5068;0.7735,-3.6051,0.3792;2.6905,-4.1855,-0.0547;2.9459,-4.8196,-0.7708;0.817,-4.7842,-1.4301;-0.0641,-4.3929,-1.959;0.4866,-5.6232,-0.8092;1.8813,-5.2887,-2.31;2.0175,-4.6869,-3.1192;1.668,-6.2193,-2.6568)|",6.13344619027
"[H]OC(=O)C([H])([H])C([H])([H])/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(3.1849,3.0055,3.3105;3.7607,3.8057,3.4441;3.8421,4.4869,2.2892;4.5266,5.4789,2.1805;2.9797,3.9587,1.1349;1.9527,4.3134,1.2989;3.3473,4.429,0.2196;2.9496,2.4286,0.9722;3.9712,2.0619,0.7966;2.3603,2.183,0.0785;2.4314,1.8176,2.1909;1.7767,0.7291,2.1958;1.3519,0.299,3.4161;0.8778,-0.5401,3.3133;1.3786,-0.1321,1.0117;2.0993,0.0158,0.2028;1.4564,-1.1899,1.3;-0.0483,0.158,0.4912;-0.0924,1.204,0.1631;-0.2252,-0.4625,-0.3932;-1.1633,-0.1267,1.5086;-1.1322,0.5895,2.3396;-1.0079,-1.1273,1.9396;-2.5754,-0.1219,0.9131;-3.2749,-0.3793,1.7316;-2.6456,-1.1042,-0.1061;-3.4234,-0.8356,-0.6374;-3.0176,1.2354,0.3403;-2.2999,1.559,-0.4209;-3.0333,1.9998,1.134;-4.3095,1.0391,-0.3363;-5.0666,1.0055,0.3448;-4.5138,1.8121,-0.9645)|",6.3130413316
"[H]OC(=O)C([H])([H])N(/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H])C([H])([H])[H] |(3.6118,0.497,3.5238;3.5028,0.4589,4.5156;4.5889,-0.1255,5.0436;4.7355,-0.262,6.2374;5.6763,-0.5827,4.0423;6.2596,-1.3621,4.5383;6.3451,0.2682,3.8649;5.2413,-1.0917,2.7311;4.4741,-0.0432,2.092;4.7934,0.2139,0.8728;4.0904,1.1818,0.2717;4.4411,1.3217,-0.6459;5.8666,-0.4669,0.053;6.434,0.3107,-0.4725;6.5389,-0.975,0.7469;5.3314,-1.5039,-0.9635;6.2038,-1.939,-1.4697;4.8598,-2.325,-0.4104;4.3404,-1.0137,-2.0369;3.409,-0.6675,-1.5701;4.0777,-1.8832,-2.6553;4.8809,0.0924,-2.9607;5.9277,-0.1278,-3.2066;4.8624,1.3798,-2.3173;4.0361,1.7886,-2.6766;4.0896,0.2375,-4.2802;3.9185,-0.7497,-4.7334;4.6899,0.8257,-4.9819;2.8642,1.0032,-4.0085;2.1266,0.41,-3.6347;2.504,1.4409,-4.8514;4.3857,-2.2778,2.8727;4.945,-3.0446,3.4181;4.1425,-2.6672,1.88;3.4451,-2.0714,3.4066)|",5.820515262194999
"[H]OC(=O)C([H])([H])[C@@]1([H])N=C(O[H])[C@](C([H])([H])[H])(C([H])([H])C([H])=C([H])[H])C1=O |(7.5347,-1.2831,-1.0653;8.3762,-1.118,-1.5586;8.7128,0.1822,-1.4881;9.6946,0.6149,-2.0437;7.8079,1.0759,-0.6265;8.1289,0.9655,0.4185;7.9879,2.1125,-0.9183;6.3054,0.7561,-0.7266;5.9921,0.8723,-1.7749;6.0154,-0.6128,-0.2813;5.1138,-0.5948,0.6239;4.7138,-1.7333,1.2148;3.9802,-1.5333,1.8218;4.5416,0.7556,1.0197;4.7183,1.0729,2.5153;4.5337,2.14,2.6752;5.7338,0.8472,2.8565;4.0139,0.5048,3.1323;3.05,0.9413,0.5721;2.7336,1.9267,0.9315;3.0099,0.9722,-0.5239;2.1177,-0.1369,1.0673;2.0914,-1.0548,0.4788;1.334,-0.0407,2.1456;0.6774,-0.8524,2.4469;1.3006,0.8618,2.7525;5.4355,1.6672,0.1537;5.4406,2.8752,0.1787)|",5.26268186867
"[H]OC(=O)C([H])([H])/N=C(/O[H])C([H])([H])C([H])([H])C([H])([H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(3.3042,3.4529,3.1136;3.4213,4.4202,2.9439;3.4184,4.5539,1.6114;3.5306,5.6146,1.0439;3.2625,3.2284,0.8446;4.1571,3.1186,0.2157;2.4072,3.351,0.1667;3.1029,2.1329,1.7836;2.8769,0.944,1.3996;2.7159,0.0159,2.382;2.5945,-0.86,1.9843;2.7151,0.435,-0.0175;3.1493,1.1538,-0.7183;3.2893,-0.4957,-0.1311;1.2356,0.1764,-0.3831;0.7995,-0.5308,0.3367;0.6754,1.1124,-0.2618;1.077,-0.3747,-1.8056;1.6877,-1.2791,-1.9205;1.4689,0.3479,-2.5362;-0.3637,-0.7634,-2.1944;-0.761,-1.4367,-1.4222;-0.3597,-1.4895,-3.4114;-0.4129,-0.7929,-4.0974;-1.3259,0.4325,-2.3393;-1.3339,1.0533,-1.43;-2.3354,0.0291,-2.4676;-0.9855,1.171,-3.567;-0.264,1.866,-3.3903;-1.7937,1.6746,-3.9221)|",6.449098256850001
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"[H]C([H])([H])OC(=O)[C@]1([H])N2C(=O)C([H])([H])C([H])([H])C([H])([H])[C@]2([H])OC1([H])[H] |(5.5541,-2.0245,0.8379;4.8386,-1.2741,0.501;4.3469,-0.7995,1.3537;5.3363,-0.5079,-0.0991;3.8753,-1.9892,-0.2917;2.895,-1.2313,-0.8144;2.8344,-0.0261,-0.7215;1.9096,-2.0463,-1.6542;0.9885,-1.4591,-1.6956;1.6864,-3.4031,-1.174;1.4056,-3.653,0.1436;1.1261,-2.7459,0.9186;1.5197,-5.1049,0.6064;2.3417,-5.1015,1.3344;0.6148,-5.3421,1.1754;1.7907,-6.1429,-0.4945;0.8518,-6.4131,-0.9968;2.179,-7.0654,-0.0498;2.7759,-5.5916,-1.5356;3.0093,-6.3362,-2.3038;3.7153,-5.2806,-1.0634;2.1388,-4.3778,-2.18;1.2727,-4.6868,-2.794;3.0342,-3.6415,-2.9845;2.4756,-2.335,-3.0723;3.2626,-1.6436,-3.3786;1.6629,-2.3057,-3.8136)|",6.906249525690001
"[H]C1=C(C([H])([H])[H])C([H])([H])[C@]([H])(C([H])([H])[H])/C(=C(/[H])N2C([H])([H])C([H])([H])C([H])([H])C2([H])[H])C1([H])[H] |(2.8347,-2.2308,-0.3416;3.2031,-1.2067,-0.2726;2.3878,-0.2443,0.1757;0.9776,-0.5264,0.6256;0.7335,-1.5917,0.5576;0.2473,0.031,0.0218;0.8234,-0.2097,1.6672;2.8527,1.1938,0.2805;3.086,1.4173,1.3329;2.0246,1.8673,0.013;4.0853,1.5249,-0.5983;4.5062,2.4632,-0.2228;3.6738,1.7355,-2.0703;2.9597,2.5635,-2.1613;3.1953,0.8388,-2.4797;4.5442,1.9666,-2.6946;5.1179,0.429,-0.4328;6.3847,0.6248,-0.0213;6.9995,-0.2567,0.191;7.0475,1.8436,0.1694;8.2752,1.8393,0.9782;8.0662,2.1894,2.0002;8.6794,0.8205,1.0675;9.2439,2.7876,0.253;9.9298,3.2973,0.9368;9.8486,2.2269,-0.4705;8.2986,3.7439,-0.4901;8.7711,4.2518,-1.3366;7.9192,4.512,0.195;7.1552,2.8201,-0.9279;7.3931,2.3158,-1.88;6.2198,3.3694,-1.0768;4.6276,-0.9759,-0.7142;4.6976,-1.2056,-1.7931;5.292,-1.7071,-0.23)|",5.8477266472450005
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"[H]OC([H])([H])C([H])([H])[C@]([H])([C@@]([H])(O[H])N(OC([H])([H])[H])C([H])([H])[H])[C@]([H])(O[H])C([H])([H])O[H] |(5.7833,4.9464,3.3919;6.2511,4.0993,3.4421;5.3033,3.0606,3.2131;4.4713,3.1065,3.931;4.8589,3.1413,2.2064;6.055,1.7372,3.3333;6.5283,1.6944,4.3221;6.8838,1.7666,2.6143;5.2111,0.4735,3.0739;4.6342,0.6338,2.154;4.1748,0.1424,4.1763;3.953,-0.9315,4.1397;4.6485,0.3774,5.4858;4.8787,1.3189,5.5525;2.9169,0.9121,3.9921;2.372,0.4397,2.7301;1.8337,1.536,2.0082;1.0401,2.0453,2.5714;1.404,1.1045,1.0989;2.606,2.2684,1.741;1.9185,0.5492,5.0052;1.7045,-0.5301,5.0046;0.9959,1.0986,4.7994;2.2938,0.8399,5.9873;6.1214,-0.7437,2.7795;6.7743,-0.4819,1.9317;5.3511,-1.9068,2.4397;4.8334,-1.7112,1.6434;7.0263,-1.21,3.9211;6.4156,-1.4728,4.7969;7.7094,-0.4085,4.2166;7.8316,-2.2992,3.4991;7.2123,-2.9317,3.0977)|",7.877695971975
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"[H]OC(=O)[C@]1([H])C([H])([H])C(=O)O[C@]1([H])C(=O)OC([H])(C([H])([H])[H])C([H])([H])[H] |(5.1117,1.6273,-2.6185;5.9198,1.9616,-3.0674;7.0297,1.5445,-2.4322;8.1189,1.9772,-2.7238;6.8717,0.4525,-1.3618;7.8964,0.2213,-1.0609;6.185,-0.8475,-1.8106;6.8486,-1.5389,-2.3337;5.3128,-0.6709,-2.4488;5.7097,-1.4644,-0.5022;5.3569,-2.5869,-0.2776;5.7417,-0.504,0.4888;6.1089,0.7835,-0.0475;6.7397,1.2797,0.6907;4.7909,1.5516,-0.1978;4.049,1.4363,-1.1649;4.5386,2.2897,0.8712;3.2574,3.0186,0.9297;3.0231,3.3248,-0.0931;2.1827,2.0777,1.4583;1.228,2.6106,1.5237;2.4445,1.7135,2.4573;2.0498,1.2215,0.7914;3.5162,4.2275,1.8146;2.6078,4.8354,1.8814;4.3169,4.8501,1.4041;3.7986,3.9174,2.8262)|",6.11711935924
"[H]O/N=C1\C2=C(SC([H])([H])[C@@]1([H])C([H])([H])[H])C([H])([H])C([H])([H])N([H])C2([H])[H] |(3.6437,-3.838,1.3784;3.8435,-3.3028,0.5952;3.0569,-2.155,0.83;3.1411,-1.2627,-0.0982;3.8827,-1.3165,-1.373;4.2674,-0.1818,-2.013;4.0221,1.4796,-1.4467;3.2202,1.2136,0.1835;4.003,1.1237,0.9431;2.6485,2.1253,0.3847;2.3123,-0.0214,0.2077;1.9607,-0.135,1.2395;1.086,0.1024,-0.7112;0.492,0.9834,-0.44;0.4491,-0.7822,-0.6082;1.3733,0.1964,-1.7622;4.9513,-0.1949,-3.3661;4.2377,0.1662,-4.1189;5.795,0.5072,-3.3652;5.4303,-1.5956,-3.7481;6.3574,-1.8321,-3.1897;5.6733,-1.6268,-4.8158;4.3462,-2.5301,-3.4709;4.5662,-3.4448,-3.8592;4.1405,-2.6709,-2.0296;3.2751,-3.3199,-1.8645;4.9933,-3.1572,-1.5248)|",4.775598076275001
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"[H]OC(=O)[C@]1([H])C([H])([H])C(=O)O[C@]1([H])C(=O)OC([H])([H])C([H])([H])C([H])([H])[H] |(6.077,-4.7032,3.3236;5.6047,-5.5269,3.0984;4.3034,-5.4855,3.4639;3.525,-6.3109,3.057;3.873,-4.4346,4.4939;4.0414,-4.9052,5.4724;2.3991,-4.0327,4.3811;1.7094,-4.7306,4.8568;2.0919,-3.922,3.3355;2.3457,-2.6741,5.0614;1.3994,-2.0634,5.4671;3.6322,-2.1859,5.1927;4.5771,-3.0405,4.5582;5.4829,-3.0546,5.1686;4.9089,-2.4757,3.1748;4.1308,-1.8958,2.4599;6.1933,-2.775,2.8593;6.6549,-2.3877,1.5272;7.4404,-3.1109,1.2956;5.8255,-2.5137,0.8273;7.1812,-0.9589,1.5231;6.3674,-0.2817,1.8054;7.9637,-0.8651,2.286;7.7331,-0.5711,0.1462;8.1116,0.4557,0.1588;6.9573,-0.6299,-0.6259;8.5588,-1.2265,-0.1557)|",6.723933245855
"[H]OC(=O)[C@]1([H])C([H])([H])C(=O)O[C@]1([H])C(=O)OC([H])([H])C([H])([H])[H] |(5.4543,3.1393,-2.5998;5.324,3.6954,-1.8137;5.4252,2.9569,-0.6839;5.2699,3.4665,0.3986;5.8055,1.493,-0.8668;6.8966,1.4674,-0.996;5.1509,0.7115,-2.0167;5.6824,0.7538,-2.9715;4.1082,1.0074,-2.1697;5.1579,-0.7267,-1.5005;5.0022,-1.7403,-2.1204;5.3985,-0.7219,-0.1507;5.4631,0.6205,0.3661;6.2278,0.6451,1.1412;4.0876,0.9602,0.9528;3.0988,1.0868,0.2595;4.1303,1.04,2.2832;2.8919,1.3971,2.96;2.0576,0.9546,2.4116;2.9786,0.9232,3.9401;2.7603,2.9075,3.069;1.8464,3.1572,3.6202;2.705,3.3657,2.0779;3.6151,3.3356,3.6012)|",6.979720265325
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"[H]C([H])([H])OC(=O)[C@@]1([H])OC(=O)C([H])([H])[C@@]1([H])C(=O)N(OC([H])([H])[H])C([H])([H])[H] |(1.8102,4.3627,-1.519;2.5965,4.3381,-0.764;3.0962,5.3076,-0.6949;2.186,4.0746,0.2139;3.52,3.3251,-1.2007;4.6004,3.1562,-0.4027;4.8592,3.8274,0.561;5.483,2.0637,-1.003;5.9385,2.4904,-1.9076;6.532,1.7455,-0.0881;6.8168,0.4127,-0.1013;7.6857,-0.074,0.5688;5.9088,-0.2876,-1.1094;6.499,-0.4958,-2.0099;5.5704,-1.2445,-0.7059;4.7837,0.7308,-1.3902;4.4842,0.7437,-2.4367;3.5739,0.4382,-0.5042;3.4842,0.8678,0.6386;2.6257,-0.4288,-1.0114;2.5971,-0.6032,-2.4103;2.8695,-1.9662,-2.7519;2.1304,-2.6451,-2.3106;3.8752,-2.2652,-2.4358;2.793,-2.005,-3.8412;1.311,-0.541,-0.3957;1.4253,-0.3175,0.6644;0.9295,-1.5587,-0.5169;0.609,0.167,-0.8521)|",6.821894232035
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"[H]OC(=O)/C([H])=C(\[H])C1=C([H])N=C(S[C@]([H])(C([H])([H])[H])C([H])([H])C([H])([H])[H])N=C1[H] |(7.0193,-9.698,1.8241;7.4928,-9.7377,2.6709;7.4512,-8.5279,3.2975;7.9939,-8.3886,4.37;6.7158,-7.4535,2.5816;6.2615,-7.68,1.6187;6.6296,-6.2286,3.134;7.1216,-6.1062,4.0987;5.9624,-5.0512,2.6019;5.2504,-4.9969,1.3859;5.154,-5.8793,0.7546;4.6596,-3.9001,0.935;4.7675,-2.7996,1.7078;3.9737,-1.3817,1.0244;4.2644,-0.0709,2.3096;5.2819,-0.2363,2.6755;3.2743,-0.2135,3.4675;3.4762,0.5551,4.2247;3.3685,-1.1917,3.946;2.2416,-0.0906,3.123;4.1708,1.3021,1.6208;3.2021,1.3923,1.1095;4.1566,2.0554,2.4204;5.3119,1.6198,0.6496;5.201,2.6303,0.2412;5.3361,0.9186,-0.1908;6.2837,1.5684,1.1553;5.4057,-2.7209,2.8836;5.9911,-3.8397,3.3093;6.5117,-3.7768,4.2649)|",4.01095815637
"[H]C1=C(NC(=O)OC([H])([H])[H])ONN1C([H])(C([H])([H])[H])C([H])([H])[H] |(3.3757,-1.1269,2.2107;3.3361,-0.0534,2.2116;3.7265,0.8658,3.2151;4.2319,0.8353,4.4076;4.4917,-0.4209,4.908;4.3078,-1.5093,4.3656;5.0097,-0.3148,6.1549;5.3297,-1.5578,6.7827;5.7326,-1.2945,7.7621;6.0737,-2.1114,6.2016;4.4384,-2.183,6.8935;3.4461,2.1107,2.6619;2.9177,1.987,1.4024;2.8779,0.6977,1.1953;2.3452,0.2052,-0.1059;2.4203,-0.8828,-0.0254;0.8772,0.6132,-0.2509;0.4711,0.1755,-1.1683;0.7795,1.7006,-0.3104;0.2827,0.2541,0.5948;3.2335,0.7021,-1.249;2.8857,0.2655,-2.1905;4.2756,0.4056,-1.096;3.1881,1.7914,-1.3316)|",4.12524597358
"[H]C1=C(NC(=O)C(F)(F)F)ONN1C([H])(C([H])([H])[H])C([H])([H])[H] |(2.1406,-2.5217,-1.2181;2.8166,-1.8242,-1.6779;3.6093,-1.9653,-2.8339;3.845,-2.8765,-3.7371;3.1712,-4.0542,-3.5824;2.3725,-4.376,-2.7074;3.521,-5.0612,-4.7015;3.1931,-4.5669,-5.9129;4.8422,-5.3316,-4.709;2.8625,-6.2167,-4.5342;4.2754,-0.755,-2.9453;3.9312,0.0925,-1.9307;3.0742,-0.5851,-1.217;2.4667,0.0651,-0.0187;1.8746,-0.7336,0.4368;3.5679,0.5075,0.9461;3.1057,0.9115,1.852;4.1951,1.2849,0.5009;4.2046,-0.3359,1.2296;1.5455,1.2027,-0.467;1.0298,1.6126,0.4069;0.7925,0.8448,-1.1755;2.119,2.0048,-0.9407)|",4.15245735863
"[H]C1=C(NC(=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])ONN1N(C([H])([H])[H])C([H])([H])[H] |(-0.1963,2.6795,3.39;-0.1864,2.4117,2.3485;0.8104,1.809,1.548;1.9974,1.4199,1.8748;2.8623,0.8324,0.9505;2.5958,0.6262,-0.2273;4.2264,0.4525,1.5625;5.1099,-0.187,0.4813;6.0806,-0.4695,0.9068;5.2829,0.5053,-0.3483;4.6394,-1.0829,0.0648;3.9984,-0.5462,2.7181;4.9587,-0.8255,3.1694;3.5187,-1.4642,2.3574;3.3627,-0.1109,3.4942;4.9028,1.7281,2.1108;5.8743,1.4778,2.5553;4.2827,2.2037,2.876;5.0789,2.457,1.3104;0.2047,1.7226,0.2742;-1.0667,2.2371,0.2943;-1.231,2.6207,1.5364;-2.4439,3.2132,1.9481;-2.6474,4.493,1.2496;-3.5195,4.9712,1.7028;-2.8155,4.3726,0.1711;-1.7756,5.1306,1.4142;-3.5589,2.2675,1.7758;-4.4384,2.7209,2.24;-3.3237,1.3411,2.3049;-3.7752,2.0433,0.7229)|",3.948371970755
"[H]C1=C(NC(=O)C(=O)OC([H])([H])C([H])([H])[H])ONN1C([H])([H])[H] |(7.56,-2.6906,-1.8211;7.877,-2.2059,-0.9138;7.2178,-1.252,-0.1133;6.0543,-0.7044,-0.2774;5.54,0.1717,0.6535;6.0335,0.5274,1.7124;4.1465,0.7008,0.2711;3.2276,0.7747,1.0548;4.1038,1.111,-1.0124;2.8492,1.6784,-1.4612;2.4203,2.2729,-0.6503;3.132,2.3382,-2.2852;1.8852,0.5942,-1.9196;0.974,1.0517,-2.3226;1.6048,-0.0493,-1.081;2.3384,-0.0221,-2.7028;8.1285,-1.0063,0.9296;9.2623,-1.7506,0.7771;9.0596,-2.4366,-0.3193;10.1169,-3.3493,-0.7546;9.7236,-4.3674,-0.7784;10.9317,-3.274,-0.0354;10.458,-3.0539,-1.7487)|",4.04361181843
"[H]C1=C(NC(=O)C([H])([H])[H])ONN1C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H] |(4.8922,3.6953,-1.0942;5.0879,3.1665,-2.0084;5.5307,3.6599,-3.2577;5.8504,4.8009,-3.7856;5.7549,5.9088,-2.9536;5.3928,5.8907,-1.7752;6.1479,7.2002,-3.6436;5.5109,7.3662,-4.5198;7.178,7.1309,-4.011;6.0552,8.0391,-2.9515;5.6099,2.5233,-4.0614;5.2445,1.3998,-3.3717;4.9493,1.8385,-2.1752;4.4637,0.8592,-1.189;4.7491,-0.1207,-1.5779;5.0141,1.0416,-0.2616;2.9497,0.9615,-0.9768;2.6983,1.968,-0.6194;2.4503,0.8326,-1.9452;2.4458,-0.0897,0.0214;2.7018,-1.0926,-0.3469;2.9726,0.032,0.978;0.9341,0.0008,0.2534;0.5996,-0.7621,0.9642;0.3803,-0.1466,-0.6812;0.6526,0.9805,0.657)|",4.0817077575
"[H]C1=NC([H])=C(/C([H])=C(\[H])N(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(5.0485,-6.7288,0.5919;4.8839,-5.6659,0.4237;3.9904,-5.0692,1.2248;3.7679,-3.7665,1.0436;3.0673,-3.3121,1.7429;4.383,-2.9718,0.0518;4.0922,-1.5539,-0.1507;4.8196,-1.0041,-0.7423;2.9883,-0.9184,0.3129;2.2146,-1.4847,0.827;2.7095,0.4295,0.2441;1.3153,0.8438,0.2562;1.2368,1.8686,0.6349;0.745,0.1886,0.9214;0.8488,0.808,-0.7423;3.6307,1.3053,-0.4538;3.3554,2.3457,-0.2588;3.6308,1.1423,-1.5448;4.6474,1.1412,-0.0829;5.3318,-3.6271,-0.754;5.8561,-3.0689,-1.5269;5.5866,-4.9818,-0.5685;6.3145,-5.5023,-1.1849)|",4.5851183809250005
"[H]O/C1=C(C(\[H])=C(/[H])C([H])([H])[H])/C([H])=C2\C3=C(C(=O)N2C([H])([H])[H])C([H])=C([H])C([H])=C31 |(3.5146,-2.2955,1.5743;4.464,-2.5143,1.5707;5.1621,-1.3497,1.4314;4.5803,-0.082,1.5462;3.1482,0.0943,1.8525;2.6695,0.9323,1.3415;2.391,-0.6259,2.7016;2.8495,-1.4179,3.295;0.9343,-0.3704,2.9611;0.3229,-1.2538,2.7324;0.5593,0.4646,2.3598;0.7581,-0.1358,4.0194;5.3552,1.1126,1.315;4.8553,2.0739,1.3919;6.6912,1.0008,1.0301;7.2594,-0.289,0.947;8.6213,-0.2053,0.6357;8.9391,1.2534,0.5191;9.9963,1.8057,0.264;7.7185,1.9198,0.7696;7.5917,3.3595,0.7537;7.2621,3.7381,1.7291;6.877,3.6853,-0.0121;8.5774,3.7683,0.5229;9.3517,-1.3673,0.4873;10.4099,-1.3472,0.2439;8.6696,-2.5998,0.6594;9.2277,-3.5248,0.5445;7.3141,-2.6706,0.9661;6.8331,-3.636,1.0859;6.5538,-1.4827,1.1238)|",3.4504036243400007
"[H]C1=C(C([H])([H])[H])N([H])C(/N=C(\[H])OC([H])([H])C([H])([H])[H])=C1C#N |(9.208,-3.9653,2.2437;8.4062,-3.2404,2.2512;8.5468,-1.873,2.2716;9.7674,-1.0112,2.2941;10.6633,-1.6372,2.2667;9.821,-0.3947,3.2014;9.8098,-0.3334,1.4313;7.272,-1.3408,2.274;7.0318,-0.3597,2.2865;6.3132,-2.3212,2.2558;4.9969,-1.9104,2.2535;4.0271,-2.7461,2.2533;4.1049,-3.834,2.2552;2.7479,-2.3666,2.2497;2.4699,-0.9489,2.2431;2.9249,-0.4981,3.1317;2.9467,-0.5016,1.3644;0.9621,-0.7845,2.2247;0.7053,0.2804,2.2212;0.5079,-1.2464,3.1069;0.5296,-1.247,1.3319;7.0051,-3.5434,2.2406;6.4278,-4.8351,2.2173;5.9435,-5.8963,2.1987)|",4.574233826905
"[H]C(=O)/C([H])=C1/C([H])=C(C([H])([H])[H])C([H])([H])C(C([H])([H])[H])(C([H])([H])[H])C1([H])[H] |(7.4417,1.6088,0.1294;6.8428,2.4566,-0.2648;7.4002,3.4981,-0.5815;5.4017,2.2526,-0.3739;4.8587,3.1107,-0.7671;4.7112,1.1292,-0.0373;3.2649,1.0981,-0.2045;2.7845,2.0267,-0.5087;2.5117,-0.0057,-0.0105;1.0172,0.0122,-0.1672;0.64,1.0143,-0.3924;0.523,-0.3428,0.748;0.6991,-0.6651,-0.9721;3.1375,-1.3239,0.3827;2.6057,-2.1455,-0.119;2.9641,-1.483,1.4601;4.6509,-1.4232,0.0835;4.8866,-1.6239,-1.4268;4.4354,-2.5639,-1.7667;5.9596,-1.6714,-1.6484;4.4582,-0.8102,-2.0204;5.2489,-2.6162,0.847;4.7609,-3.5535,0.553;5.1278,-2.5004,1.9311;6.3205,-2.7203,0.638;5.3304,-0.1175,0.5628;5.2224,-0.0537,1.6566;6.4041,-0.17,0.3613)|",4.44634031717
"[H]C([H])([H])OC(=O)C1(C([H])([H])[H])C([H])([H])OC(C([H])([H])[H])(C([H])([H])[H])OC1([H])[H] |(3.8149,2.2654,4.8557;3.8868,1.2134,4.5783;4.6187,0.6978,5.2059;2.9177,0.7195,4.6836;4.306,1.2053,3.2036;4.4293,-0.0172,2.6441;4.2372,-1.047,3.2533;4.9325,0.0539,1.1956;6.4738,0.0524,1.2388;6.8869,0.0679,0.2237;6.847,-0.8432,1.7468;6.8516,0.9305,1.7737;4.4129,1.2965,0.453;4.9375,1.3756,-0.5124;4.6052,2.2096,1.0187;3.0049,1.2407,0.2692;2.5268,0.0731,-0.4006;1.0187,0.0701,-0.1889;0.5738,0.9684,-0.6263;0.808,0.0529,0.8833;0.5746,-0.8151,-0.6526;2.8903,0.0943,-1.8937;2.4744,0.9912,-2.3625;2.4723,-0.7889,-2.386;3.9699,0.0946,-2.067;3.005,-1.1089,0.2367;4.4119,-1.175,0.4308;4.5889,-2.0875,1.0029;4.9444,-1.2524,-0.5298)|",7.306256885925
"[H]C([H])=C([H])C([H])([H])[C@]([H])(OC(=O)C([H])(OC([H])([H])[H])OC([H])([H])[H])C([H])([H])[H] |(1.326,1.0696,-2.7973;1.7547,0.9022,-1.8128;1.234,1.3596,-0.9733;2.8579,0.1728,-1.6443;3.3549,-0.266,-2.5087;3.5076,-0.1024,-0.3142;3.4722,-1.1799,-0.0942;2.9606,0.408,0.4884;4.9747,0.3394,-0.2548;5.0688,1.393,-0.5314;5.6651,-0.443,-1.2767;6.7051,0.1277,-1.9053;7.1856,1.2048,-1.6369;7.2322,-0.8148,-3.0118;7.5209,-1.7775,-2.5402;6.1843,-0.9995,-3.908;6.45,-1.9809,-4.9048;5.5384,-2.0685,-5.4994;6.678,-2.9547,-4.445;7.284,-1.6817,-5.5477;8.3313,-0.2662,-3.6807;9.5597,-0.35,-2.9732;10.3342,0.0053,-3.6565;9.7858,-1.3893,-2.686;9.5525,0.2813,-2.0775;5.6353,0.0921,1.0962;5.1152,0.6547,1.8795;6.6784,0.4206,1.0764;5.6027,-0.9719,1.3557)|",6.933460910740001
"[H]OC(=O)C1=C([H])C([C@@]2([H])N(C([H])([H])[H])C(=O)[C@]([H])(O[H])C2([H])[H])=C([H])N=C1[H] |(1.3186,-1.7993,-3.9094;1.4499,-1.6655,-4.8619;1.5611,-0.3406,-5.1491;1.545,0.0351,-6.2959;1.7063,0.5948,-3.9859;2.1811,0.2108,-2.7299;2.4831,-0.815,-2.5247;2.301,1.1651,-1.7154;2.8585,0.8186,-0.3504;2.6344,1.6491,0.336;2.3087,-0.4251,0.1995;0.8843,-0.5956,0.4287;0.4864,0.2159,1.0534;0.7457,-1.5458,0.9474;0.3336,-0.6075,-0.5171;3.231,-1.1579,0.9066;2.9954,-2.1208,1.6207;4.6028,-0.488,0.7471;5.3762,-1.2286,0.5047;4.899,0.1842,1.9688;4.8062,-0.475,2.6781;4.388,0.5416,-0.3563;4.9709,1.4495,-0.1855;4.6821,0.1205,-1.323;1.9185,2.4743,-2.0239;1.9792,3.249,-1.2597;1.476,2.8714,-3.2241;1.3843,1.948,-4.1812;1.0326,2.274,-5.1564)|",5.01233712621
"[H]OC([H])([H])[C@@]1([H])O[C@@](O[H])(C([H])([H])F)[C@]([H])(O[H])[C@]([H])(O[H])[C@]1([H])O[H] |(-0.0972,-0.138,1.2071;0.3237,-0.8305,0.6693;1.2312,-0.1823,-0.1925;2.0918,0.2329,0.3612;1.6338,-0.9466,-0.8659;0.5454,0.8987,-1.0478;-0.1487,0.3963,-1.7271;-0.3342,1.7198,-0.2421;0.1628,2.8409,0.4678;-0.9699,3.6388,0.7472;-1.6608,3.0363,1.0722;0.7978,2.4326,1.8039;0.9471,3.3184,2.4277;1.7446,1.8996,1.6878;-0.0982,1.5831,2.4643;1.0875,3.7173,-0.4031;1.5649,4.4857,0.2225;0.2999,4.342,-1.4166;-0.5508,4.5571,-0.99;2.1735,2.8673,-1.088;2.8212,2.4452,-0.3095;3.002,3.6533,-1.9139;2.5156,3.7377,-2.7544;1.5226,1.7262,-1.8969;2.314,1.0598,-2.2591;0.9176,2.2647,-3.068;0.3072,2.9616,-2.7526)|",8.07089680583
"[H]OC1=NC2=NC(=O)N(C([H])([H])[H])[C@@]2([H])C(=O)N1C([H])([H])[H] |(6.9974,3.3439,-0.6247;6.1538,3.8258,-0.5311;5.2205,2.8873,-0.3377;5.5603,1.6386,-0.3276;4.5337,0.7514,-0.1268;4.6252,-0.4266,0.3782;3.2789,-0.9163,0.5025;2.9638,-2.0191,0.8893;2.3797,0.0945,0.1268;0.974,-0.1681,-0.12;0.3633,0.6529,0.2625;0.7236,-1.0933,0.4016;0.7684,-0.2997,-1.1927;3.1135,1.1696,-0.4624;2.9934,1.213,-1.5629;2.8415,2.587,0.0294;1.7704,2.9932,0.4224;3.9627,3.4317,-0.1173;3.8065,4.8754,0.1209;2.7434,5.0584,0.2681;4.1744,5.4372,-0.7394;4.359,5.1791,1.0136)|",4.696685059630001
"[H]OC(=O)[C@]1(C([H])([H])[H])C([H])([H])O[C@]([H])(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])OC1([H])[H] |(0.7703,1.9206,2.3964;0.2678,2.728,2.641;1.1223,3.7492,2.8374;0.7169,4.8626,3.0791;2.6337,3.4296,2.7207;3.4255,4.4449,3.5534;4.504,4.2916,3.4313;3.1827,4.3509,4.617;3.171,5.4629,3.2507;3.0471,3.5274,1.2308;4.1476,3.5025,1.1635;2.691,4.459,0.7836;2.4934,2.467,0.4563;2.8642,1.1985,0.9408;3.9675,1.1317,0.9772;2.2913,0.0952,0.0338;2.7628,-1.2709,0.5676;2.3833,-2.0742,-0.0737;2.4043,-1.4477,1.586;3.8578,-1.3406,0.5749;2.8416,0.3127,-1.3892;2.4882,-0.4858,-2.0511;3.9388,0.2935,-1.4009;2.5142,1.2717,-1.7995;0.7519,0.1546,0.014;0.3597,-0.577,-0.7014;0.3971,1.1456,-0.2833;0.3287,-0.0852,0.9956;2.3827,1.0262,2.2769;2.9587,1.9909,3.1712;2.5537,1.7693,4.1628;4.0495,1.8491,3.2023)|",7.494015442769999
"[H]NC1=NC2=NC([H])C([C@@]([H])(O[H])[C@@]([H])(O[H])C([H])([H])[H])=N[C@@]2([H])C(O[H])=N1 |(8.0797,-3.0627,3.9376;7.408,-3.5149,3.3091;6.6641,-2.602,2.8112;6.8642,-1.2359,3.1669;5.8874,-0.4272,2.9819;6.0635,0.9336,3.2669;5.2079,1.7465,2.7533;5.3111,2.8044,2.98;4.1179,1.3285,1.8298;3.3613,2.3844,1.0323;2.4501,1.9066,0.6588;2.9788,3.452,1.8843;3.68,4.1232,1.7984;4.1528,2.934,-0.1705;3.4587,3.592,-0.7154;5.1928,3.727,0.4233;5.652,4.2182,-0.275;4.7067,1.8736,-1.1153;3.9033,1.241,-1.5087;5.4367,1.2348,-0.6099;5.2037,2.3483,-1.9694;3.7848,0.0976,1.6978;4.5328,-0.8811,2.4875;3.8984,-1.1402,3.3535;4.7225,-2.1611,1.678;3.7782,-2.408,0.7624;3.2246,-1.6006,0.7096;5.6913,-2.9649,1.8596)|",3.63544104268
"[H]OC(=O)[C@@]1([H])C(C([H])([H])[H])(C([H])([H])[H])N(O[H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[C@@]1([H])N([H])[H] |(6.4477,1.2787,-2.461;7.1325,0.6241,-2.2493;6.6324,-0.3381,-1.4303;7.3833,-1.1744,-0.9916;5.107,-0.2845,-1.2225;4.6716,-0.3756,-2.229;4.5202,-1.5003,-0.4304;4.7256,-2.7677,-1.2916;5.7888,-2.9379,-1.4698;4.2113,-2.6631,-2.2541;4.3195,-3.6424,-0.7775;5.1535,-1.7733,0.9524;6.1962,-2.0668,0.8276;4.6054,-2.5882,1.4323;5.1259,-0.9219,1.6345;3.0392,-1.2728,-0.3999;2.4407,-2.3882,0.2997;1.9439,-2.8346,-0.4036;2.467,-0.029,0.1822;0.9641,-0.0375,-0.1707;0.5139,0.9346,0.0607;0.4328,-0.8024,0.4024;0.8246,-0.2393,-1.2382;2.5817,0.1143,1.7186;1.9095,0.9096,2.0619;3.5848,0.375,2.0578;2.2809,-0.8167,2.206;3.1227,1.1463,-0.5725;2.7042,1.1653,-1.5879;2.8243,2.0893,-0.0954;4.6596,1.113,-0.7169;4.8728,1.8369,-1.5214;5.4831,1.5572,0.4095;5.4546,0.9023,1.1836;5.1492,2.4525,0.7615)|",6.446377118345
"[H]NC1=NC([H])[C@@]([H])(C([H])([H])C([H])([H])Cl)C(O[H])=N1 |(4.0942,-0.8543,4.2145;4.0864,0.0867,3.8086;3.8315,-0.0284,2.5628;3.6084,-1.3268,1.9907;3.0501,-1.3823,0.847;2.8625,-2.3683,0.4129;2.5805,-0.18,0.0559;2.8527,-0.3183,-0.9996;1.0276,-0.0939,0.1334;0.6124,-1.0116,-0.2988;0.7255,-0.0662,1.1886;0.3909,1.1194,-0.5347;0.6999,2.0573,-0.072;-0.6972,1.0484,-0.513;0.8488,1.2605,-2.2996;3.278,1.0461,0.607;3.3204,2.1455,-0.1787;2.9303,1.9527,-1.0501;3.8124,1.1222,1.7612)|",5.29533553073
"[H]/N=C(/O[H])C([H])([H])/C(=C(/C#N)C(=O)O[H])N([H])[H] |(-0.1855,0.45,-2.0251;0.6106,-0.0902,-2.3644;1.3745,-0.4213,-1.4028;2.479,-1.1378,-1.6512;3.0113,-1.2333,-0.8272;1.1395,-0.0919,0.0858;2.0609,0.2956,0.5236;0.371,0.6837,0.1603;0.6736,-1.3005,0.8674;1.5143,-2.1468,1.5865;0.9231,-3.1873,2.3556;0.4361,-4.0319,2.9981;2.982,-2.0253,1.6019;3.634,-1.3379,0.8241;3.6432,-2.7305,2.5383;3.0285,-3.2193,3.1152;-0.6566,-1.4995,0.8601;-1.251,-0.9439,0.2628;-1.0639,-2.3257,1.281)|",5.064038757805001
"[H]C1=C([H])SC(/C([H])=N/N(C([H])([H])[H])C([H])([H])[H])=N1 |(6.7973,5.1649,0.7963;5.9538,4.54,0.524;4.9296,4.9251,-0.2933;4.7841,5.8751,-0.7886;3.7877,3.6287,-0.4694;4.8065,2.6515,0.5934;4.4748,1.2853,0.9492;5.1712,0.8066,1.6377;3.4139,0.7329,0.4586;3.039,-0.5088,0.8142;2.1305,-1.1321,-0.1373;1.5367,-1.8982,0.371;2.6616,-1.5993,-0.9822;1.4666,-0.3592,-0.5273;3.946,-1.3777,1.546;3.4534,-2.3382,1.7097;4.1857,-0.9437,2.523;4.8893,-1.5471,1.0003;5.8838,3.2629,1.0224)|",4.21776468275
"[H]C1=C([H])C2=C(C([H])=C1[H])N([H])C(C1=N[C@@]([H])(N([H])[H])N=N1)=N2 |(3.067,-1.1315,0.1515;2.1077,-0.6272,0.0777;0.9372,-1.3574,0.2192;0.9462,-2.427,0.4025;-0.2837,-0.67,0.1179;-0.2872,0.7319,-0.1232;0.8919,1.4713,-0.2671;0.8803,2.5415,-0.4512;2.0844,0.7667,-0.1617;3.0249,1.3002,-0.2662;-1.6184,1.0718,-0.165;-2.0365,1.9808,-0.3098;-2.3366,-0.0921,0.0463;-3.7863,-0.0343,0.0587;-4.6443,-0.9668,0.2195;-5.9373,-0.2935,0.1149;-6.4126,-0.615,-0.8236;-6.8681,-0.4832,1.1939;-6.4098,-0.2815,2.0816;-7.1515,-1.4601,1.2228;-5.6309,1.1646,-0.0816;-4.3939,1.3002,-0.1386;-1.5721,-1.151,0.2178)|",3.692584951285
"[H]OC(=O)[C@]1([H])N([H])OC(=O)[C@@]2([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[C@@]21[H] |(3.1871,1.2313,-1.4856;2.6275,1.4384,-2.268;1.3561,1.469,-1.8438;0.4253,1.6169,-2.5996;1.2094,1.3646,-0.305;1.3051,2.3934,0.0736;2.3962,0.6743,0.2196;2.4468,0.7851,1.2334;2.3549,-0.7696,0.0396;1.1603,-1.4779,0.021;1.254,-2.6731,0.1237;-0.137,-0.7326,-0.2434;-0.2397,-0.728,-1.3406;-1.3475,-1.477,0.3475;-1.3692,-2.5002,-0.0377;-1.2225,-1.5555,1.4368;-2.6514,-0.7328,0.0246;-2.8293,-0.769,-1.0598;-3.4967,-1.2479,0.4967;-2.601,0.7346,0.4802;-2.5429,0.7717,1.5784;-3.5274,1.25,0.1997;-1.3954,1.4723,-0.1236;-1.3459,2.5003,0.2602;-1.497,1.5433,-1.2103;-0.0885,0.7349,0.2151;0.008,0.7377,1.3147)|",6.854547894095001
"[H]OC1=NC(=O)N(C([H])([H])[H])[C@@]([H])(C(=O)OC([H])([H])C([H])([H])[H])C1([H])[H] |(5.3617,-3.0075,-1.7865;5.1291,-2.2851,-2.3908;5.3589,-1.0996,-1.7888;5.0118,-0.0444,-2.4151;5.309,1.2281,-1.8649;5.2629,2.2272,-2.5608;5.67,1.2879,-0.5211;5.8552,2.5962,0.0939;6.6361,2.5315,0.8603;4.9307,2.9634,0.5553;6.1632,3.294,-0.6837;5.5239,0.124,0.3273;6.1432,0.27,1.2214;4.0901,-0.1422,0.8488;3.7467,-1.2215,1.2868;3.3152,0.9464,0.7878;1.9388,0.804,1.2388;1.9229,0.1307,2.0989;1.6603,1.8113,1.5557;1.0488,0.3015,0.1138;0.0056,0.287,0.4494;1.3286,-0.7143,-0.179;1.1218,0.9543,-0.7613;6.0177,-1.1152,-0.4272;5.7595,-2.0145,0.1391;7.1073,-1.0755,-0.5476)|",5.68717947545
"[H]OC1=NC(=O)/C(=C(/N([H])[H])C([H])([H])[H])C1([H])[H] |(6.7687,0.8813,2.5337;6.0879,1.2677,3.1077;4.8821,1.1135,2.5357;3.8192,1.5383,3.1062;2.7169,1.2184,2.2397;1.5668,1.5029,2.523;3.2303,0.5315,1.0261;2.5074,0.0622,-0.0254;3.1058,-0.6089,-1.0715;2.5829,-0.6835,-1.9334;4.1032,-0.5095,-1.2014;1.0097,0.171,-0.1278;0.6022,0.6578,0.7571;0.5675,-0.8276,-0.234;0.7317,0.7512,-1.0182;4.725,0.43,1.1888;5.3087,0.9594,0.4173;5.1049,-0.6033,1.2288)|",5.11029811239
"[H]O/N=C1/C([H])([H])C(=O)C([H])([H])[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])C1([H])[H] |(2.0834,-4.4475,1.3727;2.4145,-3.6912,0.8646;2.9965,-2.8998,1.8746;3.4477,-1.7878,1.4266;4.1067,-0.8799,2.4376;5.1877,-0.8259,2.2236;3.985,-1.2586,3.4544;3.5763,0.5523,2.3546;3.3828,1.2201,3.3509;3.3665,1.1011,0.9478;2.8259,2.0484,1.0246;4.3709,1.3303,0.5587;2.6842,0.1273,-0.0441;2.8716,0.5203,-1.051;1.1706,0.0252,0.1089;0.4057,-0.3173,-1.0169;0.9043,-0.468,-1.9726;-0.9773,-0.4641,-0.9348;-1.5458,-0.7258,-1.8234;-1.6294,-0.2667,0.2844;-2.708,-0.378,0.3534;-0.8847,0.0828,1.4094;-1.3796,0.2473,2.3628;0.5023,0.2297,1.323;1.0497,0.5223,2.2131;3.3652,-1.2742,0.0133;4.3861,-1.1812,-0.3871;2.8263,-1.9836,-0.6155)|",5.64908353638
"[H]OC(NN([H])[H])[C@@]1([H])N([H])[C@]([H])(C(NN([H])[H])O[H])C([H])([H])C(=O)C1([H])[H] |(-0.6616,1.9348,-3.1308;-1.1021,2.6361,-2.6284;-0.8457,2.4761,-1.3036;-1.2285,3.4237,-0.5386;-0.9787,3.1794,0.8607;-1.7364,2.5819,1.1986;-1.1074,4.0869,1.3052;-0.1363,1.2132,-0.8298;0.6989,1.581,-0.226;-1.0169,0.4702,0.0839;-1.8113,0.0935,-0.4306;-0.3456,-0.6017,0.8294;-1.1221,-1.2429,1.2598;0.3939,-0.0489,2.0522;0.6495,-0.7373,3.0998;0.2041,-2.0974,3.0524;0.8203,-2.6403,2.4436;0.3812,-2.4518,3.9898;0.84,1.2315,2.0514;0.2129,1.8597,1.6087;0.5342,-1.5155,-0.0921;1.2741,-2.0953,0.465;-0.1416,-2.2354,-0.5797;1.269,-0.7921,-1.2112;2.409,-1.0644,-1.5261;0.4736,0.304,-1.9205;-0.3232,-0.162,-2.5219;1.1626,0.8354,-2.5866)|",4.6803582286
"[H]C1=C2C(=O)C([H])=C(N([H])[H])N=C2[C@]([H])(OC([H])([H])[H])C([H])=C1[H] |(2.9656,1.3247,0.336;3.9628,1.5201,0.724;4.7114,0.4384,1.0805;4.1455,-0.946,0.9807;3.0403,-1.148,0.464;4.9943,-1.9654,1.5379;4.6443,-2.9927,1.5112;6.2137,-1.6413,2.0831;7.0759,-2.57,2.605;6.716,-3.4808,2.8509;7.8181,-2.2065,3.1861;6.747,-0.3492,2.1371;6.0343,0.6216,1.6563;6.6467,2.0265,1.6423;7.3262,2.0301,0.763;7.383,2.3547,2.8035;8.7517,1.9663,2.7867;9.197,2.3818,3.6945;9.2714,2.3873,1.9113;8.8601,0.878,2.7833;5.6563,3.1266,1.3761;6.0196,4.1356,1.549;4.4223,2.8805,0.8969;3.7443,3.696,0.6618)|",2.89529136932
"[H]C1C(=O)C2=C([H])/C([H])=C([H])/C(C([H])[H])=C\2N=C1N([H])[H] |(4.9956,4.572,-0.0666;4.4924,3.6101,-0.0568;5.3033,2.4232,-0.0247;6.5409,2.4109,0.0005;4.5172,1.1492,-0.0194;5.1532,-0.0594,0.0067;6.2405,-0.0476,0.0206;4.4379,-1.3064,0.0168;4.9967,-2.2373,0.0365;3.0806,-1.3129,0.0029;2.5263,-2.2478,0.0115;2.3171,-0.0754,-0.0249;0.9637,-0.0996,-0.0389;0.4182,-1.0388,-0.0299;0.3952,0.8234,-0.0607;3.0665,1.2115,-0.0389;2.3986,2.3347,-0.068;3.1179,3.5223,-0.0802;2.3,4.6233,-0.1558;2.6824,5.5237,0.0941;1.3313,4.4664,0.0846)|",2.359227083835
"[H]/N=C1/N=C(O[H])[C@]([H])(C(=O)O[H])C([H])=C1C(=O)O[H] |(-3.0728,1.1852,0.6284;-2.768,0.2377,0.3915;-1.5022,0.1983,0.1632;-0.9429,-1.0608,-0.0703;0.3196,-1.2341,0.0075;0.8202,-2.4596,-0.1693;1.7389,-2.4784,0.1806;1.3604,-0.1346,0.2486;2.0762,-0.1903,-0.5861;2.2155,-0.5124,1.4777;2.8388,-1.5522,1.4858;2.2474,0.3279,2.5166;1.6492,1.0839,2.3714;0.737,1.2234,0.2541;1.3848,2.0981,0.2561;-0.5927,1.3899,0.1712;-1.0967,2.8096,0.0596;-0.5136,3.7482,0.5447;-2.2282,2.9939,-0.6608;-2.5362,2.1504,-1.04)|",4.783761491789999
"[H]C1=C(Cl)/C2=N/C(=O)/C([H])=C(/C([H])([H])[H])[C@@]2([H])C(=O)C1=O |(4.4198,-4.6306,-1.9294;4.0454,-3.6689,-1.5945;4.8273,-2.8159,-0.8941;6.465,-3.2318,-0.5193;4.3468,-1.4816,-0.4474;5.2015,-0.5649,-0.1961;4.7434,0.74,0.1855;5.5667,1.6091,0.3966;3.2958,0.9707,0.3181;3.0117,1.9667,0.6444;2.3731,0.0231,0.0803;0.9006,0.2514,0.2685;0.7115,1.2305,0.7162;0.4638,-0.5173,0.9201;0.3768,0.1919,-0.6911;2.8306,-1.3485,-0.3402;2.5176,-2.0783,0.4291;2.194,-1.8952,-1.6403;1.3763,-1.3134,-2.3076;2.6865,-3.3091,-2.0277;1.9744,-4.0497,-2.678)|",3.67353698175
"[H]OC1=NC2=C(C(=O)C(O[H])=C([H])[C@@]2([H])Cl)C(C([H])([H])[H])=C1[H] |(4.6329,-3.0654,-0.1936;5.2134,-2.2882,-0.171;4.458,-1.1715,-0.064;5.1489,-0.0405,-0.0296;4.4593,1.098,0.0635;3.0548,1.1769,0.0938;2.3798,2.497,0.0984;1.1669,2.6365,0.1831;3.2559,3.706,-0.0448;2.5428,4.8498,-0.1679;3.1577,5.6002,-0.2102;4.5985,3.6236,-0.0497;5.2116,4.5189,-0.1312;5.3135,2.3406,0.1385;6.1586,2.2293,-0.5423;6.1598,2.3779,1.8098;2.3238,-0.0443,0.0711;0.8194,-0.1203,0.1152;0.4917,-1.1641,0.0837;0.3693,0.4218,-0.7215;0.4252,0.3526,1.0189;3.0556,-1.2226,-0.0012;2.5391,-2.1797,-0.0199)|",4.37014843903
"[H]C1=C([H])C(C([H])([H])[C@@]2([H])C(C([H])([H])[H])N=C(C([H])([H])[H])NC2=O)=C([H])C([H])=C1F |(3.9731,-2.1652,-4.2891;4.4757,-1.5058,-3.5892;3.7733,-0.5718,-2.8269;2.6946,-0.5013,-2.9462;4.4305,0.2809,-1.9295;3.6577,1.2916,-1.1082;2.7183,1.5418,-1.6134;4.2294,2.2208,-1.0144;3.326,0.8587,0.3647;2.7668,1.698,0.7983;2.4688,-0.3809,0.4515;1.0293,-0.2941,0.0296;0.5096,0.4877,0.5983;0.5345,-1.2532,0.1927;0.9438,-0.0222,-1.0298;2.9119,-1.5041,0.8991;4.2637,-1.5515,1.3238;4.7355,-2.9437,1.6105;4.1043,-3.3963,2.3847;5.7778,-2.9393,1.9311;4.6188,-3.5629,0.7126;5.0711,-0.5565,1.5097;4.6195,0.7276,1.1724;5.2647,1.72,1.4567;5.8263,0.1803,-1.8171;6.3584,0.8353,-1.1327;6.5458,-0.7463,-2.5695;7.6248,-0.8273,-2.4899;5.8554,-1.5773,-3.4446;6.5453,-2.4752,-4.179)|",4.266745175840001
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"[H]SC1=N[C@]([H])(C([H])([H])[H])[C@@]([H])(C([H])([H])[H])C(S[H])=N1 |(4.5939,1.2096,-4.1424;4.7178,-0.0795,-3.7647;3.9788,0.1633,-2.1603;3.616,1.3166,-1.7528;3.0389,1.3718,-0.4031;2.3053,2.1879,-0.4106;4.1414,1.733,0.604;3.7162,1.907,1.5998;4.6543,2.6438,0.2817;4.8874,0.9341,0.6796;2.2803,0.0726,-0.0308;2.1298,0.0507,1.0551;0.9055,-0.022,-0.7244;0.2688,0.808,-0.3987;0.396,-0.9603,-0.4818;1.0118,0.0371,-1.8119;3.1513,-1.1007,-0.4472;3.1008,-2.6358,0.4436;2.2551,-2.225,1.4122;3.9257,-1.0689,-1.469)|",4.94702980209
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"[H]C1=C([H])[C@]2([H])N(C(=O)C1([H])[H])C([H])([H])C([H])([H])[C@]2([H])C(=O)N1C([H])([H])C([H])([H])C([H])([H])C1([H])[H] |(-2.5146,0.2061,-3.6326;-2.7609,1.2249,-3.3379;-2.6137,1.6052,-2.069;-2.272,0.9031,-1.3116;-2.9832,2.9766,-1.5909;-3.8678,2.8785,-0.9344;-3.2994,3.9071,-2.6732;-3.3806,3.6147,-4.0043;-3.5354,4.4882,-4.8508;-3.2788,2.145,-4.4045;-4.2708,1.8158,-4.7495;-2.6413,2.1178,-5.2964;-3.1183,5.3029,-2.2516;-2.4623,5.7959,-2.9706;-4.0751,5.837,-2.2363;-2.4828,5.2024,-0.8481;-3.2392,5.3469,-0.0683;-1.704,5.9552,-0.7065;-1.9268,3.7622,-0.7484;-1.9317,3.4043,0.2849;-0.5296,3.6705,-1.3626;-0.2312,4.322,-2.3639;0.3792,2.8426,-0.7686;1.7414,2.7525,-1.3208;1.7278,2.1857,-2.2611;2.1177,3.7524,-1.5516;2.5295,2.0277,-0.222;3.3781,1.4629,-0.6188;2.9199,2.7519,0.5032;1.468,1.1362,0.4432;1.7403,0.8059,1.4501;1.2907,0.2429,-0.1679;0.2119,2.0228,0.4402;0.1736,2.6516,1.3422;-0.708,1.431,0.4055)|",6.386512071235
"[H]O[C@]1([H])/C([H])=C2/C3=C(N=C(OC([H])([H])[H])C([H])=C3[H])C([H])([H])[C@@]2([H])C([H])([H])[C@@]1([H])C([H])([H])[H] |(5.1259,2.6561,-1.3393;4.4228,2.8106,-0.6899;3.3295,3.4352,-1.3787;3.7292,4.2667,-1.9914;2.4242,4.0347,-0.3349;2.9201,4.6278,0.4315;1.0896,3.927,-0.3804;0.0646,4.5823,0.4359;-1.165,4.529,-0.2345;-2.3069,5.0507,0.2262;-2.2418,5.6554,1.4119;-3.3612,6.1944,1.9449;-4.5675,6.0756,1.1871;-5.3341,6.5659,1.7896;-4.8269,5.0256,1.0208;-4.473,6.5693,0.215;-1.0621,5.7794,2.1765;-1.1026,6.2875,3.1336;0.1097,5.2354,1.6765;1.0353,5.3075,2.2419;-1.0223,3.8434,-1.578;-1.8509,3.1605,-1.7909;-1.0115,4.5967,-2.3799;0.352,3.1344,-1.4577;0.1611,2.1295,-1.0478;1.2115,2.9784,-2.7118;0.738,2.2941,-3.4285;1.3109,3.9499,-3.2188;2.6056,2.4466,-2.3216;2.4639,1.5258,-1.7373;3.4589,2.1134,-3.5509;4.4385,1.7094,-3.2667;2.97,1.3595,-4.1784;3.6277,3.0042,-4.1701)|",4.781040353285
"[H]C(=O)[C@]1(C([H])([H])[H])[C@]([H])(C([H])([H])[H])C([H])([H])/C([H])=C2/C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])[C@]21[H] |(1.3927,0.4198,-2.7166;2.3852,0.0127,-2.4141;2.8961,-0.8746,-3.0619;2.9928,0.638,-1.1606;4.4925,0.2822,-1.1128;4.6248,-0.7796,-0.8833;5.0159,0.8702,-0.3537;4.9737,0.4591,-2.0775;2.2748,0.0077,0.0854;2.5155,-1.0636,0.0767;0.7399,0.1344,0.0853;0.3326,-0.3567,0.9768;0.2803,-0.3456,-0.7851;0.4107,1.1786,0.1113;2.8724,0.632,1.359;2.26,0.34,2.224;3.871,0.2176,1.5604;2.9344,2.1362,1.2901;3.0269,2.6438,2.2465;2.8397,2.8573,0.1629;2.7232,4.3949,0.179;1.2313,4.7736,-0.0132;1.1095,5.8643,-0.0092;0.6247,4.3618,0.801;0.8212,4.3984,-0.9559;3.1975,5.016,1.5063;3.1662,6.1096,1.4342;4.2271,4.7231,1.7413;2.561,4.7298,2.3506;3.5739,5.0201,-0.9591;3.3708,6.0987,-0.9993;4.6369,4.9132,-0.6976;3.3429,4.3813,-2.3305;2.3154,4.563,-2.6736;4.0019,4.8481,-3.0736;3.6148,2.878,-2.2616;4.6724,2.7277,-2.0127;3.454,2.4099,-3.2422;2.7289,2.1781,-1.2037;1.6888,2.3117,-1.5485)|",5.787861600135001
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"[H]OC([H])([H])C#C/C(=N\OC([H])([H])C([H])=C([H])[H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(9.2508,1.6106,1.6138;8.4564,1.8046,2.1354;7.6175,0.6567,2.0783;8.0639,-0.1965,2.6154;6.7051,0.9293,2.6199;7.274,0.2418,0.7133;6.9955,-0.0955,-0.4178;6.6388,-0.4852,-1.7483;5.4588,-0.9341,-2.0517;4.6108,-0.9784,-0.9606;3.3411,-1.5225,-1.3655;3.0431,-1.0337,-2.3023;3.4385,-2.5995,-1.5524;2.3585,-1.2408,-0.2707;2.2682,-0.1949,0.0202;1.6044,-2.1715,0.3125;0.8754,-1.919,1.0774;1.6825,-3.223,0.0436;7.6366,-0.3802,-2.8447;7.2946,-0.7253,-4.1641;6.2873,-1.0669,-4.3744;8.2363,-0.6286,-5.1831;7.957,-0.8988,-6.198;9.5346,-0.1865,-4.9088;10.2672,-0.1117,-5.708;9.8813,0.159,-3.6033;10.8866,0.5052,-3.3783;8.9398,0.0631,-2.5784;9.2125,0.3326,-1.5632)|",4.48987853325
"[H]C1=C([H])[C@]([H])(N([H])[H])[C@]([H])(C(=O)N(OC([H])([H])[H])C([H])([H])[H])C1([H])[H] |(8.2901,-0.3144,-2.6463;7.3284,-0.6295,-2.2495;7.0099,-1.882,-1.9221;7.6504,-2.7523,-2.0299;5.588,-2.0274,-1.4333;5.5545,-2.4694,-0.4308;4.8425,-2.9455,-2.2959;4.7141,-2.5378,-3.2211;3.9183,-3.1205,-1.9054;5.1017,-0.51,-1.3379;5.0472,-0.2452,-0.2789;3.7319,-0.3253,-1.9721;3.5639,0.0723,-3.1184;2.6286,-0.612,-1.176;2.8746,-1.4929,-0.0961;2.5767,-0.8518,1.1462;3.2239,0.0173,1.3105;2.765,-1.6089,1.9116;1.5268,-0.538,1.1919;1.327,-0.8433,-1.7863;1.2094,-0.1264,-2.5982;0.5443,-0.6862,-1.0395;1.2464,-1.8621,-2.1871;6.1925,0.3398,-2.0402;5.8264,0.7406,-2.9931;6.4862,1.2008,-1.4253)|",6.37834865572
"[H]C1=C([H])C([H])=C([C@@]2(C(=O)C([H])([H])[H])C(=O)O[C@]([H])(C([H])([H])C([H])([H])[H])C2([H])[H])S1 |(4.9538,-2.8249,-6.5489;4.6182,-3.0065,-5.5367;5.2145,-3.7551,-4.5626;6.1495,-4.286,-4.7047;4.4806,-3.7592,-3.3371;4.7842,-4.3099,-2.4545;3.3353,-3.0055,-3.3835;2.2719,-2.8033,-2.3191;1.0156,-3.6999,-2.5984;-0.0954,-3.214,-2.5488;1.2531,-5.1575,-2.9136;1.7289,-5.2596,-3.8953;0.2953,-5.6811,-2.9176;1.9322,-5.5933,-2.1743;2.8801,-3.1805,-0.9404;3.1061,-4.2878,-0.5165;3.1455,-2.0636,-0.2347;2.8576,-0.8595,-1.0063;3.8117,-0.5412,-1.4462;2.3257,0.2153,-0.0695;2.0359,1.0755,-0.6885;1.4065,-0.1599,0.3984;3.3293,0.6503,1.003;2.8994,1.4259,1.6455;4.2418,1.0579,0.5512;3.6159,-0.1953,1.6358;1.883,-1.331,-2.0894;1.9438,-0.7184,-2.9917;0.8522,-1.2964,-1.7249;3.1439,-2.2849,-4.9729)|",5.417786763455
"[H]OC(C([H])([H])[H])(C([H])([H])[H])[C@@]1([H])C([H])=C([H])C(=O)C([H])([H])[C@@]1([H])OC([H])([H])[H] |(0.3654,-0.1366,0.6445;0.8914,0.0215,-0.1557;2.2761,-0.194,0.1923;2.5115,-1.6984,0.3874;3.5457,-1.9116,0.6753;2.2723,-2.2518,-0.5272;1.8604,-2.0743,1.1862;2.599,0.5745,1.4813;3.6489,0.4613,1.7542;1.9802,0.1914,2.3045;2.3854,1.6417,1.3677;3.0246,0.3344,-1.0705;2.4944,-0.1546,-1.9046;2.8377,1.822,-1.256;1.8946,2.2263,-0.8968;3.731,2.6283,-1.8523;3.5616,3.6974,-1.9507;4.9889,2.108,-2.4196;5.8532,2.8368,-2.8836;5.1148,0.5877,-2.4539;6.1675,0.3257,-2.5899;4.5808,0.2281,-3.3462;4.5024,-0.0845,-1.2143;4.5568,-1.1776,-1.3394;5.1986,0.2742,-0.0238;6.4974,-0.2769,0.0972;6.8359,-0.0595,1.1135;7.211,0.1667,-0.6104;6.4874,-1.3685,-0.0507)|",5.151115189965
"[H]C1=C(OC([H])([H])[H])[C@]([H])(C(=O)N2C(=O)OC([H])([H])C2([H])[H])C([H])([H])C([H])([H])C1=O |(2.8496,2.4334,-0.411;3.3166,2.1204,0.5159;3.5704,0.8211,0.7918;3.2614,-0.2319,0.0047;2.5365,0.0142,-1.1994;2.3593,-0.9654,-1.6453;3.1218,0.6336,-1.8889;1.5818,0.5072,-0.9848;4.2673,0.3506,2.0468;4.9765,-0.4326,1.7727;3.2343,-0.2356,3.0124;2.0938,0.1835,3.1074;3.6235,-1.2977,3.8394;4.8982,-1.8601,4.0201;5.9378,-1.5358,3.5042;4.796,-2.8667,4.9241;3.414,-3.1189,5.2484;3.0934,-4.0153,4.7075;3.3469,-3.3,6.3221;2.6628,-1.8619,4.7887;2.4724,-1.1552,5.6039;1.7141,-2.0775,4.2948;5.0465,1.5114,2.7139;5.4166,1.1952,3.694;5.9315,1.708,2.0977;4.1963,2.7814,2.8167;4.7724,3.621,3.2166;3.3463,2.6178,3.4937;3.6227,3.1981,1.4651;3.3977,4.3716,1.1977)|",5.259960730165
"[H]C1=C(/C([H])=C(\[H])OC([H])([H])C([H])([H])Cl)C2=C([H])/C([H])=C([H])\C([H])=C\2C([H])([H])C1([H])[H] |(3.2923,-0.9352,-0.3064;2.3309,-0.4284,-0.273;1.2034,-1.1515,-0.0918;1.2278,-2.6229,0.0213;0.5874,-3.0722,0.7762;1.9924,-3.3965,-0.7669;2.5913,-2.9836,-1.573;2.1447,-4.748,-0.7234;1.4482,-5.4577,0.2942;1.7309,-5.083,1.2865;0.3635,-5.3374,0.1749;1.846,-6.9173,0.1325;2.9196,-7.0499,0.2726;1.5598,-7.2953,-0.85;1.0035,-7.9279,1.3736;-0.1054,-0.4525,-0.0193;-1.3185,-1.136,-0.1886;-1.3035,-2.2031,-0.39;-2.539,-0.463,-0.1288;-3.4667,-1.012,-0.2669;-2.5623,0.9123,0.0974;-3.5079,1.4462,0.1416;-1.3599,1.6057,0.2583;-1.3745,2.68,0.4308;-0.1335,0.9441,0.2027;1.1751,1.6758,0.4124;1.0552,2.7434,0.195;1.4607,1.6009,1.4736;2.2997,1.0699,-0.4384;3.2638,1.5068,-0.1536;2.1452,1.3291,-1.4991)|",4.72389644468
"[H]C1=C(/C([H])=C(\[H])OC([H])([H])/C([H])=C(\[H])C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C1([H])[H] |(10.2279,-2.9252,2.1321;10.1492,-1.8404,2.2062;8.9199,-1.2872,2.2791;7.7348,-2.1407,2.3027;7.8974,-3.215,2.2333;6.4653,-1.7154,2.4129;6.1936,-0.6645,2.5069;5.418,-2.5881,2.3837;4.1638,-2.0641,2.8369;4.1843,-1.9221,3.9271;4.0027,-1.0767,2.3729;3.0745,-3.0124,2.4435;3.0076,-3.2399,1.3796;2.2079,-3.5524,3.3033;2.3063,-3.3149,4.3644;1.0811,-4.4726,2.9349;1.1695,-5.4327,3.4605;1.0593,-4.6734,1.8588;0.1104,-4.0467,3.2231;8.746,0.2202,2.3612;7.9297,0.5317,1.6959;8.4215,0.4908,3.3782;10.0218,0.9946,1.9974;10.1634,0.9692,0.9078;9.9075,2.0497,2.2754;11.2514,0.3829,2.6764;12.1503,0.9726,2.4581;11.1115,0.4076,3.7662;11.4438,-1.0703,2.2205;12.169,-1.5798,2.8709;11.895,-1.0883,1.2141)|",5.123903804915001
"[H]OC(C([H])([H])[H])(C([H])([H])[H])[C@]1([H])C([H])([H])C([H])([H])C(C([H])=C([H])[H])=C([H])[C@]1([H])O[H] |(5.6401,5.4384,0.3395;4.9785,5.0134,-0.2296;5.5669,3.7883,-0.7147;5.7784,2.8546,0.4922;6.2569,1.9077,0.2195;4.8423,2.624,1.0022;6.4444,3.3477,1.2127;6.9249,4.1251,-1.3566;7.3587,3.255,-1.8552;7.6334,4.4824,-0.5965;6.7954,4.9172,-2.1017;4.5722,3.2883,-1.8162;4.8182,3.8933,-2.698;3.0785,3.5497,-1.5175;2.9341,4.6016,-1.2651;2.5272,3.3664,-2.4506;2.4803,2.6592,-0.4186;1.3846,2.7129,-0.4688;2.747,3.0469,0.5743;2.9142,1.2134,-0.5334;2.2536,0.1915,0.2831;2.6145,-0.8267,0.1342;1.28,0.4024,1.1807;0.8539,-0.4189,1.7492;0.8747,1.3899,1.3821;3.91,0.86,-1.3707;4.1908,-0.1896,-1.4643;4.6868,1.803,-2.2566;4.2537,1.7539,-3.2663;6.0245,1.3264,-2.4607;6.3782,1.067,-1.596)|",5.32254691578
"[H]OC(C([H])([H])[H])(C([H])([H])[H])[C@@]1([H])C(OC([H])([H])[H])=C([H])C(=O)C([H])([H])C1([H])[H] |(3.7924,0.3188,1.683;3.9148,0.4255,0.7261;2.704,-0.0065,0.0857;1.5201,0.8054,0.6394;0.5814,0.5416,0.1398;1.6825,1.8771,0.5173;1.3973,0.5919,1.7096;2.4703,-1.499,0.3811;1.634,-1.9026,-0.2018;2.2261,-1.6334,1.4425;3.3631,-2.0921,0.1689;2.9044,0.236,-1.4521;1.9274,0.0246,-1.9059;3.2338,1.6764,-1.7899;2.1247,2.4559,-1.8019;2.2709,3.8418,-2.1041;1.2667,4.2649,-2.0526;2.6816,3.9814,-3.1109;2.9225,4.3375,-1.3753;4.4848,2.1117,-2.0706;4.6972,3.1486,-2.3049;5.651,1.2212,-2.0762;6.7803,1.6512,-2.2811;5.3995,-0.2614,-1.8241;6.1157,-0.8281,-2.4283;5.6307,-0.4548,-0.7708;3.955,-0.6708,-2.1357;3.7864,-1.7193,-1.8741;3.7904,-0.5972,-3.2187)|",5.31710463877
"[H]OC([H])([H])[C@@]1(C([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])([H])C([H])([H])[H])[C@@]2([H])[C@]1([H])C([H])=C([H])[C@]2([H])C([H])([H])[H] |(6.2761,3.2594,-2.0102;7.1502,3.5228,-1.6856;7.1113,3.5162,-0.2616;6.7411,2.5518,0.1154;8.1569,3.6005,0.0538;6.3047,4.6843,0.3809;6.3589,4.4371,1.8999;7.4007,4.3407,2.2331;5.8366,3.512,2.1678;5.9118,5.2492,2.4771;7.0473,5.9822,-0.0239;8.1036,5.8591,0.2567;7.0388,6.0384,-1.1202;6.5709,7.321,0.5492;6.7217,7.3431,1.638;7.2302,8.1007,0.1436;5.1161,7.7011,0.2472;4.9243,7.608,-0.8319;4.993,8.7671,0.4813;4.0665,6.8985,1.0529;4.5188,6.6616,2.0254;2.8504,7.8007,1.3899;2.1851,7.281,2.089;3.2391,8.665,1.9464;2.0376,8.3206,0.197;1.25,9.0027,0.5376;1.5507,7.5123,-0.359;2.6686,8.8737,-0.5083;3.6801,5.565,0.3698;3.0916,5.8258,-0.521;4.8425,4.6486,-0.2077;4.9727,4.9115,-1.2673;4.2188,3.271,-0.0882;4.628,2.3884,-0.5745;3.1558,3.2447,0.7137;2.584,2.3539,0.9653;2.7664,4.614,1.2173;3.0118,4.707,2.2886;1.2495,4.8275,1.0569;0.6973,4.0475,1.5958;0.9604,4.7661,0.0006;0.9123,5.7919,1.4451)|",7.012373927385
"[H]C1=C([H])C([H])=C(C([H])([H])O[C@]2([H])C([H])([H])C([H])([H])[C@]3([H])OC([H])([H])[C@@]2([H])O3)C([H])=C1[H] |(-1.6696,-2.7616,-4.8969;-0.7808,-2.3354,-4.4392;0.4759,-2.8874,-4.6969;0.5678,-3.7461,-5.357;1.618,-2.3417,-4.1109;2.5946,-2.7742,-4.3033;1.5157,-1.2407,-3.2518;2.7516,-0.6164,-2.6441;2.4912,-0.0714,-1.7219;3.1739,0.1264,-3.3422;3.707,-1.6273,-2.3644;4.941,-1.1516,-1.8307;4.7258,-0.3167,-1.1463;5.9241,-0.6591,-2.9114;5.3974,-0.0432,-3.6487;6.6488,-0.0051,-2.4148;6.6876,-1.8066,-3.5964;7.5719,-1.4042,-4.1044;6.0756,-2.308,-4.3521;7.1024,-2.8582,-2.5397;8.1175,-3.2385,-2.681;6.1946,-3.9596,-2.5472;5.2457,-3.7223,-1.5005;4.2295,-3.8095,-1.8855;5.4024,-4.449,-0.693;5.5988,-2.3086,-1.0133;5.4332,-2.1794,0.0578;7.0182,-2.2952,-1.2381;0.2528,-0.6983,-2.9909;0.1605,0.1496,-2.3152;-0.8896,-1.2373,-3.5849;-1.8639,-0.8054,-3.3716)|",6.451819395355
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"[H]C([H])=C([H])C([H])([H])S[C@]1([H])C2=C(C([H])=C([H])C([H])=C2[H])C([H])([H])C1([H])[H] |(0.5405,3.8026,-0.6921;1.5376,3.9789,-0.2983;2.0666,4.8486,-0.6825;2.0838,3.1721,0.6128;1.5329,2.3016,0.9674;3.4487,3.3638,1.2032;3.3985,3.3536,2.2981;3.881,4.3151,0.8774;4.5395,1.9619,0.6849;5.9285,2.039,1.9226;6.4909,1.1379,1.6547;5.4759,2.0149,3.3649;5.7608,3.2304,4.0011;5.4262,3.4162,5.3432;5.6477,4.3564,5.8433;4.8063,2.3772,6.0412;4.5472,2.5096,7.0884;4.517,1.1657,5.4019;4.0362,0.3643,5.9565;4.8461,0.9799,4.0581;4.6125,0.0446,3.5559;6.4183,4.2011,3.0427;5.7075,4.9811,2.7355;7.2763,4.7195,3.486;6.83,3.305,1.8469;7.874,2.9929,1.9716;6.7546,3.8098,0.8808)|",5.488536364585
"[H]C(=O)C1=C(F)C(C([H])([H])[H])=C([H])C(C([H])=O)=C1OC([H])([H])[H] |(2.9054,-3.5388,0.7078;3.6782,-2.7505,0.7621;4.8284,-3.0408,1.0368;3.1644,-1.3849,0.4976;1.8295,-1.247,0.0861;1.1148,-2.3706,-0.1398;1.168,-0.0318,-0.1069;-0.2701,-0.0024,-0.5576;-0.6215,1.0282,-0.6529;-0.9218,-0.5228,0.1535;-0.3949,-0.4987,-1.5268;1.9147,1.111,0.1531;1.4715,2.0962,0.0352;3.2551,1.056,0.5672;3.9702,2.3298,0.8275;5.0099,2.2295,1.186;3.4613,3.4256,0.6688;3.8922,-0.1871,0.7306;5.1731,-0.1927,1.1882;6.2079,-0.5279,0.2478;6.1878,0.1674,-0.6005;6.1077,-1.5602,-0.0902;7.1449,-0.4123,0.7947)|",4.70756961365
"[H]C(=O)C1=C(OC([H])([H])[H])C([H])=C([H])C(C([H])([H])[H])=C1F |(5.9016,-2.4003,-0.1704;4.8018,-2.5105,-0.1566;4.2894,-3.6147,-0.1781;4.0687,-1.2225,-0.1117;4.7805,0.007,-0.093;6.1372,-0.0911,-0.1196;6.9111,1.1004,-0.0913;6.7241,1.6776,0.8228;6.7093,1.7295,-0.9674;7.9527,0.7762,-0.1096;4.0919,1.2212,-0.0524;4.6246,2.1639,-0.0387;2.6954,1.2215,-0.0305;2.1718,2.1739,0.0002;1.9462,0.0446,-0.0464;0.4388,0.0246,-0.0205;0.039,1.0429,-0.0194;0.06,-0.4909,0.8698;0.03,-0.5023,-0.8902;2.6686,-1.1502,-0.0872;1.9549,-2.2862,-0.1027)|",4.770155799265
"[H]C(=O)C1=C([H])C(F)=C(C([H])=O)C([H])=C1OC([H])([H])[H] |(1.0475,4.3986,-0.2426;1.9001,5.0955,-0.164;1.7447,6.2971,-0.2865;3.2243,4.4725,0.097;4.3427,5.3044,0.2076;4.2161,6.3767,0.104;5.5869,4.7496,0.4486;6.6614,5.5597,0.5569;5.7561,3.3676,0.5849;7.0904,2.7702,0.8443;7.9288,3.486,0.9228;7.2717,1.5728,0.9661;4.636,2.5311,0.4736;4.804,1.4665,0.584;3.3716,3.0682,0.232;2.2384,2.3283,0.1116;2.3318,0.914,0.2516;1.315,0.5385,0.1304;2.7098,0.6369,1.243;2.9777,0.4797,-0.5211)|",3.981025632815
"[H]C(=O)C1=C([H])C(OC([H])([H])[H])=C([H])C(C([H])=O)=C1F |(5.0058,-2.7596,-4.346;4.3711,-2.7387,-3.4407;3.9728,-3.7674,-2.9292;4.0531,-1.3876,-2.9107;3.2658,-1.2546,-1.7649;2.8963,-2.1507,-1.2771;2.9522,0.0084,-1.2432;2.1812,0.0122,-0.1236;1.8257,1.2679,0.4429;1.2122,1.0361,1.3145;1.2437,1.8751,-0.262;2.7131,1.8299,0.7605;3.4359,1.1514,-1.8836;3.2251,2.1528,-1.5267;4.2289,1.0433,-3.039;4.7284,2.2736,-3.7001;5.3476,2.1158,-4.6024;4.4856,3.3947,-3.2931;4.5248,-0.2255,-3.5333;5.2867,-0.3408,-4.6441)|",4.247697206305
"[H]OC(=O)[C@@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])C([H])=C1C([H])([H])OC([H])([H])[H] |(3.698,0.3804,-0.974;2.8451,0.1928,-1.4209;2.9831,-0.7478,-2.3791;2.0118,-1.1939,-2.9471;4.4129,-1.1723,-2.7174;4.6914,-2.6588,-2.7595;5.9482,-3.1191,-3.1849;6.6991,-2.4053,-3.5123;6.2522,-4.4797,-3.1983;7.2322,-4.8075,-3.536;5.3033,-5.4149,-2.7837;5.5365,-6.4762,-2.7937;4.0501,-4.971,-2.3609;3.2976,-5.6877,-2.0422;3.7444,-3.6097,-2.3492;2.7566,-3.2887,-2.0409;5.1808,-0.2377,-3.6342;5.3173,0.0457,-4.6678;5.5326,-0.1932,-2.39;6.3373,0.2992,-1.252;6.9444,1.1701,-1.5433;7.0198,-0.4903,-0.8969;5.4149,0.6474,-0.2227;6.0333,1.1086,0.9681;5.2316,1.3361,1.6732;6.6239,2.0177,0.7839;6.6884,0.3373,1.3983)|",5.5728916582400005
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])C([H])=C1C([H])([H])OC([H])([H])[H] |(5.671,2.3707,0.0941;5.3468,3.2095,0.4717;4.0635,2.9525,1.0025;3.7117,3.8967,1.4361;3.3474,2.6815,0.2093;4.0574,1.8476,2.082;2.6772,1.3567,2.4542;1.849,0.7495,1.4972;2.2139,0.6173,0.4813;0.5726,0.2988,1.835;-0.0501,-0.1749,1.0801;0.0967,0.4516,3.1389;-0.8981,0.1026,3.403;0.9092,1.054,4.1009;0.5481,1.1784,5.1188;2.1859,1.5014,3.758;2.8161,1.9665,4.5125;5.1814,1.783,3.0859;5.625,2.2176,3.971;5.251,0.9154,2.124;5.9482,-0.1298,1.3458;5.4097,-1.0905,1.4127;6.9691,-0.2898,1.7277;5.984,0.3088,-0.0127;6.6776,-0.5878,-0.8623;6.6351,-0.1689,-1.8701;6.2063,-1.5824,-0.8646;7.7304,-0.6977,-0.5608)|",6.19875351439
"[H]OC([H])([H])[C@@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])C(=C([H])[H])[C@@]1([H])C([H])([H])C([H])=C([H])[H] |(6.521,2.2315,0.7068;6.0288,2.1578,1.5411;6.4161,0.9317,2.149;7.4884,0.9422,2.4007;5.8618,0.8735,3.0886;6.1317,-0.2987,1.2805;6.8886,-0.2894,-0.0254;6.3831,0.4126,-1.1312;5.4066,0.885,-1.0583;7.107,0.4816,-2.3238;6.6984,1.0247,-3.172;8.3461,-0.152,-2.4286;8.9078,-0.1033,-3.3576;8.8566,-0.8561,-1.3357;9.8168,-1.3597,-1.4129;8.1338,-0.9216,-0.1441;8.5303,-1.477,0.7018;5.8562,-1.5736,1.9706;6.3008,-2.5321,2.7683;5.6483,-3.3302,3.116;7.335,-2.5666,3.1045;4.7175,-0.9437,1.2853;4.4478,-1.378,0.321;3.516,-0.3632,2.0316;2.7833,-1.1658,2.1886;3.8302,-0.0331,3.0304;2.8763,0.7821,1.289;3.5071,1.6616,1.162;1.6403,0.7654,0.7881;1.2268,1.6169,0.2538;0.9887,-0.1,0.8987)|",6.293993362065001
"[H]C1=NC(S/C([H])=C(\[H])C(=O)OC([H])([H])C([H])([H])[H])=NC([H])=C1[H] |(8.0899,4.1445,4.9418;7.2917,3.8469,4.2641;7.4533,2.6755,3.6439;6.4653,2.3185,2.8138;6.77,0.7397,2.0323;5.3529,0.5326,1.0218;4.625,1.3386,1.0573;5.1576,-0.5443,0.2442;5.8605,-1.3693,0.1838;3.9324,-0.6304,-0.5752;3.0474,0.2043,-0.6215;3.919,-1.789,-1.2822;2.7742,-2.0059,-2.1377;2.7056,-3.0932,-2.2273;1.8814,-1.625,-1.6358;2.9645,-1.3466,-3.4962;2.1246,-1.5988,-4.154;3.8889,-1.6928,-3.97;3.005,-0.2588,-3.3918;5.3521,2.9981,2.5429;5.2148,4.1692,3.1764;4.3071,4.7282,2.957;6.1704,4.6536,4.0654;6.0501,5.6033,4.5743)|",4.66403139757
"[H]C1=C([H])C([H])=C(C([H])(C([H])([H])[H])C([H])([H])[H])C(S/C([H])=C(\[H])C(=O)OC([H])([H])[H])=C1[H] |(4.4323,0.9647,-5.1941;4.0307,0.629,-4.2422;3.741,-0.7185,-4.0271;3.9126,-1.4483,-4.8139;3.2308,-1.1328,-2.7993;3.0142,-2.1868,-2.6473;2.9917,-0.2342,-1.7467;2.4525,-0.7626,-0.4215;2.3907,0.08,0.273;1.0301,-1.3362,-0.5775;0.6511,-1.6796,0.3921;1.0142,-2.1909,-1.2642;0.3344,-0.5836,-0.9638;3.4045,-1.7992,0.2056;3.0279,-2.1135,1.1859;4.4085,-1.3836,0.3438;3.4954,-2.697,-0.417;3.272,1.1279,-1.9899;3.0649,2.4048,-0.7316;1.321,2.6134,-0.7065;0.7628,2.1113,-1.493;0.6596,3.3608,0.1939;1.1555,3.8866,1.0038;-0.8057,3.4788,0.0939;-1.5142,2.9444,-0.7401;-1.2902,4.2746,1.0816;-2.7133,4.45,1.078;-2.931,5.101,1.9256;-3.2217,3.4884,1.192;-3.0432,4.9136,0.1439;3.7918,1.5471,-3.2234;4.0014,2.6012,-3.3761)|",5.042269649765
"[H]C1=C([H])C([H])=C(S/C([H])=C(\[H])C2([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H])S1 |(-4.2081,-6.1225,5.5611;-3.4797,-5.3816,5.2581;-2.1369,-5.5558,5.0482;-1.6336,-6.5096,5.1649;-1.4757,-4.3505,4.6734;-0.4131,-4.2735,4.4714;-2.3224,-3.271,4.5932;-1.9274,-1.5919,4.1986;-1.8425,-1.6502,2.4148;-2.5803,-2.2947,1.9402;-0.9928,-0.9009,1.7057;-0.2696,-0.2821,2.236;-0.9851,-0.8486,0.1956;-1.7434,-1.5602,-0.1582;0.3844,-1.2788,-0.3915;0.683,-2.2425,0.0393;0.2567,-1.4417,-1.4717;1.4875,-0.2291,-0.1769;1.7159,-0.1409,0.8947;2.4142,-0.5636,-0.6606;1.07,1.1452,-0.7233;0.9722,1.0819,-1.8175;1.8523,1.8884,-0.5228;-0.2688,1.6054,-0.1261;-0.5752,2.5591,-0.5745;-0.1421,1.7991,0.9485;-1.3702,0.5551,-0.3417;-1.5653,0.4654,-1.4205;-2.3082,0.8814,0.1238;-3.9626,-3.7417,4.9918)|",5.115740389400001
"[H]C1=NC(S/C([H])=C(\[H])C2([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H])=C([H])C([H])=C1[H] |(-2.886,-5.91,3.6993;-2.7329,-5.1257,4.4386;-2.2798,-3.9581,3.9593;-2.076,-2.9667,4.8247;-1.4754,-1.3829,4.2452;-1.3399,-1.6335,2.495;-1.8203,-2.5443,2.1483;-0.7173,-0.7749,1.6813;-0.2347,0.109,2.0981;-0.6446,-0.9662,0.1839;-1.1822,-1.8949,-0.0496;0.8154,-1.1286,-0.314;1.3216,-1.9017,0.2769;0.7835,-1.4932,-1.3515;1.6172,0.1831,-0.2805;1.7705,0.5025,0.7601;2.6181,0.013,-0.6978;0.9009,1.2999,-1.0556;0.8714,1.0353,-2.1233;1.4658,2.2382,-0.9818;-0.5356,1.5002,-0.5484;-1.0475,2.2638,-1.148;-0.5077,1.8865,0.4805;-1.3351,0.1874,-0.5898;-1.4467,-0.1241,-1.639;-2.3473,0.3427,-0.1971;-2.3159,-3.0902,6.2048;-2.1391,-2.2518,6.8718;-2.7821,-4.3069,6.683;-2.9764,-4.4368,7.7443;-2.9982,-5.3571,5.7843;-3.3607,-6.3241,6.1179)|",4.710290752155001
"[H]C1=NC(S/C([H])=C(\[H])C2([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H])=NC([H])=C1[H] |(-2.9173,-5.87,3.7313;-2.7933,-5.009,4.3863;-2.3825,-3.879,3.799;-2.2314,-2.8237,4.6054;-1.7035,-1.2578,3.95;-1.5128,-1.594,2.2173;-2.0262,-2.487,1.8732;-0.8206,-0.7906,1.4031;-0.3079,0.0776,1.8168;-0.7138,-1.0164,-0.0869;-1.2616,-1.9407,-0.3146;0.7554,-1.2113,-0.5439;1.2351,-1.9812,0.0726;0.7425,-1.5939,-1.5753;1.5766,0.0887,-0.5142;1.7072,0.4262,0.5238;2.5853,-0.105,-0.9009;0.8985,1.2014,-1.3286;0.8921,0.9173,-2.3916;1.4761,2.1321,-1.257;-0.5471,1.4335,-0.8624;-1.0319,2.1934,-1.4885;-0.5394,1.838,0.1598;-1.3654,0.1324,-0.901;-1.4521,-0.1985,-1.9466;-2.3855,0.3099,-0.5393;-2.4512,-2.7838,5.9292;-2.8566,-3.9251,6.4865;-3.0339,-3.8974,7.5606;-3.0487,-5.0966,5.7514;-3.3777,-6.0192,6.2165)|",4.468109425210001
"[H]C(=O)C1=C([H])C([H])=C(OC([H])([H])[H])C(C([H])=O)=C1F |(4.5736,5.2714,-3.7407;4.6059,4.2058,-4.0334;5.0505,3.8651,-5.1157;4.0814,3.2487,-3.0359;4.0806,1.8735,-3.3095;4.4763,1.5544,-4.269;3.5961,0.9485,-2.3976;3.6107,-0.1061,-2.642;3.0925,1.3933,-1.1638;2.6038,0.5611,-0.2177;2.5759,-0.8428,-0.4655;2.1382,-1.2873,0.4289;3.5869,-1.2392,-0.6149;1.9526,-1.0795,-1.3355;3.0719,2.777,-0.8387;2.5367,3.2329,0.4697;2.175,2.4148,1.1184;2.4809,4.3905,0.8383;3.5741,3.6647,-1.7999;3.5738,4.9759,-1.5368)|",4.72933872169
"[H]O[C@@]1([H])C([H])([H])C([H])=C([H])C([H])([H])[C@]1([H])OC(=O)C([H])(Cl)Cl |(0.1711,0.6407,-2.7172;0.5023,1.4437,-2.282;-0.0057,1.4396,-0.9509;0.1108,2.4649,-0.5866;-1.4814,1.0225,-0.9001;-1.9107,1.2922,0.0749;-2.0311,1.6023,-1.6523;-1.6457,-0.4601,-1.1398;-2.6312,-0.8119,-1.4397;-0.6489,-1.3394,-0.9853;-0.8274,-2.3965,-1.174;0.75,-0.955,-0.5683;1.4447,-1.0919,-1.4103;1.1209,-1.6216,0.219;0.8592,0.4985,-0.0967;1.9003,0.8256,-0.1;0.3529,0.6311,1.2698;1.1957,0.3138,2.264;2.3413,-0.0392,2.1477;0.4437,0.4515,3.599;-0.3814,1.1535,3.5077;-0.2869,-1.1507,3.9976;1.5258,1.0319,4.8895)|",5.880380309305001
"[H]OC1=C([H])\C2=C(C(/[H])=C/1[H])\N([H])[C@]1([H])C(=O)N=C([H])C([H])=C21 |(4.1787,0.324,-0.2048;3.594,-0.3331,0.2026;2.2902,0.0136,-0.0362;1.2977,-0.8156,0.477;1.5763,-1.695,1.0484;-0.0421,-0.483,0.2424;-0.3901,0.6695,-0.5149;0.6143,1.4994,-1.021;0.3692,2.3831,-1.6021;1.9427,1.1659,-0.7695;2.7334,1.8061,-1.157;-1.7687,0.7908,-0.6732;-2.2184,1.6916,-0.5468;-2.39,-0.3192,0.0263;-2.8188,-1.027,-0.7105;-3.5602,-0.0229,0.9519;-4.1593,1.0342,0.9131;-3.9326,-1.0891,1.7895;-3.0136,-1.9702,2.0889;-3.3257,-2.7526,2.7847;-1.6265,-1.9708,1.6718;-0.9147,-2.6102,2.184;-1.273,-1.056,0.7295)|",2.9932523555
"[H]C1=C([H])C(C([H])([H])O[C@@]([H])(C([H])([H])C([H])([H])[H])[C@@]2([H])OC2([H])[H])=C([H])C([H])=C1OC([H])([H])[H] |(7.752,-0.0643,-5.652;7.8401,0.6866,-4.8748;6.8743,0.746,-3.8674;6.0563,0.0323,-3.8706;6.9487,1.6893,-2.8414;5.875,1.78,-1.7827;5.0222,2.3803,-2.1449;6.2606,2.2918,-0.8914;5.4173,0.4662,-1.4657;4.2187,0.4004,-0.7009;3.4596,1.0716,-1.1409;3.7427,-1.0558,-0.7876;3.7088,-1.3172,-1.8516;4.5105,-1.6986,-0.3375;2.3812,-1.322,-0.1375;2.0757,-2.3615,-0.2981;1.6013,-0.6793,-0.5642;2.4027,-1.1477,0.9435;4.4629,0.8472,0.7277;5.2415,0.2852,1.2492;4.5113,2.2649,0.9648;3.4305,1.5205,1.531;3.4434,1.4245,2.617;2.4501,1.718,1.0962;8.0281,2.5843,-2.8434;8.1157,3.3215,-2.048;8.9931,2.5465,-3.8426;9.8302,3.2379,-3.8449;8.9061,1.5943,-4.8676;9.9074,1.634,-5.7976;9.8777,0.6927,-6.8567;10.7562,0.9032,-7.4697;8.9735,0.802,-7.4706;9.935,-0.3391,-6.4843)|",5.831399816215
"[H]OC([H])([H])[C@@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])C(=C([H])[H])[C@@]1([H])C([H])([H])C([H])([H])[H] |(4.7665,3.353,-1.1411;3.9911,2.9343,-1.5498;2.878,3.2038,-0.7067;2.6817,4.2867,-0.6548;2.0152,2.7387,-1.1888;3.0456,2.6691,0.7196;4.215,3.2841,1.4496;5.5141,2.787,1.2573;5.6591,1.9059,0.6372;6.6098,3.3932,1.8759;7.6076,2.9907,1.7211;6.4231,4.5059,2.6974;7.2742,4.9756,3.1832;5.1351,5.0072,2.8982;4.9809,5.8688,3.5428;4.0422,4.4023,2.2769;3.0405,4.79,2.4422;1.8227,2.3047,1.4621;0.6799,2.7064,1.9963;-0.0271,1.9991,2.425;0.4022,3.7577,2.0363;2.6871,1.1905,1.0464;3.3178,0.7709,1.834;2.2692,0.1637,0.0017;1.7023,-0.6271,0.5122;1.5709,0.6181,-0.711;3.4585,-0.4465,-0.7509;3.1231,-1.2105,-1.4617;4.1614,-0.9243,-0.0568;3.997,0.3285,-1.3055)|",6.326647024125
"[H]OC([H])([H])[C@@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])C(=C([H])[H])[C@@]1([H])C([H])([H])[H] |(5.4679,-2.6434,0.1399;5.3417,-2.0904,0.9279;3.9593,-1.7674,0.9945;3.3426,-2.6786,1.0467;3.8213,-1.2278,1.9342;3.4745,-0.9145,-0.1832;3.5945,-1.6102,-1.5165;4.8381,-1.6838,-2.1654;5.6941,-1.1748,-1.7297;4.9756,-2.3782,-3.369;5.9446,-2.4207,-3.8599;3.8708,-3.0081,-3.9444;3.9754,-3.5454,-4.8832;2.6285,-2.9368,-3.3107;1.7618,-3.418,-3.7567;2.4935,-2.2453,-2.1061;1.5232,-2.184,-1.6206;2.3643,0.0262,0.0645;1.0939,0.1925,0.3985;0.6733,1.1831,0.5582;0.4165,-0.6502,0.5211;3.6908,0.6239,-0.1553;3.8522,1.0416,-1.1512;4.4579,1.3501,0.9367;4.1781,2.4102,0.9535;5.5384,1.2854,0.7665;4.2469,0.9408,1.9286)|",6.299435639075
"[H]OC([H])([H])[C@]1(C2=C(C([H])([H])[H])C([H])=C([H])C([H])=C2[H])/C(=C(/[H])C([H])([H])[H])C1([H])[H] |(1.0775,-3.2552,-2.3473;1.3098,-2.4966,-2.9081;2.727,-2.3747,-2.8708;3.2004,-3.2552,-3.3334;2.9694,-1.5015,-3.4834;3.2517,-2.17,-1.4471;3.0068,-3.3481,-0.5299;3.8519,-4.4837,-0.5313;5.1004,-4.5613,-1.3824;5.5324,-5.5661,-1.3415;5.8681,-3.8572,-1.0394;4.9061,-4.3249,-2.4336;3.5277,-5.5614,0.3022;4.178,-6.4335,0.3062;2.399,-5.5443,1.1226;2.1759,-6.3985,1.7564;1.5674,-4.4274,1.1225;0.6866,-4.3939,1.7582;1.8765,-3.3427,0.3;1.243,-2.461,0.3109;3.1964,-0.7902,-0.925;2.4607,0.259,-0.5854;2.9758,1.1815,-0.312;0.9563,0.2925,-0.5523;0.5788,1.1599,-1.1094;0.5766,0.3907,0.4745;0.5333,-0.6077,-1.0067;4.5281,-1.3184,-1.2434;5.1896,-1.611,-0.4288;5.053,-0.9471,-2.1245)|",6.223243760935
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C(F)C([H])=C2[H])/C(=C(/[H])C([H])([H])[H])C1([H])[H] |(4.5109,-4.1298,-1.1078;3.604,-4.4757,-1.1307;2.7278,-3.3551,-1.0285;2.7479,-2.7642,-1.9566;1.7238,-3.7784,-0.9256;3.0329,-2.4288,0.148;2.7675,-2.9522,1.54;2.8377,-4.3268,1.8241;3.0893,-5.0208,1.0298;2.5979,-4.8104,3.1104;2.6475,-5.8716,3.3327;2.2981,-3.9094,4.1238;2.0686,-4.3724,5.3728;2.2242,-2.5432,3.8844;1.9795,-1.8672,4.6974;2.4536,-2.0758,2.591;2.3804,-1.0106,2.3933;2.9122,-0.9813,-0.129;2.1075,0.0507,-0.3425;2.5563,1.0203,-0.5644;0.6052,-0.0033,-0.2975;0.1716,0.2711,-1.2686;0.2069,0.7097,0.4365;0.248,-1.0027,-0.0319;4.2776,-1.5011,-0.0613;4.8823,-1.2936,0.8203;4.8558,-1.6078,-0.9816)|",6.049090896615
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C(OC([H])([H])[H])C([H])=C2[H])/C(=C(/[H])C([H])([H])[H])C1([H])[H] |(4.7103,-3.8572,-1.7874;3.8168,-4.2284,-1.8693;2.9016,-3.1503,-1.6823;2.9156,-2.4795,-2.5548;1.911,-3.613,-1.6335;3.1606,-2.3233,-0.4231;2.8729,-2.9708,0.9106;2.4591,-2.213,2.0113;2.3217,-1.1413,1.8988;2.2069,-2.795,3.2568;1.8843,-2.1661,4.079;2.3586,-4.1755,3.4188;2.1354,-4.8551,4.5841;1.7168,-4.1163,5.7186;1.6007,-4.8433,6.5247;0.7558,-3.6139,5.5435;2.4639,-3.3665,6.0126;2.7653,-4.9519,2.325;2.8785,-6.023,2.463;3.0234,-4.3585,1.0961;3.3478,-4.9731,0.2633;3.018,-0.8604,-0.5772;2.2007,0.1739,-0.7201;2.6367,1.1663,-0.8463;0.6987,0.0926,-0.7186;0.2837,0.4446,-1.6728;0.2689,0.731,0.0648;0.3534,-0.9322,-0.5523;4.3924,-1.3605,-0.5265;4.9769,-1.2186,0.3812;4.9875,-1.3758,-1.442)|",5.7388811070450005
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])/C(=C(/[H])C([H])([H])C([H])=C([H])[H])C1([H])[H] |(3.8511,-5.5785,-0.1258;4.0233,-5.0579,-0.9275;5.3813,-4.6396,-0.868;6.0597,-5.5084,-0.8631;5.5608,-4.0743,-1.7872;5.6663,-3.756,0.3479;5.5125,-4.4758,1.6662;6.5543,-5.2804,2.1532;7.4878,-5.334,1.5978;6.4089,-5.9996,3.3386;7.2292,-6.6127,3.7028;5.2137,-5.9294,4.0591;5.1004,-6.4872,4.9848;4.1697,-5.1347,3.5858;3.2385,-5.069,4.1425;4.3183,-4.4144,2.3974;3.5096,-3.7825,2.0425;5.3571,-2.3202,0.2281;4.4404,-1.3741,0.0829;4.7646,-0.3401,-0.0422;2.9464,-1.6151,0.0415;2.7654,-2.6967,0.0847;2.4641,-1.1541,0.9158;2.3219,-1.0566,-1.2147;2.6797,-1.4928,-2.1479;1.4031,-0.091,-1.2502;0.9901,0.2725,-2.1875;1.023,0.3709,-0.3407;6.7801,-2.6909,0.2251;7.3821,-2.5042,1.1135;7.3399,-2.632,-0.709)|",6.18242668336
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])/C(=C(/[H])C([H])([H])C([H])([H])[H])C1([H])[H] |(7.783,1.1347,-2.2701;7.8611,0.2609,-1.8543;6.5606,-0.0985,-1.3942;5.9091,-0.3596,-2.2417;6.7027,-1.0024,-0.7935;5.8708,0.9829,-0.5638;6.4279,1.2833,0.8082;7.7786,1.0474,1.1145;8.4355,0.6493,0.3488;8.2811,1.3331,2.3849;9.3292,1.139,2.5998;7.4535,1.8692,3.372;7.8487,2.093,4.3594;6.1114,2.1129,3.0772;5.4513,2.5246,3.8367;5.6046,1.8172,1.8124;4.5535,1.9927,1.6014;4.4261,1.1639,-0.8257;3.1965,0.6926,-0.6705;2.369,1.2334,-1.135;2.837,-0.5543,0.0958;3.7356,-0.9663,0.5691;2.1467,-0.289,0.9104;2.1684,-1.6207,-0.7886;1.8857,-2.4989,-0.1974;2.8449,-1.9494,-1.5853;1.26,-1.2298,-1.2627;5.3062,2.1812,-1.3998;5.3554,3.1646,-0.9344;5.4821,2.1901,-2.4775)|",6.315762470105
"[H]OC([H])([H])[C@]1(C2=C([H])C([H])=C([H])C([H])=C2[H])/C(=C(/[H])C([H])([H])[H])C1([H])[H] |(4.8616,-4.1945,0.0896;3.9909,-4.6232,0.067;3.0251,-3.5944,-0.1381;3.0723,-3.2231,-1.1731;2.0518,-4.0764,-0.0026;3.1622,-2.4072,0.8136;2.8269,-2.6277,2.2702;2.9406,-3.8991,2.8566;3.2796,-4.734,2.2531;2.6345,-4.0919,4.2047;2.7271,-5.086,4.6351;2.2204,-3.0221,4.9987;1.986,-3.1747,6.0489;2.1096,-1.7532,4.4283;1.7827,-0.9098,5.0316;2.4034,-1.5608,3.0787;2.2939,-0.5724,2.6416;2.9551,-1.0747,0.2053;2.0944,-0.1939,-0.2858;2.4846,0.7399,-0.694;0.602,-0.3754,-0.3229;0.2285,-0.3612,-1.3557;0.0924,0.4406,0.2065;0.3023,-1.3212,0.1383;4.3454,-1.4374,0.4759;4.8577,-0.9829,1.3226;5.0041,-1.6932,-0.3567)|",6.315762470105
"[H]OC(=O)[C@]1([H])[C@]([H])(C(=O)OC([H])([H])C([H])([H])[H])[C@]2([H])C([H])([H])[C@@]1([H])[C@]([H])(C([H])([H])[H])[C@@]2([H])C([H])([H])[H] |(4.3679,4.3084,-1.5679;4.9799,3.5983,-1.2976;4.8256,3.4664,0.0467;4.1218,4.2074,0.6937;5.7108,2.3559,0.5864;6.7257,2.6355,0.2839;5.4116,0.9086,0.0287;6.2698,0.517,-0.5217;4.1941,0.7885,-0.8676;3.1172,1.3151,-0.6691;4.4443,-0.0462,-1.9003;3.3415,-0.3191,-2.7995;2.7654,0.5989,-2.9379;3.8242,-0.5911,-3.7415;2.4664,-1.4465,-2.2738;1.6864,-1.6859,-3.0058;1.98,-1.1519,-1.3396;3.0596,-2.3497,-2.0967;5.1847,0.1011,1.3498;4.6684,-0.8497,1.1824;4.4596,1.1416,2.2284;3.51,1.4781,1.8063;4.2942,0.7982,3.2554;5.5875,2.1866,2.1197;5.4163,3.1354,2.6319;6.792,1.3758,2.6704;6.6441,1.3063,3.7576;8.1905,1.9536,2.4404;8.9501,1.3275,2.924;8.2791,2.963,2.8599;8.4518,2.0095,1.3769;6.5612,-0.0524,2.0571;7.3388,-0.2667,1.3108;6.5887,-1.1746,3.0999;7.5403,-1.178,3.6455;6.47,-2.1591,2.6314;5.7862,-1.0552,3.8376)|",7.208295899745001
"[H]O[C@@]([H])(C([H])([H])[H])[C@]([H])(OC(=O)C([H])(Cl)Cl)C([H])([H])[H] |(2.047,1.5613,-0.509;1.9734,0.996,-1.2963;3.2255,0.3371,-1.4471;3.0931,-0.3292,-2.3083;4.3499,1.332,-1.7466;5.2861,0.8256,-2.0054;4.5406,1.9793,-0.8833;4.0549,1.9604,-2.5918;3.4817,-0.5683,-0.2321;2.6047,-1.2049,-0.0886;3.5341,0.3462,0.9168;3.1754,-0.13,2.1072;2.8545,-1.2664,2.367;3.1974,0.9542,3.1916;2.8885,0.5037,4.1309;4.8562,1.6044,3.4219;2.0137,2.2572,2.8085;4.75,-1.4078,-0.2824;4.7511,-2.0227,-1.1896;4.7966,-2.0769,0.5809;5.6447,-0.7785,-0.2892)|",6.18786896037
"[H]O[C@@]1([H])C([H])([H])C([H])=C([H])C([H])([H])[C@]1([H])OC(=O)C([H])(C([H])([H])[H])C([H])([H])[H] |(5.6095,2.1314,-4.811;5.4457,1.5099,-4.0825;6.06,2.0623,-2.9196;6.1349,1.2363,-2.2056;7.4542,2.6259,-3.224;8.0013,2.7836,-2.2844;8.0182,1.874,-3.7908;7.3696,3.9246,-3.9914;8.26,4.2469,-4.5288;6.2676,4.6838,-4.0223;6.2683,5.6105,-4.5944;4.9864,4.3278,-3.3078;4.2091,4.0663,-4.0415;4.587,5.191,-2.7634;5.1459,3.1516,-2.3367;4.1677,2.7355,-2.0877;5.7868,3.5834,-1.1048;4.9967,4.1512,-0.163;3.8033,4.3253,-0.2984;5.7916,4.5566,1.0705;6.8126,4.1789,0.9532;5.8293,6.0937,1.1587;6.3919,6.4076,2.0447;4.8138,6.4965,1.2321;6.3104,6.5341,0.278;5.1516,3.9378,2.3236;5.7012,4.2435,3.2206;5.1578,2.843,2.2788;4.1126,4.2675,2.4213)|",7.178363376189999
"[H]/N=C(\C1=C([H])C([H])=C(F)C(OC([H])([H])[H])=C1[H])N([H])O[H] |(7.506,-4.8059,0.3055;7.8381,-3.8973,0.623;6.8744,-3.0689,0.8246;5.4125,-3.3304,0.7513;4.9028,-4.5686,1.1711;5.5683,-5.3261,1.5724;3.536,-4.8086,1.0942;3.1043,-5.7524,1.4116;2.68,-3.8285,0.6047;1.3586,-4.123,0.5345;3.1558,-2.5748,0.1886;2.4256,-1.5486,-0.3191;0.9978,-1.5385,-0.2382;0.7047,-0.5464,-0.5861;0.6538,-1.6826,0.7909;0.5514,-2.3005,-0.8827;4.5372,-2.3516,0.2709;4.916,-1.3961,-0.0758;7.2208,-1.7518,1.1041;6.7119,-1.3374,1.881;8.5998,-1.5585,1.3311;8.9561,-2.465,1.1566)|",5.053154203785001
"[H]O[C@@]1([H])C2=C([H])C([H])=C([H])C(OC([H])([H])[H])=C2C(=O)C([H])([H])[C@@]1([H])O[H] |(4.8917,-5.3627,0.1326;4.0515,-5.59,-0.294;2.9781,-5.0543,0.5049;2.9387,-5.5917,1.4633;3.1839,-3.572,0.7595;3.9938,-3.2027,1.8368;4.3838,-3.9716,2.5006;4.3074,-1.8633,2.0632;4.9457,-1.5839,2.897;3.8013,-0.8838,1.2146;4.0491,0.1644,1.3507;2.959,-1.2269,0.152;2.5595,-0.2173,-0.6783;1.3139,0.4107,-0.3564;1.1999,1.2287,-1.0705;1.3351,0.8173,0.6638;0.4856,-0.2923,-0.4718;2.6265,-2.5836,-0.0878;1.6997,-2.9922,-1.1952;0.9706,-2.2028,-1.7749;1.6803,-4.4708,-1.5492;2.5691,-4.6973,-2.1533;0.7918,-4.672,-2.1518;1.7123,-5.361,-0.2972;0.8384,-5.1473,0.3292;1.642,-6.7386,-0.6118;2.5264,-6.9782,-0.9399)|",4.819136292355
"[H]C1=C([H])C2=C(C(OC([H])([H])[H])=C1[H])/C([H])=C(/[H])[C@]1([H])OC(C([H])([H])[H])(C([H])([H])[H])O[C@]21[H] |(5.5284,-3.9078,-1.7328;5.628,-3.0251,-1.1072;4.8435,-1.9004,-1.3521;4.1222,-1.8986,-2.1648;4.9791,-0.7687,-0.5478;5.9029,-0.7463,0.5118;6.6898,-1.8963,0.7487;7.5696,-1.8093,1.7915;8.3785,-2.9342,2.0931;8.9862,-2.6431,2.9518;9.0379,-3.1939,1.2544;7.7707,-3.8091,2.3589;6.55,-3.03,-0.0586;7.1555,-3.9116,0.1167;6.0427,0.4556,1.3391;6.6815,0.3864,2.2131;5.4292,1.6077,1.0313;5.5498,2.4934,1.6502;4.5824,1.7642,-0.2038;5.1265,2.3474,-0.9593;3.3654,2.4728,0.0567;2.3086,1.5189,0.2582;1.8621,1.5171,1.7172;1.4671,2.4999,1.9934;2.7135,1.2805,2.3607;1.081,0.7665,1.8755;1.1794,1.8585,-0.7149;0.817,2.876,-0.5365;0.3457,1.1601,-0.5904;1.5405,1.797,-1.746;2.8595,0.2337,-0.0386;4.081,0.4265,-0.7678;3.8586,0.5341,-1.8398)|",4.737502137205
"[H]O[C@]1([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[C@]1([H])OC(=O)C([H])(Cl)Cl |(2.7552,0.9073,0.3855;2.6122,0.8392,-0.5742;1.2592,0.4764,-0.7999;1.2319,0.21,-1.8631;0.3425,1.6921,-0.5752;0.5989,2.1419,0.3947;0.6334,2.4295,-1.3314;-1.1785,1.4495,-0.6278;-1.664,2.384,-0.9355;-1.4247,0.7171,-1.4112;-1.7881,1.019,0.7172;-2.883,1.0432,0.638;-1.5215,1.773,1.472;-1.3744,-0.3683,1.2287;-1.8073,-1.142,0.577;-1.827,-0.5194,2.2165;0.1409,-0.6203,1.3522;0.2883,-1.554,1.9073;0.6213,0.1676,1.9402;0.8123,-0.7963,-0.0195;0.1043,-1.3335,-0.6573;1.9228,-1.757,0.0155;2.9078,-1.611,0.9008;3.0534,-0.7206,1.7059;3.8417,-2.8287,0.7774;3.7931,-3.2493,-0.2238;5.5331,-2.3875,1.1133;3.24,-4.1065,1.9049)|",6.068138866150001
"[H]O[C@]1([H])C([H])([H])C([H])([H])C([H])([H])[C@]1([H])OC(=O)C([H])(C([H])([H])[H])C([H])([H])[H] |(4.7533,-5.7855,-1.4303;5.2647,-5.3856,-2.1532;6.2739,-4.6161,-1.5341;6.707,-3.9945,-2.3278;7.3912,-5.4138,-0.8358;6.9278,-6.1968,-0.2221;8.0579,-5.9061,-1.55;8.0992,-4.3696,0.0543;8.9561,-3.9346,-0.4718;8.4904,-4.815,0.9739;7.0324,-3.2656,0.345;7.3714,-2.2871,-0.0117;6.8134,-3.149,1.4092;5.7678,-3.6749,-0.4247;5.1817,-2.8304,-0.7899;4.891,-4.5011,0.3972;4.034,-3.8428,1.22;4.0109,-2.6365,1.3291;3.1023,-4.8105,1.9377;3.677,-5.7212,2.147;2.5958,-4.2011,3.2489;1.9368,-4.9077,3.7651;2.0377,-3.2805,3.0541;3.4236,-3.9515,3.9204;1.9399,-5.1837,0.9932;1.2791,-5.911,1.477;2.305,-5.6237,0.0594;1.3454,-4.2975,0.7441)|",7.284487777885
"[H]O[C@]1([H])C([H])([H])C([H])([H])C([H])([H])[C@]1([H])OC(=O)C([H])(Cl)Cl |(2.173,-0.1947,-0.5743;2.24,0.2646,0.2797;0.9713,0.1853,0.887;1.0191,0.8432,1.764;0.5065,-1.2166,1.3237;0.6684,-1.9159,0.4937;1.0687,-1.5882,2.1851;-1.0012,-1.0373,1.5997;-1.1689,-0.7849,2.6525;-1.5668,-1.9519,1.4007;-1.4614,0.146,0.6886;-1.9206,0.9468,1.2777;-2.2003,-0.1555,-0.0588;-0.193,0.6783,0.0076;-0.195,1.7532,-0.1759;0.0157,0.0136,-1.2835;-0.5393,0.568,-2.3601;-1.2303,1.5592,-2.3878;-0.2156,-0.2104,-3.6413;-0.6324,0.3327,-4.485;1.56,-0.3319,-3.9025;-0.9994,-1.8291,-3.6034)|",6.00283154203
"[H]O[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[C@@]1([H])OC(=O)C([H])([H])Br |(-2.0368,0.9458,0.0731;-2.3076,0.0584,-0.2178;-1.2199,-0.4877,-0.9457;-1.5205,-1.5163,-1.1849;-0.9473,0.2669,-2.2548;-1.8594,0.2511,-2.8617;-0.7389,1.3198,-2.0184;0.2429,-0.3342,-3.021;-0.01,-1.3549,-3.3443;0.431,0.2431,-3.9342;1.5048,-0.3764,-2.1459;1.8222,0.6489,-1.9144;2.3329,-0.8476,-2.6889;1.2486,-1.1368,-0.8349;2.1292,-1.1172,-0.1823;1.0467,-2.1949,-1.0547;0.0393,-0.5933,-0.0706;-0.1698,-1.1925,0.8175;0.3199,0.7709,0.3818;0.6544,0.9535,1.671;0.85,0.0778,2.482;0.8039,2.4294,1.9921;1.5172,2.9077,1.3203;1.0947,2.5502,3.0323;-0.9001,3.4045,1.7486)|",5.738881107045
"[H]C1=C(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])[C@]2([H])C(=NN=C2OC([H])([H])C([H])([H])[H])OC1=O |(3.904,-3.0207,-2.1672;4.5359,-2.1738,-2.4136;5.2746,-2.1513,-3.5386;5.2916,-3.2655,-4.5489;4.5136,-3.9925,-4.2872;5.0333,-2.8488,-5.5309;6.6537,-3.9879,-4.6751;6.5711,-4.7041,-5.5033;7.4224,-3.266,-4.9803;7.0946,-4.728,-3.4066;7.1779,-4.0201,-2.5706;6.3123,-5.4435,-3.1163;8.4245,-5.4676,-3.5838;8.7168,-5.9857,-2.664;9.2328,-4.7743,-3.8471;8.3587,-6.2168,-4.3822;6.1852,-0.9533,-3.7193;7.1978,-1.2214,-3.3752;5.6545,0.2197,-2.95;5.5972,1.3148,-3.6104;6.0969,1.0438,-4.9362;6.3452,-0.2184,-5.031;6.7879,-0.8541,-6.1176;7.0134,-0.0414,-7.3052;6.9771,-0.7641,-8.1234;6.1833,0.6631,-7.4022;8.3512,0.6779,-7.2496;8.5199,1.2054,-8.1954;9.1709,-0.0333,-7.1033;8.3622,1.4127,-6.4408;5.1785,0.0853,-1.6865;4.561,-1.1289,-1.3669;4.089,-1.2533,-0.2678)|",4.713011890660001
"[H]OC(=O)C1=NN=C2OC(=O)/C([H])=C(/C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])[C@@]21[H] |(6.5998,0.3346,8.3537;6.0328,0.567,7.594;6.4303,-0.2173,6.5795;7.3166,-1.0474,6.6796;5.6899,0.0299,5.3208;4.9594,1.086,5.1495;4.4501,1.127,3.8272;4.9582,0.1186,3.2028;4.619,-0.191,1.9425;4.5878,-1.5596,1.5912;4.2612,-1.8376,0.4709;4.9463,-2.5204,2.6464;4.7421,-3.5528,2.3824;5.5495,-2.205,3.8091;6.0137,-3.2538,4.7806;5.8153,-2.9279,5.8067;5.4374,-4.1704,4.6071;7.522,-3.5695,4.6463;7.7246,-3.9114,3.6221;8.1002,-2.653,4.8075;7.981,-4.6322,5.6522;7.7764,-4.2707,6.6686;7.3825,-5.5447,5.5192;9.469,-4.9707,5.5179;9.7694,-5.7307,6.2478;10.0926,-4.0842,5.6838;9.7005,-5.3588,4.5184;5.8746,-0.7343,4.0277;6.9226,-0.5728,3.72)|",4.914376140030001
"[H]/C1=C(\C([H])([H])C([H])([H])[H])[C@]2([H])C(=NN=C2C([H])([H])[H])OC1=O |(3.124,-3.0106,0.4948;3.0945,-2.1224,1.1179;3.582,-2.1255,2.3721;4.2644,-3.3262,2.9757;4.0468,-3.3774,4.0495;3.8545,-4.237,2.5246;5.7913,-3.301,2.7541;6.2594,-4.16,3.2463;6.248,-2.3894,3.1529;6.0252,-3.3481,1.686;3.3693,-0.8615,3.1839;2.4272,-0.9901,3.7468;3.2616,0.3191,2.2677;3.9497,1.3381,2.6262;4.6019,0.9822,3.8583;4.3604,-0.2501,4.1603;4.8664,-0.858,5.4304;4.0368,-1.1911,6.0672;5.446,-0.1093,5.9756;5.5043,-1.7301,5.247;2.5569,0.2721,1.1131;2.4276,-0.9701,0.4793;1.804,-1.0192,-0.5474)|",5.02866395724
"[H]OC1=C([H])C(=O)C([H])=C([H])C1C1=NC2=C([H])C([H])=C(C([H])[H])C([H])=C2N1[H] |(9.9854,-0.0891,0.0627;9.0544,-0.3659,0.0431;9.0311,-1.717,-0.0922;10.1813,-2.4505,-0.1767;11.1506,-1.9578,-0.1363;10.1949,-3.8983,-0.3228;11.2392,-4.5584,-0.4028;8.8691,-4.5339,-0.3717;8.846,-5.6137,-0.4805;7.7335,-3.8017,-0.2871;6.7826,-4.3267,-0.3313;7.7268,-2.3619,-0.1431;6.5165,-1.6712,-0.062;6.3479,-0.31,0.0697;5.0476,-0.093,0.106;4.3527,1.1557,0.233;4.9246,2.0741,0.3134;2.9955,1.1386,0.2475;2.4385,2.0667,0.342;2.2166,-0.097,0.1401;0.857,-0.038,0.163;0.3332,0.9077,0.2596;0.2468,-0.933,0.0862;2.9252,-1.3588,0.0112;2.3574,-2.281,-0.0692;4.2846,-1.3258,-0.0022;5.2613,-2.2903,-0.1067;5.0949,-3.2799,-0.1919)|",1.9891522471550005
"[H]OC(C#CC1C([H])O[C@@]2([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])[C@]2([H])C1=O)(C([H])([H])[H])C([H])([H])[H] |(2.3675,-1.1118,-1.9104;2.5723,-1.3337,-0.9868;2.8229,-0.0888,-0.3105;3.1443,-0.4266,1.0826;3.4189,-0.6554,2.2459;3.8514,-1.0747,3.524;4.4486,-0.2044,4.4091;4.8461,-0.565,5.3612;3.9506,1.0795,4.4523;2.6689,0.865,5.167;2.9355,0.6547,6.2149;1.8323,2.1355,5.1048;2.4281,2.9844,5.458;1.5635,2.3351,4.0597;0.5689,1.956,5.9719;0.8707,1.8889,7.0268;-0.0629,2.848,5.8864;-0.2336,0.6991,5.5954;-0.6784,0.8371,4.5994;-1.0699,0.5705,6.2935;0.637,-0.5715,5.5805;0.0543,-1.436,5.2442;0.9915,-0.8107,6.5912;1.8378,-0.3666,4.649;1.4749,-0.1138,3.6477;2.7947,-1.605,4.5246;2.7985,-2.5551,5.2654;1.556,0.7909,-0.3605;1.6926,1.7224,0.1979;0.7068,0.2445,0.0593;1.3249,1.048,-1.4025;4.0216,0.6295,-0.962;4.2447,1.5711,-0.4507;3.792,0.8551,-2.0115;4.9069,-0.0109,-0.9278)|",3.37965402321
"[H]C1O[C@@]2([H])C([H])([H])C([H])([H])[C@@]([H])(F)C([H])([H])[C@]2([H])C(=O)C1N([H])[H] |(0.7588,-0.0267,0.8884;-0.0178,0.0421,0.1284;0.433,0.51,-1.1198;0.7954,1.8937,-0.8252;1.6819,1.8614,-0.1686;1.1543,2.6364,-2.1062;1.9253,2.0831,-2.653;0.2698,2.6947,-2.7513;1.653,4.0536,-1.7558;1.8211,4.6318,-2.6711;2.6179,3.9917,-1.2345;0.6754,4.8173,-0.8615;1.0943,5.7916,-0.5805;-0.483,5.0761,-1.6197;0.2495,4.0369,0.3844;-0.5028,4.6101,0.9355;1.1076,3.9122,1.0563;-0.3133,2.6749,-0.0321;-1.147,2.8513,-0.7158;-0.8047,1.7899,1.1784;-0.5789,2.0409,2.3426;-1.2649,0.5314,0.4733;-2.2811,0.5916,-0.43;-2.1208,0.2623,-1.3777;-3.0136,1.2761,-0.3091)|",3.823199599525
"[H]C1=C([H])C([H])=C2C(=O)C(/C([H])=C(\[H])C#N)=C(/C([H])([H])[H])OC2=C1[H] |(7.4775,3.4436,-0.395;6.8809,2.5394,-0.3152;7.513,1.2856,-0.249;8.5968,1.225,-0.276;6.7538,0.1299,-0.1484;7.2089,-0.8537,-0.096;5.3506,0.2025,-0.1116;4.5202,-1.0145,-0.0041;5.0327,-2.133,0.0554;3.061,-0.7783,0.0261;2.1069,-1.8753,0.1314;1.0576,-1.5946,0.1464;2.39,-3.1961,0.2099;3.4134,-3.5513,0.2006;1.3478,-4.1632,0.3092;0.5091,-4.9681,0.3898;2.5826,0.509,-0.0449;1.138,0.9247,-0.0257;0.6419,0.6002,0.8951;0.5884,0.4943,-0.8697;1.0737,2.0125,-0.0895;3.3827,1.5946,-0.1452;4.7486,1.4608,-0.1784;5.496,2.6368,-0.2807;4.9842,3.5923,-0.3299)|",4.449061455675
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"[H]C1=C([H])C2=C(C([H])=C1[H])N([H])/C(=C(/[H])C1=C([H])C([H])=C(F)C([H])=C1[H])C2=O |(2.6146,2.2851,-0.065;1.907,1.4626,-0.0269;0.5362,1.7143,-0.0181;0.1412,2.7255,-0.0563;-0.3408,0.6314,0.0312;0.1429,-0.6888,0.081;1.5122,-0.9531,0.0666;1.8945,-1.9696,0.1011;2.3796,0.1402,0.0105;3.4513,-0.0408,-0.0013;-0.9214,-1.5937,0.1688;-0.8254,-2.5408,-0.1717;-2.1368,-0.9037,0.0498;-3.3995,-1.3777,-0.0348;-4.1459,-0.5992,-0.1796;-3.8812,-2.753,0.0064;-3.1472,-3.8193,0.5694;-2.1948,-3.6243,1.0517;-3.6519,-5.1165,0.5831;-3.0971,-5.9372,1.0259;-4.9046,-5.3567,0.0285;-5.3936,-6.6128,0.0345;-5.6713,-4.3339,-0.5204;-6.6499,-4.558,-0.932;-5.1577,-3.0416,-0.5204;-5.7486,-2.2338,-0.9435;-1.8134,0.5705,0.0069;-2.6254,1.4836,-0.0401)|",3.371490607695
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"[H]/N=C(/S[H])N([H])/N=C(\[H])C(=O)/C([H])=C(\[H])C#N |(-4.4069,0.1626,-1.4478;-5.1786,-0.0213,-0.801;-4.7487,-0.1844,0.3779;-5.8469,-0.5232,1.7555;-6.9505,-0.4567,0.9877;-3.4118,-0.138,0.8162;-3.1914,-0.2655,1.8033;-2.4341,0.0911,-0.0595;-1.2167,0.1384,0.3636;-0.9395,0.0011,1.414;-0.0977,0.3887,-0.5674;1.0487,0.4338,-0.1341;-0.4098,0.5759,-2.0127;-1.4435,0.5239,-2.3347;0.5921,0.8028,-2.8795;1.6154,0.847,-2.512;0.386,0.9917,-4.2769;0.238,1.1482,-5.4209)|",4.076265480490001
"[H]OC1=N[C@]([H])(Cl)N([H])[C@@]([H])(O[H])C1([H])[H] |(-3.2115,-0.0602,0.1396;-2.6513,-0.8515,0.1691;-1.3503,-0.4905,0.0774;-0.4882,-1.4255,0.1245;0.8813,-1.0772,0.0119;1.4366,-1.8462,-0.5201;1.6469,-1.2822,1.7948;1.1719,0.2062,-0.5147;2.1692,0.4054,-0.4818;0.4151,1.3059,0.0754;0.6328,1.4231,1.145;0.7977,2.5225,-0.513;0.7066,2.4126,-1.4757;-1.071,0.9845,-0.1294;-1.3555,1.2441,-1.1598;-1.6819,1.5986,0.5433)|",6.291272223560001
"[H]C1C([H])=C2N=C(C([H])([H])[H])NC(=O)C2=C([H])[C@]1([H])F |(7.2427,3.1999,0.1223;6.6654,2.2812,0.0649;5.3237,2.2878,0.0706;4.7476,3.2056,0.1352;4.5634,1.0403,0.0265;3.2633,1.0827,0.0224;2.5878,-0.1544,0.0149;1.0963,-0.0072,-0.0408;0.7498,0.5697,0.8251;0.6146,-0.9854,-0.055;0.8121,0.5667,-0.931;3.0981,-1.3488,0.0576;4.4934,-1.4866,0.1021;5.0253,-2.5819,0.1734;5.3005,-0.2249,0.0428;6.6471,-0.2374,0.0512;7.1815,-1.1824,0.1129;7.4546,1.0142,-0.0882;7.905,1.0078,-1.0976;8.5225,0.9989,0.8125)|",3.376932884705
"[H]C(NO)C1=C([H])C2=C(O1)C([H])[C@]([H])(Br)/C([H])=N\2 |(-4.2383,1.069,-0.1469;-3.7144,0.1164,-0.1148;-4.4305,-1.0809,-0.135;-5.6555,-0.9308,-0.1901;-2.356,0.0175,-0.0519;-1.5053,-1.1459,0.008;-1.8653,-2.1638,0.0161;-0.2235,-0.6884,0.0617;-0.2746,0.7612,0.0282;-1.5841,1.1612,-0.0326;0.8166,1.5426,0.0763;0.78,2.6255,0.0691;2.1102,0.8343,0.1053;2.8247,1.245,0.8199;3.1718,1.1951,-1.6515;2.037,-0.6639,0.1998;2.9908,-1.1826,0.2885;0.9701,-1.3862,0.1662)|",2.217727881575
"[H]C1=NC([H])=C([H])C(=O)/C1=N\C(=O)C([H])([H])Cl |(1.3274,-0.3631,-2.2118;0.9112,-0.202,-1.2172;1.2398,0.8578,-0.565;0.6864,1.0478,0.7145;1.0093,1.9684,1.1932;-0.1633,0.2054,1.3477;-0.5442,0.414,2.342;-0.5785,-1.032,0.6965;-1.2993,-1.8859,1.1967;-0.0174,-1.2283,-0.705;-0.3014,-2.2352,-1.4367;-1.2551,-3.2178,-1.1208;-2.3963,-3.1273,-1.5182;-0.7728,-4.5088,-0.4667;-0.8971,-5.3247,-1.1817;-1.4056,-4.6905,0.4022;0.9453,-4.5193,0.0836)|",3.0857710646700003
"[H]C1=NC([H])=C([H])C(=O)/C1=N/C(=O)C([H])([H])[H] |(0.5015,-1.6396,0.1238;1.1356,-2.5261,0.1391;0.5667,-3.6763,0.0529;1.373,-4.8291,0.0913;0.8098,-5.7556,0.0197;2.7188,-4.8409,0.2044;3.2847,-5.7668,0.2234;3.4698,-3.5839,0.2936;4.6854,-3.5238,0.3769;2.6002,-2.3275,0.2684;3.1715,-1.1861,0.3507;2.4936,0.047,0.4202;1.8595,0.3709,1.4056;2.7714,0.9516,-0.7604;3.8294,1.237,-0.7609;2.1505,1.8456,-0.6788;2.5737,0.4382,-1.708)|",3.099376757194999
"[H]C1=C([H])C(=O)C(=C=O)C(Cl)=N1 |(-0.4095,2.3806,0.0002;-0.2667,1.3036,-0.0003;-1.3325,0.4627,-0.0005;-2.3499,0.8381,-0.0004;-1.1592,-0.9853,0.0001;-2.0591,-1.8169,0.0011;0.2959,-1.3674,-0.0003;0.5918,-2.6805,-0.0003;0.7936,-3.8149,-0.0005;1.3112,-0.3428,-0.0005;2.9925,-0.8715,-0.0007;1.0677,0.9146,-0.0008)|",4.26130289883
"[H]C1=NC([H])=C([H])C(=O)[C@@]1([H])C#C[Si](C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(4.6258,-4.4057,-1.3217;4.2776,-4.333,-2.3535;4.0489,-5.4213,-2.9891;3.5616,-5.3192,-4.3042;3.4436,-6.2789,-4.8013;3.2071,-4.1725,-4.9257;2.7607,-4.1761,-5.9152;3.3153,-2.881,-4.2395;2.854,-1.8421,-4.6726;4.1424,-2.9313,-2.9264;5.1654,-2.6783,-3.2743;3.7343,-1.9511,-1.9271;3.3707,-1.1558,-1.081;2.8008,0.0861,0.1614;0.9446,-0.1474,0.4324;0.5577,0.5999,1.136;0.7231,-1.1401,0.84;0.3933,-0.0419,-0.5085;3.1598,1.8149,-0.5126;2.8317,2.583,0.1985;2.6364,1.9867,-1.4596;4.2315,1.9574,-0.6905;3.7451,-0.202,1.7752;3.4228,0.5124,2.5426;4.824,-0.0747,1.6321;3.5741,-1.212,2.1645)|",4.38103299305
"[H]OC(=O)C1=C([H])[C@]([H])(OC([H])([H])[H])C(=O)N=C1[H] |(-0.3094,1.5331,-3.0575;-0.0772,2.0996,-3.8118;0.9661,2.9056,-3.4953;1.2918,3.81,-4.2263;1.6972,2.6065,-2.2229;1.7787,1.3903,-1.6528;1.2843,0.5012,-2.0391;2.6639,1.1829,-0.4537;3.6808,0.9582,-0.8454;2.1681,0.106,0.2914;3.104,-0.4729,1.199;2.6126,-1.3527,1.6199;3.3651,0.2232,2.0005;4.0159,-0.7904,0.67;2.8035,2.5039,0.3286;2.7622,2.5637,1.5347;3.0296,3.6696,-0.4556;2.4933,3.7003,-1.6252;2.6179,4.5998,-2.2287)|",3.84224756906
"[H]C1=C([H])[C@@]([H])(OC([H])([H])[H])C(OC([H])([H])[H])=N/C1=C(\[H])NO |(6.1617,-2.2671,2.1286;5.1991,-2.03,1.6913;5.0156,-0.9618,0.9038;5.83,-0.2878,0.6547;3.6829,-0.6382,0.2949;3.3247,0.3352,0.6753;3.852,-0.5575,-1.117;3.0118,0.3773,-1.7828;3.3587,0.4118,-2.8184;3.1049,1.3812,-1.3408;1.9594,0.0739,-1.7605;2.6522,-1.6926,0.6801;1.4544,-1.4389,0.1451;0.403,-2.3896,0.4007;-0.469,-1.9965,-0.122;0.2144,-2.4676,1.474;0.6796,-3.3738,0.0155;2.8331,-2.7267,1.4234;4.0925,-2.9422,1.9751;4.2436,-4.044,2.7758;3.4189,-4.7261,2.9735;5.4847,-4.3023,3.3558;5.5076,-5.3211,4.0565)|",2.7510710285550006
"[H]C1=NC([H])=C(N([H])[H])C(=O)[C@@]1([H])OC([H])([H])[H] |(2.5343,-1.4982,1.8848;2.6847,-2.0209,0.9397;2.7718,-3.3003,0.9345;2.9365,-3.9649,-0.2842;3.0436,-5.0441,-0.1966;2.8953,-3.3758,-1.5161;2.9838,-4.0213,-2.731;2.6837,-4.9861,-2.77;2.6625,-3.4523,-3.5065;2.6534,-1.9136,-1.5943;2.3714,-1.3649,-2.6511;2.8659,-1.155,-0.2818;3.961,-0.9396,-0.3022;2.1321,0.0309,-0.1472;2.644,1.1469,-0.8704;2.0429,2.0076,-0.5673;2.5615,0.9984,-1.9505;3.6963,1.336,-0.6052)|",3.68442153577
"[H]C1=C([H])C(F)=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])C2([H])[H])C([H])=C1[H] |(-2.0492,-1.7069,0.2092;-1.3824,-0.8545,0.1163;-0.1562,-0.8609,0.7798;0.1618,-1.6977,1.3929;0.6842,0.2379,0.6465;1.8707,0.2096,1.2992;0.3579,1.3611,-0.1175;1.3166,2.5225,-0.2439;2.1974,2.3094,0.372;1.79,2.7126,-1.6363;0.9888,2.6037,-2.2591;2.1975,4.0935,-1.7512;3.2155,4.1166,-1.7736;1.7645,4.859,-0.5548;1.2734,5.7885,-0.8677;2.9377,5.2534,0.2258;3.2837,4.4666,0.7759;2.6948,5.9918,0.8842;0.7619,3.9102,0.1399;0.7103,4.0833,1.2197;-0.2461,4.0417,-0.2721;-0.884,1.3373,-0.7695;-1.1835,2.1922,-1.3712;-1.7466,0.2467,-0.6611;-2.7005,0.2584,-1.1801)|",5.287172115215
"[H]C1=C([H])C([H])=C([C@@]2([H])N([H])N([H])[C@]([H])(N([H])[H])C2([H])[H])C(C([H])([H])[H])=C1[H] |(5.1893,-2.1625,-0.0953;4.5979,-1.2542,-0.1775;5.0915,-0.1507,-0.8673;6.0733,-0.1837,-1.3321;4.3158,1.0065,-0.9596;4.7104,1.8567,-1.507;3.0467,1.0855,-0.3741;2.2129,2.3523,-0.4495;1.1787,2.065,-0.696;2.1492,3.0488,0.8618;3.1034,3.0971,1.2227;1.7584,4.4128,0.588;0.7863,4.5233,0.8709;1.8293,4.6766,-0.8717;2.3614,5.6184,-1.0514;0.4795,4.859,-1.4083;0.0201,3.958,-1.5434;0.5178,5.3122,-2.3199;2.634,3.473,-1.4145;2.4133,3.2709,-2.4679;3.7074,3.6763,-1.3223;2.541,-0.0385,0.3221;1.169,-0.0249,0.958;1.0212,-0.9119,1.5815;0.3754,-0.025,0.1975;1.0268,0.8679,1.5742;3.3326,-1.1891,0.4075;2.946,-2.0521,0.9451)|",5.676294921429999
"[H]C1=C([H])C([H])=C([C@@]([H])(N([H])[H])C2([H])[C@@]([H])(C([H])([H])[H])N([H])N([H])[C@]2([H])C([H])([H])[H])C([H])=C1[H] |(5.6353,-6.6134,2.0136;5.1893,-5.721,1.583;5.5812,-5.2772,0.3198;6.336,-5.8226,-0.2408;5.0066,-4.128,-0.2265;5.3188,-3.7888,-1.2127;4.0335,-3.3988,0.4717;3.3586,-2.1953,-0.1935;3.9732,-1.9094,-1.0558;2.0344,-2.5527,-0.752;1.4267,-2.8639,0.0073;2.1464,-3.3666,-1.357;3.2232,-0.9642,0.7243;2.5794,-1.2422,1.5735;2.5944,0.3123,0.1082;3.078,0.5142,-0.8601;1.0808,0.3263,-0.078;0.7813,-0.4063,-0.8285;0.751,1.3243,-0.3872;0.5707,0.0798,0.8643;2.997,1.3821,1.0402;2.3536,1.3624,1.834;4.2901,1.032,1.5706;4.9618,1.5821,1.0383;4.5604,-0.4179,1.318;4.7563,-0.9114,2.2781;5.8095,-0.5733,0.4431;6.1255,-1.6166,0.359;6.64,-0.0165,0.8947;5.6508,-0.1741,-0.5667;3.6478,-3.8605,1.7396;2.893,-3.3216,2.3074;4.2194,-5.0075,2.2907;3.9087,-5.3428,3.2769)|",5.461324979535
"[H]OC1=N[C@]([H])(C2=C([H])C([H])=C(C([H])([H])[H])C([H])=C2[H])N([H])[C@@]([H])(C([H])([H])[H])[C@@]1([H])C([H])([H])C([H])([H])[H] |(3.897,-2.0234,-0.2445;3.7381,-2.318,-1.1538;2.9945,-1.3686,-1.8001;2.713,-1.6058,-3.0138;1.9457,-0.655,-3.7849;1.0863,-1.2017,-4.1976;2.7615,-0.1316,-4.9728;4.1585,-0.1323,-4.9949;4.7026,-0.5955,-4.1782;4.8546,0.4209,-6.0726;5.9426,0.4037,-6.0687;4.1826,0.9835,-7.1624;4.9351,1.591,-8.3238;6.0029,1.3547,-8.2753;4.8393,2.6848,-8.3356;4.5536,1.2256,-9.2847;2.7799,0.967,-7.1446;2.2283,1.3798,-7.987;2.084,0.4175,-6.0714;0.9949,0.404,-6.0927;1.3748,0.4436,-2.9656;1.047,1.1797,-3.589;2.3407,0.9921,-2.0121;3.3092,1.2235,-2.4946;1.8085,2.2906,-1.4074;2.4521,2.6379,-0.5909;0.7916,2.1661,-1.0231;1.7861,3.0796,-2.1688;2.6134,-0.1359,-0.9886;3.49,0.1526,-0.3853;1.4247,-0.4009,-0.0314;1.2249,0.5254,0.5201;0.5388,-0.604,-0.6417;1.6332,-1.5317,0.9841;0.7766,-1.5943,1.664;2.5218,-1.357,1.6081;1.7407,-2.5082,0.5012)|",6.049090896615
"[H]OC1=N[C@]([H])(C2=C([H])C([H])=C(C([H])([H])[H])C([H])=C2[H])N([H])[C@@]([H])(O[H])[C@@]1([H])C([H])([H])C([H])([H])[H] |(3.2761,-1.8423,1.2868;4.0357,-1.4672,1.7573;3.9838,-0.1048,1.6628;4.908,0.5382,2.2457;4.9261,1.98,2.2265;5.8974,2.2794,1.8098;4.848,2.544,3.6486;5.3667,3.8203,3.904;5.8752,4.3633,3.1085;5.2564,4.4008,5.1659;5.6749,5.39,5.3394;4.6275,3.7231,6.2192;4.4854,4.3618,7.5814;4.4025,3.6074,8.3708;3.586,4.9901,7.6356;5.342,5.0034,7.8153;4.1262,2.4413,5.9636;3.6519,1.884,6.7688;4.2347,1.8573,4.7007;3.8702,0.8481,4.538;3.895,2.594,1.3388;3.7504,3.5593,1.6336;2.6052,1.9224,1.3692;2.1458,1.8851,2.3699;1.686,2.6566,0.5886;2.161,2.8975,-0.2257;2.8241,0.4762,0.8692;1.9142,-0.096,1.1064;3.0553,0.407,-0.6601;2.1881,0.8789,-1.1374;3.933,1.0146,-0.9072;3.2222,-1.0058,-1.2323;3.2949,-0.969,-2.3245;2.3583,-1.6411,-0.991;4.1258,-1.4979,-0.8585)|",6.160657575320001
"[H]C([H])([H])C1N=C(C([H])([H])C([H])([H])C([H])([H])[H])NC(=O)[C@@]1([H])C([H])([H])C([H])([H])[H] |(7.9568,2.732,-0.3445;7.1561,2.7779,-1.0935;6.4414,3.5555,-0.8175;7.6296,3.0474,-2.0462;6.4583,1.4517,-1.2018;5.1831,1.4081,-1.0219;4.5512,0.1425,-1.1286;3.0516,0.2292,-1.118;2.7453,0.9876,-1.8515;2.6442,-0.7395,-1.421;2.5047,0.6325,0.2688;2.8184,-0.1198,1.004;2.9648,1.5815,0.5679;0.979,0.761,0.275;0.6119,1.0344,1.2706;0.4989,-0.1827,-0.0103;0.6426,1.5321,-0.4288;5.1181,-1.0208,-1.1789;6.5225,-1.0664,-1.1983;7.1236,-2.1091,-1.0237;7.281,0.2243,-1.5132;8.1747,0.23,-0.8765;7.7976,0.1962,-2.988;8.4884,1.0362,-3.1298;8.3896,-0.7208,-3.0819;6.7063,0.2293,-4.0621;7.1596,0.1973,-5.0586;6.1013,1.1416,-4.0031;6.0303,-0.628,-3.9774)|",4.247697206305
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"[H]OC1=NC(C([H])([H])C([H])([H])C([H])([H])[H])=NC(=O)[C@@]1([H])C([H])([H])C([H])([H])[H] |(8.0435,0.1409,2.5564;7.0943,0.2062,2.7514;6.4001,-0.4955,1.8425;5.1209,-0.4896,1.9569;4.4063,-1.2326,1.0072;2.9436,-1.3619,1.3253;2.5135,-2.1315,0.6781;2.8447,-1.6807,2.3715;2.1928,-0.0249,1.1412;2.6747,0.7394,1.7619;2.2982,0.2981,0.0975;0.7094,-0.1394,1.5024;0.1956,0.8177,1.3585;0.5776,-0.4348,2.5505;0.2039,-0.8868,0.8792;4.8534,-1.7471,-0.1012;6.2113,-1.6096,-0.3873;6.6613,-1.7949,-1.5019;7.1565,-1.2777,0.7909;7.3752,-2.2549,1.2577;8.4898,-0.6632,0.3156;9.2186,-0.6607,1.1415;8.8964,-1.3463,-0.4347;8.358,0.7382,-0.2925;9.3366,1.1073,-0.6165;7.9476,1.4627,0.4211;7.7031,0.7123,-1.1678)|",4.70212733664
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"[H]OC1=NC(C([H])([H])C([H])([H])[H])=NC(=O)[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])[H] |(4.5083,-3.8153,-0.041;5.0609,-3.7417,-0.837;5.295,-2.4453,-1.1028;6.0507,-2.1774,-2.105;6.282,-0.8149,-2.3566;6.9535,-0.5561,-3.678;7.1889,0.509,-3.7404;7.8958,-1.12,-3.6876;6.0915,-1.0052,-4.8712;6.6312,-0.8414,-5.8096;5.1562,-0.4359,-4.9165;5.8464,-2.0686,-4.7949;6.0068,0.1923,-1.582;5.3658,-0.0695,-0.3672;5.3374,0.7408,0.5395;4.6231,-1.4088,-0.2411;4.6636,-1.7088,0.8142;3.1238,-1.2556,-0.6567;3.085,-0.8308,-1.668;2.6773,-2.2576,-0.7272;2.3045,-0.3948,0.3124;2.3234,-0.859,1.3083;2.7803,0.5846,0.4262;0.8544,-0.2269,-0.1531;0.2821,0.3742,0.5615;0.3492,-1.1956,-0.2544;0.8054,0.2769,-1.1258)|",4.60416635046
"[H]OC1=NC(C([H])([H])C([H])([H])[H])=NC(=O)C1=C([H])[H] |(4.5589,-2.3402,4.6119;4.4265,-1.5514,4.0628;3.8801,-1.8957,2.8837;3.514,-0.9315,2.1121;2.9204,-1.2787,0.8919;2.5989,-0.0891,0.0293;3.5148,0.5067,-0.0787;2.2981,-0.4513,-0.9567;1.4984,0.7939,0.647;1.3087,1.6619,0.0072;1.798,1.1514,1.6365;0.5616,0.2353,0.7482;2.596,-2.4644,0.4635;2.9096,-3.5664,1.2583;2.5788,-4.7005,0.9574;3.7206,-3.3126,2.5154;4.2723,-4.3487,3.1657;4.0939,-5.3539,2.7954;4.9141,-4.2529,4.0392)|",3.74972885989
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"[H]OC(NOC([H])([H])[H])[C@@]1([H])N([H])N([H])[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])C1([H])[H] |(5.7994,1.2352,1.6996;4.9457,1.0187,2.1527;4.2064,0.4736,1.1678;2.9605,0.2707,1.3891;2.3601,-0.3118,0.2358;0.9696,-0.4404,0.4889;0.5491,-0.8996,-0.4102;0.5029,0.5376,0.6598;0.7819,-1.083,1.3579;4.9613,0.1646,-0.1279;4.4602,0.6888,-0.947;6.359,0.6467,0.0229;6.6437,1.1165,-0.8336;7.2316,-0.5329,0.1298;7.8123,-0.3775,0.9523;6.3637,-1.7095,0.3638;6.0866,-1.7964,1.4272;7.0206,-3.0077,-0.0654;7.9131,-3.0572,-1.1433;8.1814,-2.1332,-1.6457;8.4652,-4.2726,-1.5485;9.1603,-4.2956,-2.3841;8.1326,-5.4548,-0.884;8.5658,-6.4003,-1.1994;7.2468,-5.4134,0.1936;6.9866,-6.3264,0.7228;6.6996,-4.1964,0.6012;6.0155,-4.1696,1.4473;5.0961,-1.3404,-0.4227;4.2185,-1.9092,-0.1167;5.2631,-1.499,-1.4937)|",5.657246951895
"[H]OC(=O)C([H])([H])C([H])([H])OC([H])([H])C([H])([H])OC([H])([H])C([H])([H])C([H])([H])C(=O)O[H] |(10.4378,-4.7942,-1.1757;10.0509,-5.6572,-1.3992;8.692,-5.5721,-1.4092;8.0277,-6.5468,-1.6582;8.1218,-4.197,-1.0983;8.4987,-3.8521,-0.1259;8.4842,-3.4755,-1.8434;6.5984,-4.1965,-1.0853;6.2259,-4.9068,-0.3321;6.2124,-4.5265,-2.0614;6.1759,-2.8775,-0.7912;4.7668,-2.7572,-0.7246;4.2989,-3.0749,-1.6703;4.3545,-3.3859,0.0802;4.4386,-1.2958,-0.4543;4.8411,-0.6685,-1.2659;4.9115,-0.9725,0.4859;3.0297,-1.1876,-0.3784;2.5786,0.1343,-0.1378;3.099,0.5643,0.7319;2.7957,0.7756,-1.0082;1.0736,0.0798,0.1073;0.6823,1.1052,0.1449;0.6003,-0.4143,-0.7489;0.7002,-0.6918,1.3942;1.2007,-1.662,1.3842;-0.3834,-0.8656,1.4239;1.1505,0.0521,2.6428;2.1896,-0.1453,3.2217;0.3191,1.0485,3.0697;-0.4802,1.0733,2.5188)|",7.227343869280001
"[H]C#CC([H])([H])OC1=C([H])C([H])=C(C2([H])C([H])([H])C2([H])[H])C([H])=C1[H] |(12.5587,1.8294,-1.4407;11.4924,1.8254,-1.4202;10.2868,1.8179,-1.3919;8.8275,1.854,-1.363;8.4428,2.2812,-2.3015;8.4841,2.5029,-0.543;8.3329,0.5296,-1.1858;6.973,0.3609,-1.1469;6.5325,-0.9592,-0.9937;7.2744,-1.7472,-0.9093;5.1717,-1.2412,-0.944;4.8554,-2.2709,-0.8055;4.2089,-0.2261,-1.0524;2.734,-0.4887,-1.0113;2.1994,0.1349,-0.2954;2.1314,-1.8539,-1.2351;1.2776,-2.1369,-0.6257;2.793,-2.6813,-1.4747;1.9522,-0.7844,-2.2808;2.5189,-0.8832,-3.2026;0.9735,-0.3271,-2.4011;4.6707,1.0871,-1.1982;3.9491,1.897,-1.2807;6.0327,1.3929,-1.2448;6.3386,2.4267,-1.3598)|",5.714390860500001
"[H]C1=C([H])N2/C([H])=C([H])\C([H])=C/2C(N2C([H])([H])C([H])([H])[C@]([H])(N([H])[H])C2([H])[H])=N1 |(2.2761,-1.1145,-1.8062;1.5724,-0.5827,-1.1714;0.4981,-1.2396,-0.6638;0.2652,-2.2828,-0.833;-0.3656,-0.5236,0.1525;-1.4777,-0.9517,0.8209;-1.8147,-1.9741,0.729;-1.9799,0.1147,1.5515;-2.8562,0.0832,2.185;-1.1538,1.2307,1.3238;-1.2783,2.2076,1.7622;-0.1411,0.8352,0.4408;1.015,1.4336,-0.1846;1.3293,2.7521,0.0047;2.5473,3.3097,-0.6042;3.4207,2.7175,-0.3176;2.4831,3.2676,-1.7003;2.5727,4.7489,-0.0823;3.1158,5.429,-0.7468;3.0509,4.7901,0.9063;1.0782,5.1069,0.0588;0.6748,5.3179,-0.94;0.6999,6.2245,0.9173;1.0327,7.0998,0.5169;1.1582,6.1323,1.8243;0.4605,3.7869,0.5586;-0.5737,3.6727,0.2221;0.4586,3.7712,1.6603;1.8438,0.7372,-0.954)|",4.677637090095001
"[H]C1=C([H])N2/N=C([H])\C([H])=C/2C(N2C([H])([H])C([H])([H])[C@]([H])(N([H])[H])C2([H])[H])=N1 |(0.8988,-1.0413,-2.5756;0.6954,-0.5295,-1.6393;0.223,-1.2344,-0.5751;0.0215,-2.2954,-0.5388;-0.006,-0.5258,0.585;-0.4379,-1.0758,1.7319;-0.4853,-0.0455,2.5923;-0.8075,-0.226,3.6096;-0.0894,1.1644,2.0151;-0.0361,2.1271,2.4957;0.2216,0.8415,0.686;0.7084,1.493,-0.5093;0.9712,2.8323,-0.5381;1.557,3.4494,-1.7386;2.4283,2.8787,-2.0708;0.8353,3.4374,-2.5671;1.8838,4.873,-1.276;1.8884,5.5955,-2.0984;2.8729,4.8989,-0.7983;0.8035,5.1687,-0.2132;-0.14,5.3945,-0.7267;1.0367,6.2402,0.7481;1.0218,7.1429,0.277;1.9675,6.1436,1.1546;0.6506,3.81,0.4972;-0.3622,3.6724,0.8863;1.3505,3.7499,1.3458;0.9373,0.812,-1.6261)|",4.6803582286
"[H]C1=C([H])N2C(=N1)C(N1C([H])([H])C([H])([H])[C@]([H])(N([H])[H])C1([H])[H])=NC([H])=C2[H] |(2.4909,5.1794,1.7102;2.8616,4.1997,1.4377;4.1755,3.7708,1.4467;5.1033,4.2586,1.7044;4.1378,2.4659,1.0219;2.8,2.1648,0.7761;2.0164,3.2162,1.0269;2.5036,0.8287,0.3129;1.2335,0.4367,0.0388;0.9658,-0.9181,-0.4696;1.6154,-1.1447,-1.3206;1.1906,-1.6611,0.3068;-0.5246,-0.8768,-0.8335;-1.0042,-1.8554,-0.7273;-0.6647,-0.5344,-1.8652;-1.1035,0.1876,0.1096;-1.1876,-0.2515,1.1209;-2.3595,0.7526,-0.3849;-3.0776,0.0292,-0.3929;-2.6924,1.4671,0.262;0.0118,1.243,0.1396;0.0185,1.8532,1.0432;-0.0981,1.9169,-0.7211;3.4743,-0.068,0.1416;4.7583,0.2974,0.4048;5.5103,-0.4714,0.2477;5.1434,1.5291,0.8402;6.1592,1.8346,1.0523)|",4.75110782973
"[H]C1=NC([H])=C2C(=C1[H])/N=C(C(=O)N([H])C([H])([H])C([H])([H])OC([H])([H])[H])\C([H])=C/2[H] |(11.8764,-2.1964,0.5415;11.0344,-1.5179,0.4255;11.012,-0.465,1.291;10.01,0.3829,1.1886;10.002,1.2218,1.886;8.9596,0.2666,0.238;8.9978,-0.8401,-0.6613;10.0815,-1.7452,-0.5412;10.1372,-2.597,-1.2111;8.0385,-1.039,-1.6081;7.0488,-0.1653,-1.6771;5.9792,-0.3835,-2.7309;5.0422,0.4047,-2.8507;6.1645,-1.4908,-3.4865;6.9935,-2.0451,-3.306;5.2834,-1.8689,-4.5732;4.736,-2.7855,-4.3154;4.5575,-1.0623,-4.7028;6.0625,-2.1018,-5.8597;6.5854,-1.1791,-6.1607;5.3708,-2.3814,-6.6724;6.9934,-3.1419,-5.6145;7.8301,-3.4088,-6.7209;8.5097,-4.2122,-6.4253;8.4218,-2.5246,-7.0063;7.2501,-3.7358,-7.5986;6.9216,0.9665,-0.8295;6.0701,1.6212,-0.9713;7.8813,1.1792,0.1296;7.8268,2.0315,0.8029)|",4.574233826905
"[H]C([H])([H])OC(=O)C([H])([H])[C@@]1([H])C(=O)OC1([H])[H] |(-2.0828,5.3379,1.6107;-1.5805,4.8467,0.7774;-2.295,4.2791,0.1759;-1.0822,5.5818,0.1404;-0.614,3.9644,1.3778;0.1137,3.2285,0.5172;0.0023,3.2867,-0.6898;1.0709,2.3097,1.2542;0.4755,1.5591,1.7918;1.6028,2.8778,2.0252;2.0525,1.6468,0.2961;2.667,2.3988,-0.2083;2.8987,0.5037,0.8714;3.6815,0.3482,1.7618;2.4369,-0.4298,-0.0242;1.5397,0.5524,-0.6549;1.7778,0.6738,-1.7124;0.4967,0.2548,-0.5256)|",7.357958517520001
"[H]SC([H])([H])C([H])([H])C1=NN(C([H])([H])[H])C(=O)C1([H])[H] |(6.5623,-4.1983,1.8864;7.2531,-3.1127,2.2988;5.9026,-1.8898,2.0378;5.0486,-2.1472,2.6711;6.3165,-0.9485,2.4138;5.4669,-1.75,0.5678;5.0651,-2.7032,0.2082;6.3487,-1.5121,-0.0411;4.4097,-0.7036,0.3796;3.203,-1.0224,0.0478;2.4539,0.1462,-0.0441;1.0589,0.0854,-0.4156;0.6893,1.1124,-0.4317;0.4883,-0.5005,0.3127;0.9409,-0.3648,-1.4069;3.1706,1.2946,0.2303;2.7448,2.4354,0.2135;4.5801,0.7843,0.5473;5.3129,1.2163,-0.1451;4.8792,1.0844,1.5585)|",5.39873879392
"[H]C1=C(N([H])C2=C([H])C(OC([H])([H])[H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(7.3884,-0.7283,4.5081;7.4085,-0.7264,3.4281;6.7045,-1.5202,2.5407;5.8393,-2.5834,2.7277;5.9758,-3.1108,3.5778;4.6445,-2.8336,2.0318;4.079,-1.882,1.1675;4.5429,-0.91,1.0643;2.8889,-2.1605,0.4845;2.2802,-1.294,-0.3598;2.8602,-0.0136,-0.5683;2.1991,0.4976,-1.2692;3.8634,-0.0931,-1.0069;2.9157,0.5602,0.3653;2.2592,-3.4046,0.6802;0.7719,-3.7745,-0.1665;2.8168,-4.3367,1.5473;2.3179,-5.2896,1.6882;4.0059,-4.0654,2.2195;4.4414,-4.8121,2.8777;7.1263,-1.0772,1.3228;6.8717,-1.419,0.4068;8.0223,-0.0604,1.4456;8.1972,0.1321,2.7209)|",5.069481034815
"[H]C1=C(N([H])C2=C([H])C(F)=C([H])C([H])=C2[H])N([H])N=N1 |(4.9224,-0.4165,-0.4966;4.4503,0.3732,0.0692;3.1422,0.47,0.509;2.0787,-0.4131,0.4453;2.3262,-1.3908,0.4023;0.7258,-0.101,0.2581;-0.2228,-1.1156,0.4672;0.0728,-2.1115,0.7825;-1.5666,-0.8303,0.2801;-2.4613,-1.8181,0.486;-2.0252,0.4284,-0.0939;-3.087,0.6047,-0.2225;-1.0717,1.4248,-0.2994;-1.393,2.4151,-0.6095;0.2913,1.1766,-0.1352;1.0113,1.9586,-0.349;3.118,1.6661,1.1604;2.3478,2.105,1.6449;4.3304,2.2831,1.1116;5.1306,1.4863,0.4651)|",5.2790086997
"[H]C1=C(N([H])C2=C([H])C([H])=C(Cl)C(C#N)=C2[H])N([H])N=N1 |(-3.1946,-3.5085,1.2835;-3.3767,-2.9672,0.3666;-2.4838,-2.2815,-0.4324;-1.1031,-2.0994,-0.3898;-0.5649,-2.9142,-0.1291;-0.465,-0.8635,-0.2128;-1.1766,0.3416,-0.0941;-2.2613,0.338,-0.1016;-0.5007,1.5492,0.0552;-1.0605,2.4734,0.1499;0.8908,1.5864,0.0985;1.712,3.1149,0.2899;1.6182,0.387,-0.0101;3.0505,0.3672,0.0305;4.2114,0.3093,0.0575;0.934,-0.8259,-0.1698;1.5096,-1.7418,-0.265;-3.2694,-1.8069,-1.4427;-2.9951,-1.2722,-2.2561;-4.563,-2.1594,-1.272;-4.6208,-2.8758,-0.1829)|",4.503484225775
"[H]C1=C(N([H])C2=C([H])C(Cl)=C([H])C([H])=C2C([H])([H])[H])N([H])N=N1 |(0.1131,2.8598,0.2909;1.0237,3.0996,-0.2385;2.0358,2.2571,-0.6607;2.1679,0.8773,-0.6318;1.295,0.3749,-0.5598;3.325,0.1535,-0.2916;4.4648,0.7925,0.2165;4.4644,1.8583,0.4115;5.5984,0.0383,0.5093;7.021,0.86,1.138;5.626,-1.3399,0.3268;6.5181,-1.9113,0.5561;4.4747,-1.9602,-0.1613;4.4798,-3.0374,-0.308;3.3188,-1.2481,-0.4829;2.0974,-1.9486,-1.0258;2.2938,-3.0142,-1.1718;1.7849,-1.5286,-1.9908;1.2359,-1.8744,-0.345;2.9174,3.1027,-1.263;3.79,2.8837,-1.7231;2.4869,4.3922,-1.2052;1.3361,4.3771,-0.5983)|",5.246355037640001
"[H]C1=C(N([H])C2=C([H])C(C([H])([H])[H])=C([H])C(Cl)=C2[H])N([H])N=N1 |(7.2661,-3.7503,-0.6755;7.3621,-3.0276,0.1216;6.4236,-2.1354,0.6083;5.0861,-1.9475,0.305;4.6084,-2.7565,-0.0644;4.3872,-0.7344,0.2431;2.9892,-0.7748,0.1197;2.4875,-1.7397,0.0847;2.234,0.3967,0.05;0.7275,0.3304,-0.0519;0.3176,1.24,-0.5014;0.2706,0.2194,0.94;0.4048,-0.5235,-0.6569;2.8854,1.6362,0.1125;2.3223,2.562,0.068;4.2712,1.6651,0.2319;5.0926,3.2204,0.2896;5.0412,0.5064,0.291;6.1213,0.5754,0.3293;7.0948,-1.4817,1.597;6.7573,-0.7639,2.2229;8.3759,-1.9293,1.7069;8.521,-2.872,0.8222)|",5.175605436510001
"[H]C([H])=C([H])C1=C([H])C(Cl)=C([H])C([H])=C1N([H])C1=C([H])N=NN1[H] |(-0.149,1.2836,-2.1487;0.7034,1.8119,-1.7322;1.0251,2.7094,-2.255;1.3242,1.3693,-0.6319;0.9805,0.4428,-0.1711;2.4525,2.0359,0.0444;2.5247,3.4342,0.1178;1.7347,4.0304,-0.3249;3.5683,4.0583,0.793;3.6154,5.8135,0.8854;4.5655,3.3066,1.4111;5.3791,3.7991,1.9328;4.5173,1.9169,1.3366;5.3183,1.3349,1.7801;3.4722,1.2709,0.6621;3.4114,-0.1343,0.5587;3.0737,-0.494,-0.3233;4.1745,-1.028,1.293;4.8329,-2.1948,0.9492;4.9565,-2.647,-0.024;5.3515,-2.7557,2.0789;5.0738,-2.0088,3.1071;4.3427,-0.9585,2.6424;3.9951,-0.2624,3.2871)|",4.45722487119
"[H]C1=C(N([H])C2=C(C([H])([H])[H])C(Cl)=C([H])C([H])=C2[H])N([H])N=N1 |(-0.0928,-2.5746,-1.4854;0.6109,-2.9214,-0.7428;1.5668,-2.2062,-0.045;1.8668,-0.8534,0.0063;1.1116,-0.2481,-0.2819;3.159,-0.2913,-0.0284;3.2778,1.0967,0.234;2.0418,1.9063,0.5368;1.3992,2.0168,-0.3496;2.293,2.9117,0.8741;1.4428,1.4225,1.3178;4.569,1.6374,0.1996;4.8194,3.3605,0.4955;5.7071,0.8707,-0.0548;6.6846,1.3384,-0.0599;5.5536,-0.4893,-0.3034;6.4259,-1.0999,-0.5182;4.2893,-1.0726,-0.3019;4.178,-2.1246,-0.5404;2.1521,-3.1506,0.7424;2.8897,-3.0353,1.4232;1.6055,-4.3791,0.5351;0.6658,-4.2268,-0.352)|",5.28989325372
"[H]C1=C(N([H])C2=C([H])C(C([H])([H])[H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(6.0523,4.994,-1.1971;5.3586,4.7919,-0.3941;4.9544,3.5744,0.1242;5.3525,2.2766,-0.1428;6.2854,2.1778,-0.5157;4.5421,1.131,-0.1819;3.1411,1.1983,-0.173;2.6472,2.1648,-0.1754;2.3456,0.0464,-0.2045;0.8422,0.1598,-0.1992;0.5291,1.2071,-0.1651;0.4081,-0.3577,0.6641;0.4064,-0.2998,-1.0936;2.9974,-1.1937,-0.2576;2.0595,-2.6851,-0.2986;4.3884,-1.2815,-0.2833;4.8651,-2.2549,-0.3288;5.1605,-0.127,-0.2387;6.2451,-0.2007,-0.2408;4.0785,3.9273,1.106;3.5722,3.33,1.7443;3.9333,5.2792,1.1806;4.7224,5.7906,0.2815)|",5.107576973885
"[H]C1=C(N([H])C2=C([H])C([H])=C(F)C([H])=C2C([H])([H])[H])N([H])N=N1 |(1.5957,-4.8441,-0.2558;2.282,-4.5007,0.5046;2.6541,-3.2071,0.8259;2.2226,-1.9741,0.3673;1.3508,-2.0062,-0.1425;3.048,-0.8542,0.1089;4.4443,-0.9604,0.0516;4.919,-1.9297,0.1615;5.2367,0.1649,-0.1743;6.318,0.0921,-0.2207;4.6148,1.3897,-0.3637;5.3668,2.4893,-0.5875;3.231,1.5151,-0.331;2.7854,2.493,-0.4853;2.4262,0.4006,-0.086;0.9242,0.5325,-0.0283;0.62,1.5806,-0.0924;0.5235,0.1161,0.9042;0.4293,0.0038,-0.8566;3.5335,-3.3797,1.8505;4.0258,-2.6775,2.3844;3.7086,-4.7003,2.1398;2.9378,-5.3652,1.3308)|",5.102134696875
"[H]C1=C(N([H])C2=C(C([H])([H])N([H])[H])C([H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(3.2467,3.8993,-1.336;2.4375,3.9674,-0.6239;1.672,2.9516,-0.076;1.7565,1.5781,-0.1878;2.6266,1.1936,-0.5683;0.6886,0.675,-0.2553;0.9923,-0.7083,-0.1844;2.419,-1.1768,0.0094;2.4356,-2.2766,0.0384;2.7874,-0.8251,0.9829;3.3222,-0.6048,-1.011;3.0805,-0.9657,-1.9327;4.284,-0.8825,-0.8233;-0.0438,-1.6381,-0.2611;0.1832,-2.6989,-0.2102;-1.3689,-1.2219,-0.3849;-2.6543,-2.4207,-0.4616;-1.6758,0.1332,-0.4529;-2.7054,0.4555,-0.5669;-0.6494,1.0748,-0.3971;-0.8931,2.1269,-0.5005;0.8133,3.6188,0.745;0.1107,3.2415,1.3644;1.0259,4.9655,0.7035;2.015,5.1608,-0.1183)|",4.83818426189
"[H]C1=C(N([H])C2=C([H])C([H])=C(Cl)C([H])=C2F)N([H])N=N1 |(0.6942,4.7194,-0.845;0.4036,4.3594,0.131;0.4707,3.0773,0.6427;0.9833,1.8958,0.1314;1.7675,1.9794,-0.4993;0.3779,0.6389,0.1756;-0.8923,0.3741,0.7054;-1.4992,1.188,1.0869;-1.4047,-0.9249,0.7219;-2.3891,-1.1128,1.1358;-0.6563,-1.9756,0.1986;-1.298,-3.6092,0.2206;0.606,-1.7437,-0.3532;1.2045,-2.5428,-0.7743;1.092,-0.4505,-0.3483;2.3103,-0.1903,-0.8856;-0.031,3.2148,1.9027;-0.1151,2.5176,2.6293;-0.3987,4.4992,2.1521;-0.1193,5.1851,1.0815)|",5.05043306528
"[H]C1=NC(Cl)=C([H])C([H])=C1N([H])C1=C([H])N=NN1[H] |(1.3953,-1.6566,-0.6082;0.871,-0.7474,-0.3132;1.6346,0.3186,-0.0683;1.0351,1.4397,0.2882;2.0652,2.835,0.6062;-0.3482,1.5734,0.4258;-0.7879,2.5176,0.7257;-1.1415,0.4609,0.1705;-2.2201,0.5238,0.2733;-0.5285,-0.7413,-0.2118;-1.2532,-1.9028,-0.5261;-0.703,-2.753,-0.5368;-2.5988,-2.0678,-0.1725;-3.264,-2.4901,0.9611;-2.8581,-2.7993,1.9135;-4.6044,-2.4715,0.7219;-4.8176,-2.0528,-0.5007;-3.6145,-1.8104,-1.0478;-3.5437,-1.4916,-2.0056)|",5.145672912955
"[H]OC([H])([H])C1=C(N([H])C2=C([H])N=NN2[H])C([H])=C([H])C(Cl)=C1[H] |(4.0679,-1.6641,-1.5045;3.154,-1.3431,-1.5613;2.4443,-1.832,-0.4085;2.3692,-2.9267,-0.4412;2.9899,-1.5554,0.5078;1.0636,-1.2284,-0.3726;0.896,0.1767,-0.372;2.0479,0.9731,-0.4711;2.7879,0.5446,-1.0211;2.0993,2.3503,-0.3466;2.7828,3.3203,-1.0585;3.3905,3.2117,-1.9449;2.5797,4.5299,-0.463;1.8021,4.3856,0.5701;1.5126,3.0576,0.659;0.9435,2.7182,1.4214;-0.3964,0.7128,-0.2759;-0.5441,1.7867,-0.3157;-1.508,-0.1214,-0.168;-2.5036,0.3039,-0.0985;-1.3349,-1.5019,-0.1751;-2.7302,-2.5645,-0.0493;-0.0584,-2.0512,-0.288;0.0634,-3.1299,-0.299)|",4.884443616475
"[H]C1=C(N([H])C2=C(C([H])([H])[H])C([H])=C(Cl)C([H])=C2C([H])([H])[H])N([H])N=N1 |(1.8557,-4.289,-0.9564;2.0522,-3.7236,-0.0574;2.7265,-2.5236,0.0921;3.3501,-1.6881,-0.8284;3.8339,-2.1924,-1.5597;3.9404,-0.4475,-0.4537;5.3418,-0.2968,-0.5157;6.2271,-1.443,-0.9478;7.2828,-1.1836,-0.8313;6.0771,-1.7049,-2.0048;6.0355,-2.3474,-0.3583;5.9065,0.9342,-0.173;6.9824,1.069,-0.2136;5.0899,1.9834,0.2388;5.8176,3.5195,0.6876;3.7073,1.8394,0.2911;3.0875,2.6789,0.5888;3.1091,0.6276,-0.0721;1.604,0.5055,-0.0876;1.1381,1.4949,-0.0889;1.2185,-0.0401,0.7824;1.2696,-0.0404,-0.9755;2.6591,-2.2878,1.428;3.0518,-1.5174,1.9508;1.9751,-3.2781,2.0768;1.6259,-4.1407,1.1692)|",5.113019250895
"[H]C1=C(N([H])C2=C([H])C(F)=C(Cl)C([H])=C2[H])N([H])N=N1 |(-0.7283,-4.5859,-0.3925;-0.1402,-4.1524,0.4031;-0.0598,-2.8339,0.81;-0.7383,-1.6912,0.4125;-1.6709,-1.8446,0.0564;-0.1758,-0.4374,0.1414;1.2098,-0.2218,0.1476;1.9186,-1.0235,0.3186;1.7036,1.0502,-0.1143;3.0319,1.2319,-0.1026;0.8616,2.1247,-0.3972;1.5282,3.7062,-0.7169;-0.5159,1.9015,-0.4155;-1.1831,2.7283,-0.6349;-1.0328,0.64,-0.1439;-2.1088,0.4885,-0.1446;0.8281,-2.8776,1.8433;1.1468,-2.1232,2.4356;1.2832,-4.1405,2.0524;0.6806,-4.9058,1.1887)|",5.1184615279050005
"[H]C1=C(N([H])C2=C([H])C([H])=C(F)C(Cl)=C2[H])N([H])N=N1 |(-3.193,-3.848,0.9574;-3.3913,-3.0764,0.2279;-2.5172,-2.1717,-0.3452;-1.1435,-2.005,-0.2514;-0.6281,-2.8389,-0.0078;-0.4761,-0.7848,-0.0342;-1.155,0.4093,0.2531;-2.2345,0.4173,0.3535;-0.4403,1.5909,0.4416;-0.9504,2.5211,0.6697;0.9456,1.5921,0.3628;1.6246,2.7353,0.5518;1.6315,0.4072,0.0907;3.3742,0.418,-0.0042;0.9251,-0.7733,-0.1171;1.4695,-1.6828,-0.3511;-3.3159,-1.4507,-1.1815;-3.0586,-0.7054,-1.8137;-4.6071,-1.8707,-1.1197;-4.6396,-2.8631,-0.2779)|",4.993289156675
"[H]C1=C(N([H])C2=C(OC([H])([H])[H])C([H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(2.4005,-5.0298,-1.6656;3.3825,-4.6795,-1.9476;3.857,-3.381,-2.0208;3.2639,-2.1746,-1.7048;2.6004,-2.1778,-0.9434;3.5791,-0.9136,-2.2119;3.1167,0.2114,-1.4821;2.412,-0.0974,-0.3502;1.8625,0.9665,0.4168;1.3414,0.4955,1.2516;1.1507,1.5562,-0.1742;2.6499,1.6249,0.8038;3.3791,1.5025,-1.9269;3.0331,2.366,-1.3734;4.1101,1.6846,-3.1063;4.4473,3.3228,-3.6508;4.5607,0.6011,-3.8459;5.1086,0.7547,-4.7687;4.2845,-0.6948,-3.3979;4.5947,-1.5399,-4.0034;5.1455,-3.541,-2.4319;5.8563,-2.8374,-2.5713;5.4509,-4.8582,-2.6112;4.383,-5.5337,-2.3038)|",4.88172247797
"[H]C1=C(N([H])C2=C([H])C(Cl)=C([H])C([H])=C2[H])N([H])N=N1 |(5.1477,-0.998,0.7272;4.5236,-1.5475,0.0377;3.2807,-1.215,-0.4695;2.5011,-0.0773,-0.3383;3.0092,0.7741,-0.1472;1.1109,-0.0165,-0.171;0.3336,-1.165,0.0465;0.7906,-2.1428,0.1389;-1.0465,-1.0318,0.1874;-2.0044,-2.4827,0.4536;-1.6841,0.205,0.1357;-2.7596,0.2764,0.2472;-0.8943,1.34,-0.0626;-1.3656,2.3179,-0.1038;0.4832,1.2401,-0.2218;1.0803,2.132,-0.3938;2.9781,-2.2703,-1.2764;2.1636,-2.4083,-1.8584;3.9646,-3.2069,-1.2588;4.8991,-2.7536,-0.4749)|",5.18648999053
"[H]C1=C(N([H])C2=C([H])C([H])=C(Cl)C(C([H])([H])C([H])([H])[H])=C2[H])N([H])N=N1 |(4.3682,-2.2241,-6.4353;4.8698,-1.6821,-5.6468;4.8771,-1.9297,-4.2852;4.3397,-2.9547,-3.5263;4.1863,-3.8221,-4.0198;3.7423,-2.8463,-2.2596;3.4618,-4.0228,-1.5473;3.7162,-4.9906,-1.9721;2.8723,-3.9519,-0.2913;2.6574,-4.8597,0.2624;2.566,-2.712,0.2711;1.8384,-2.6903,1.8786;2.8303,-1.5162,-0.4116;2.4669,-0.1544,0.1411;2.66,-0.1308,1.2188;3.1209,0.5974,-0.316;0.9978,0.2254,-0.1213;0.7804,1.2188,0.2867;0.3159,-0.491,0.3474;0.7839,0.2452,-1.1957;3.4173,-1.6115,-1.6811;3.5937,-0.6945,-2.2353;5.6665,-0.9355,-3.7907;5.9496,-0.7709,-2.835;6.1088,-0.1127,-4.7812;5.6346,-0.5819,-5.8978)|",5.07764445033
"[H]C1=C(N([H])C2=C(C([H])([H])[H])C([H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(1.0326,-3.4441,-1.5795;1.8008,-3.4976,-0.8219;2.4591,-2.4696,-0.1717;2.2983,-1.0932,-0.2017;1.396,-0.787,-0.5366;3.3346,-0.1391,-0.2585;2.9989,1.2236,-0.0865;1.5667,1.6431,0.1388;1.4997,2.717,0.3318;1.1245,1.1164,0.9939;0.9309,1.4352,-0.7349;4.0197,2.1745,-0.1247;3.777,3.2243,0.007;5.3471,1.7936,-0.3165;6.6069,3.0209,-0.3402;5.6799,0.4544,-0.4895;6.711,0.1615,-0.6552;4.6708,-0.5069,-0.468;4.9281,-1.5465,-0.6416;3.3117,-3.1232,0.6653;3.9603,-2.7343,1.3352;3.201,-4.4734,0.5328;2.2761,-4.6881,-0.3566)|",5.156557466974999
"[H]C1=C(N([H])C2=C([H])C([H])=C(Cl)C([H])=C2C([H])([H])OC([H])([H])[H])N([H])N=N1 |(2.5666,-3.3566,-1.0205;3.426,-3.2156,-0.3816;4.147,-2.0609,-0.1327;3.9572,-0.7493,-0.5281;3.0329,-0.5144,-0.8777;4.9558,0.1534,-0.9238;6.2863,-0.2304,-1.1454;6.5753,-1.2717,-1.0498;7.2443,0.7066,-1.5295;8.2701,0.3977,-1.7009;6.8719,2.0343,-1.7146;8.0704,3.2237,-2.2059;5.5493,2.4288,-1.5173;5.2697,3.4667,-1.6693;4.5863,1.5067,-1.1103;3.1805,1.9647,-0.815;3.0731,3.0332,-1.0513;2.9563,1.8372,0.2586;2.2439,1.2019,-1.58;0.9009,1.5852,-1.3305;0.2664,0.9498,-1.9525;0.7325,2.6377,-1.602;0.6287,1.4472,-0.2728;5.11,-2.4756,0.7367;5.8265,-1.9292,1.1934;5.001,-3.8076,1.0035;3.9739,-4.2398,0.3324)|",4.900770447505
"[H]C1=C(N([H])C2=C([H])C([H])=C(Br)C([H])=C2[H])N([H])N=N1 |(-2.9979,-4.2797,0.8459;-3.3287,-3.3836,0.3416;-2.5786,-2.3276,-0.1436;-1.212,-2.1166,-0.2146;-0.6451,-2.9519,-0.2392;-0.5316,-0.9099,0.0038;-1.1599,0.2289,0.5324;-2.2026,0.1924,0.83;-0.4408,1.4091,0.7188;-0.9369,2.2812,1.1311;0.9121,1.4572,0.3918;1.9008,3.0811,0.6499;1.5559,0.3305,-0.12;2.6103,0.3675,-0.3713;0.8346,-0.842,-0.3176;1.3338,-1.7139,-0.7338;-3.5165,-1.4943,-0.6746;-3.3826,-0.6151,-1.1542;-4.7724,-1.9934,-0.5136;-4.6458,-3.1408,0.0864)|",5.05043306528
"[H]C1=C(N([H])C2=C([H])C([H])=C(Cl)C([H])=C2[H])N([H])N=N1 |(-3.3309,-3.832,0.928;-3.4887,-3.0404,0.2102;-2.5783,-2.1363,-0.3063;-1.2083,-2.0062,-0.1518;-0.7199,-2.8554,0.093;-0.4882,-0.8109,0.0018;0.9151,-0.8635,-0.0416;1.4129,-1.8174,-0.1979;1.6763,0.291,0.1051;2.7593,0.2389,0.0717;1.0391,1.5193,0.2829;1.9973,2.9838,0.4504;-0.351,1.5906,0.3297;-0.8413,2.5459,0.4842;-1.1133,0.4307,0.1966;-2.1933,0.4956,0.2753;-3.3326,-1.38,-1.1519;-3.0398,-0.6241,-1.755;-4.6335,-1.7798,-1.1494;-4.7132,-2.7919,-0.3355)|",5.066759896310001
"[H]C1=C(C2([H])C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])N([H])N=C1C(=O)OC([H])([H])C([H])([H])[H] |(7.0558,-0.8119,0.677;6.633,0.1415,0.3983;7.254,1.375,0.3961;8.6545,1.7948,0.7394;9.1568,0.9016,1.1359;8.7051,2.8842,1.8364;8.156,3.7746,1.5021;8.2196,2.5212,2.7496;10.1522,3.2951,2.1285;10.6836,2.4337,2.5823;10.1693,4.1092,2.8621;10.7861,3.7624,0.8959;11.718,4.1169,1.0989;10.866,2.7018,-0.1088;11.3974,3.0887,-0.9857;11.4279,1.8158,0.2512;9.4527,2.2615,-0.5028;9.4986,1.4511,-1.2398;8.9391,3.1078,-0.9769;6.2934,2.2517,-0.0369;6.3872,3.2493,-0.1632;5.1083,1.6841,-0.3103;5.3108,0.3879,-0.0456;4.2667,-0.646,-0.2079;4.4656,-1.8239,0.0276;3.0918,-0.1416,-0.6355;2.0194,-1.0907,-0.8293;2.0706,-1.8511,-0.0462;1.1098,-0.4983,-0.7013;2.0821,-1.7213,-2.2129;1.2143,-2.3736,-2.3655;2.9882,-2.3244,-2.3193;2.0754,-0.9506,-2.9905)|",5.771534769105
"[H]C1=C(C2([H])C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])N([H])N=C1C(=O)OC([H])([H])[H] |(2.0569,1.7815,4.3158;2.0445,2.1225,3.2916;1.9363,3.4201,2.8319;1.798,4.7318,3.5502;1.8208,4.4996,4.6239;0.4504,5.4318,3.2548;0.3575,5.621,2.1773;-0.3813,4.78,3.5465;0.3599,6.7711,3.9935;0.3255,6.575,5.0846;-0.5709,7.2849,3.7277;1.4891,7.6193,3.6096;1.39,8.5392,4.0334;2.7701,7.0283,3.9986;3.5726,7.7272,3.7371;2.8406,6.8424,5.0898;2.9681,5.701,3.2595;3.9181,5.2402,3.5542;3.0224,5.9075,2.1825;1.9686,3.3139,1.4658;1.9149,4.064,0.7917;2.0885,2.0568,1.0111;2.1353,1.3205,2.1277;2.2648,-0.1513,2.1217;2.3209,-0.8184,3.1379;2.3126,-0.6714,0.8787;2.4356,-2.0995,0.8241;2.4703,-2.3473,-0.2373;1.5775,-2.58,1.3025;3.3499,-2.4287,1.3258)|",5.752486799570001
"[H]C1=NC([H])=C(C([H])([H])C([H])=C([H])[H])C(C#CC2=C([H])C([H])=C(C([H])([H])[H])C([H])=C2[H])=C1[H] |(8.3835,0.1864,-0.2305;7.3076,0.3097,-0.3418;6.8455,1.5657,-0.2709;5.5257,1.7304,-0.4077;5.1679,2.7582,-0.3469;4.5995,0.7028,-0.6126;3.1239,1.0149,-0.7631;2.5411,0.2101,-0.2953;2.8901,1.9397,-0.223;2.704,1.1575,-2.2089;2.8787,0.284,-2.837;2.1569,2.2525,-2.736;1.8708,2.3016,-3.7832;1.9727,3.1446,-2.1403;5.1056,-0.6159,-0.6853;4.2504,-1.7357,-0.8925;3.5205,-2.6921,-1.0766;2.6798,-3.8236,-1.279;3.2102,-5.1268,-1.2359;4.2702,-5.2622,-1.0448;2.3858,-6.2309,-1.4313;2.8151,-7.2291,-1.3912;1.0146,-6.0814,-1.6756;0.1217,-7.2792,-1.8964;0.662,-8.2165,-1.7314;-0.2728,-7.298,-2.9204;-0.7411,-7.2641,-1.2199;0.4899,-4.7799,-1.7177;-0.5725,-4.6379,-1.903;1.3002,-3.6681,-1.5244;0.8764,-2.669,-1.5582;6.4944,-0.799,-0.5439;6.9188,-1.7967,-0.5915)|",4.359263885010001
"[H]OC(=O)[C@@]([H])(N([H])C([H])([H])[C@]12C([H])C1C([H])=C([H])C2=O)C([H])([H])O[H] |(1.1662,1.3846,-2.8685;0.6344,1.2789,-3.6929;-0.5894,0.8753,-3.3249;-1.4815,0.6682,-4.1148;-0.7955,0.7464,-1.8024;-1.3538,-0.1863,-1.6273;0.4829,0.7815,-1.094;0.317,1.1568,-0.1609;1.1512,-0.5219,-0.9859;0.5618,-1.2559,-0.4125;1.2712,-0.923,-2.0003;2.5287,-0.3819,-0.3568;3.031,-1.3764,0.6669;3.5647,-2.3079,0.8116;2.7891,-0.1713,1.1164;3.4831,1.0954,1.3808;3.7668,1.4648,2.3603;3.8577,1.5967,0.1838;4.517,2.444,0.0313;3.3981,0.7388,-0.972;3.5545,0.9978,-2.1492;-1.6768,1.9125,-1.3462;-2.6321,1.8564,-1.8823;-1.186,2.8639,-1.6036;-1.8429,1.7876,0.0643;-2.3185,2.5698,0.3817)|",3.823199599525
"[H]C1=C([H])C(/N=C(\[H])N(C([H])([H])[H])C([H])([H])[H])=C(C#N)C(Cl)=C1[H] |(7.1166,-0.5139,4.994;6.7013,0.2796,4.3787;6.1815,-0.0232,3.1244;6.162,-1.0515,2.7769;5.6345,0.9839,2.3081;5.07,0.7529,1.0642;5.612,-0.0922,0.2546;6.5695,-0.5877,0.4591;5.0706,-0.4302,-0.9391;5.746,-1.3364,-1.8477;5.9921,-0.8332,-2.7925;5.1111,-2.2023,-2.0773;6.6735,-1.6971,-1.3956;3.8092,0.1439,-1.3853;3.9445,0.6406,-2.3539;3.4779,0.8732,-0.6467;3.0488,-0.6398,-1.4974;5.5995,2.3158,2.8105;5.0365,3.3482,1.9964;4.5806,4.1878,1.3326;6.1313,2.5912,4.0826;6.1021,4.2282,4.7039;6.6852,1.5862,4.8698;7.0818,1.8244,5.8499)|",4.696685059629999
"[H]O[C@@]1([H])C2=C(OC([H])([H])C1([H])[H])C([H])=C([H])C([H])=C2OC([H])([H])[H] |(5.6324,-1.2948,-0.1887;6.1373,-1.2309,-1.0162;5.2273,-0.6978,-1.9871;4.4065,-1.4126,-2.1428;4.6336,0.6286,-1.5482;4.993,1.8365,-2.1594;5.9381,1.9047,-3.1412;6.8087,0.767,-3.2414;7.3911,0.9321,-4.1508;7.4867,0.757,-2.3775;6.0077,-0.5252,-3.2898;6.6713,-1.3858,-3.424;5.3134,-0.4901,-4.1382;4.3722,3.0436,-1.8013;4.6681,3.9551,-2.3096;3.4138,3.0403,-0.7993;2.9348,3.9726,-0.5124;3.0638,1.86,-0.1299;2.332,1.887,0.6685;3.6853,0.6701,-0.5071;3.4491,-0.547,0.0893;2.4591,-0.6195,1.1051;1.4774,-0.3068,0.728;2.7272,-0.0018,1.9717;2.4147,-1.6676,1.407)|",5.787861600135
"[H]C1=C(N(=O)=O)C(=O)N(C([H])([H])[H])C([H])=C1F |(6.2921,-0.0832,0.2262;5.2114,-0.035,0.156;4.4829,-1.197,0.0901;5.2332,-2.4555,0.0853;4.6709,-3.4643,-0.3232;6.4046,-2.4072,0.4779;3.0226,-1.2222,0.0249;2.2711,-2.1828,0.0137;2.4565,0.0945,0.0002;0.9918,0.1337,-0.0681;0.6661,1.1738,-0.1085;0.5694,-0.362,0.809;0.6526,-0.405,-0.9555;3.1813,1.2411,0.0455;2.6431,2.1813,0.0168;4.5464,1.197,0.1265;5.2479,2.3455,0.1736)|",3.86945895411
"[H]C1=C(F)C(N(=O)=O)=C(C([H])([H])[H])C(N(=O)=O)=C1F |(6.3297,-0.1015,-0.0544;5.2467,-0.0975,-0.0431;4.5266,-1.2813,-0.0492;5.1948,-2.4329,-0.0605;3.1284,-1.2822,0.0051;2.4459,-2.5849,-0.0022;1.5719,-2.7662,0.8431;2.8,-3.3954,-0.8522;2.3904,-0.0881,0.0556;0.8797,-0.0834,0.0768;0.4816,0.8189,-0.3885;0.5149,-0.1118,1.108;0.4756,-0.9546,-0.4401;3.1373,1.1013,0.04;2.4655,2.4092,0.0718;1.5951,2.5732,0.9245;2.8245,3.2407,-0.7555;4.5356,1.0914,-0.0145;5.2123,2.2379,0.0093)|",5.015058264715
"[H]C1=C(Cl)N=C(N(=O)=O)C(OC([H])([H])[H])=C1[H] |(3.0398,4.651,1.0599;3.2288,3.6085,1.2869;4.1631,3.2498,2.2575;5.0412,4.4991,3.1196;4.4357,1.9922,2.5774;3.7804,1.0509,1.9347;4.1108,-0.3464,2.3196;3.4414,-0.8391,3.2188;5.0227,-0.8832,1.7037;2.8162,1.258,0.9379;2.2497,0.1629,0.3899;1.2652,0.3465,-0.6249;0.9557,-0.657,-0.917;1.6851,0.8683,-1.4934;0.4,0.9003,-0.2405;2.5455,2.5932,0.6177;1.8138,2.8463,-0.1409)|",4.906212724515
"[H]OB(O[H])C1=C([H])N=C(C(=O)OC([H])([H])[H])N=C1[H] |(-1.2951,4.9356,-2.9277;-0.7695,5.6304,-3.3452;0.5631,5.5385,-3.0661;1.3386,6.5691,-3.5126;2.2839,6.4168,-3.3836;1.1722,4.3135,-2.2728;2.264,4.4335,-1.4003;2.7352,5.4022,-1.2273;2.7896,3.4118,-0.7201;2.2094,2.2211,-0.9125;2.7607,1.0222,-0.1615;2.2844,-0.0877,-0.2297;3.8323,1.3483,0.5794;4.4072,0.2598,1.3202;5.2501,0.6904,1.8608;4.7446,-0.5291,0.6425;3.6752,-0.1598,2.0155;1.1673,1.9626,-1.7119;0.6684,3.007,-2.3746;-0.1721,2.7845,-3.0337)|",4.917097278535
"[H]OB(O[H])C1=C([H])N=C(N2C([H])([H])C([H])([H])C([H])(F)C([H])([H])C2([H])[H])N=C1[H] |(4.4251,-5.3616,-2.7895;3.9453,-5.7708,-3.5213;2.6322,-5.383,-3.5688;1.8617,-6.0508,-4.4839;0.9773,-5.6717,-4.5678;2.0527,-4.2558,-2.6454;0.696,-4.1637,-2.2896;-0.0192,-4.9095,-2.6438;0.1762,-3.2221,-1.5115;1.0397,-2.289,-1.0427;0.5323,-1.3046,-0.2435;1.3636,-0.236,0.3076;1.3586,-0.3181,1.4059;2.3856,-0.3854,-0.0356;0.8255,1.1414,-0.1127;0.9294,1.2632,-1.1978;1.4085,1.935,0.3691;-0.6476,1.2752,0.256;-0.7582,1.2914,1.3505;-1.138,2.4926,-0.2223;-1.4706,0.1323,-0.3276;-2.5128,0.2118,0.0034;-1.4608,0.2138,-1.4214;-0.887,-1.2254,0.0961;-1.4043,-2.0488,-0.3923;-0.996,-1.3541,1.1846;2.3682,-2.2593,-1.3073;2.8326,-3.2281,-2.0875;3.9035,-3.1735,-2.2956)|",4.938866386575
"[H]OB(O[H])C1=C([H])N=C(N2C([H])([H])C2([H])[H])N=C1[H] |(-4.7181,-4.9388,0.9231;-5.5318,-4.6669,0.4793;-5.4555,-3.3908,-0.0143;-6.6304,-2.8914,-0.5123;-6.5104,-2.0407,-0.9537;-4.1236,-2.5634,-0.0096;-4.0853,-1.1578,0.0165;-5.0131,-0.5823,0.0497;-2.9754,-0.4262,0.0231;-1.8218,-1.1321,-0.0034;-0.6791,-0.4213,-0.001;0.7306,-0.4444,-0.0145;1.2427,-0.7652,0.8938;1.2261,-0.7288,-0.944;-0.0783,0.8541,0.019;-0.1367,1.4579,-0.8878;-0.1194,1.4214,0.9499;-1.6989,-2.4789,-0.0323;-2.846,-3.1508,-0.0318;-2.74,-4.2373,-0.067)|",5.10485583538
"[H]OB(O[H])C1=C([H])N=C(N2C([H])([H])C([H])([H])[C@@]([H])(F)C2([H])[H])N=C1[H] |(5.9967,3.9848,1.3581;6.7961,3.4643,1.2067;6.5187,2.1552,0.9122;7.6115,1.331,0.8501;7.3894,0.4493,0.5242;5.0588,1.6383,0.6646;4.6581,0.3097,0.891;5.3719,-0.423,1.2742;3.4304,-0.158,0.6934;2.5291,0.7422,0.2293;1.2697,0.2935,0.019;0.1792,1.1323,-0.4893;0.1508,2.0876,0.0391;0.3188,1.3435,-1.5572;-1.0581,0.2559,-0.2445;-1.8791,0.4601,-0.9361;-1.4258,0.3887,0.7799;-0.5207,-1.1636,-0.4109;-1.1487,-1.9414,0.0345;-0.4149,-1.4391,-1.7794;0.8826,-1.1076,0.1918;1.5656,-1.7836,-0.3302;0.878,-1.389,1.2532;2.7734,2.0491,-0.0356;4.0171,2.4566,0.1923;4.2036,3.5087,-0.0348)|",4.98512574116
"[H]C1=C([H])/C2=N/C(=O)/C([H])=C(/C(=O)N(OC([H])([H])[H])C([H])([H])[H])[C@@]2([H])C([H])=C1[H] |(4.9751,-1.7286,-0.7391;6.0298,-1.5687,-0.9488;6.6552,-0.4729,-0.4549;6.1493,0.244,0.1835;8.0822,-0.2735,-0.6454;8.6729,0.6181,0.09;10.0738,0.7271,0.0384;10.6521,1.6636,0.5676;10.8492,-0.3415,-0.6472;11.9202,-0.3221,-0.4882;10.2624,-1.2206,-1.476;11.0125,-2.1168,-2.4212;10.6365,-2.2084,-3.5897;12.0692,-2.8682,-1.9651;12.6205,-2.5945,-0.7055;12.2011,-3.575,0.2524;11.1189,-3.528,0.415;12.7269,-3.3124,1.1732;12.4885,-4.585,-0.0626;13.0004,-3.5225,-2.8701;12.4933,-3.6608,-3.8243;13.2913,-4.494,-2.4599;13.8968,-2.907,-3.0103;8.7643,-1.1177,-1.7145;8.6681,-0.524,-2.6481;8.0347,-2.3972,-2.0517;8.5625,-3.1154,-2.6688;6.7436,-2.5723,-1.7199;6.2122,-3.4668,-2.0326)|",3.986467909825
"[H]OC(=N/C([H])([H])C1([H])C([H])([H])C1([H])[H])/C([H])=C(\[H])C1=C([H])C([H])=NC([H])=C1[H] |(-5.6711,-0.9567,-2.0792;-5.0747,-0.9478,-1.3144;-3.7876,-1.1047,-1.7643;-2.8617,-0.7057,-0.9909;-1.4611,-0.9134,-1.3425;-1.2365,-1.9859,-1.4551;-1.2231,-0.4419,-2.3118;-0.5553,-0.3313,-0.2786;-0.7037,0.7342,-0.1144;0.8527,-0.8506,-0.121;1.1723,-1.6685,-0.7634;1.643,-0.1421,0.1118;-0.1889,-1.1457,0.9337;-0.1152,-0.64,1.8923;-0.5758,-2.1599,0.9981;-3.6296,-1.7581,-3.081;-2.6978,-1.5547,-3.601;-4.5335,-2.6042,-3.6128;-5.4239,-2.8439,-3.0316;-4.4393,-3.3,-4.9007;-3.4291,-3.0645,-5.8492;-2.6501,-2.3296,-5.6719;-3.4355,-3.7816,-7.0415;-2.6589,-3.6071,-7.7842;-4.3567,-4.7025,-7.3616;-5.3201,-4.9243,-6.4602;-6.0641,-5.6727,-6.7282;-5.4068,-4.2597,-5.2379;-6.2165,-4.4896,-4.5502)|",4.435455763149999
"[H]OC(=N/C([H])([H])C([H])([H])C([H])([H])[H])/C([H])=C([H])/C1=C([H])/C([H])=C(/[H])C2=C1C([H])=C([H])C([H])=C2[H] |(2.6018,1.6229,-1.2207;3.3312,1.5453,-0.5861;4.5094,1.4954,-1.2906;5.5088,0.9888,-0.6897;6.8232,0.9885,-1.3195;6.8209,0.37,-2.2341;7.1205,2.0025,-1.6321;7.8714,0.4333,-0.3511;7.8621,1.0464,0.5587;7.5617,-0.5734,-0.0436;9.2782,0.3971,-0.9532;10.0053,-0.0032,-0.2382;9.6163,1.4003,-1.2425;9.3145,-0.234,-1.8505;4.4821,2.0689,-2.6518;5.26,1.7281,-3.3292;3.6035,3.0087,-3.0573;2.9026,3.4019,-2.3225;3.5166,3.5739,-4.4104;3.8201,2.7894,-5.5153;4.1082,1.7532,-5.3645;3.7201,3.2886,-6.8301;3.9616,2.6403,-7.6676;3.2974,4.5797,-7.0485;3.2042,4.9681,-8.0597;2.9694,5.4279,-5.9584;3.0847,4.9323,-4.6161;2.7861,5.8225,-3.5486;2.9057,5.491,-2.5226;2.3689,7.1135,-3.7897;2.1461,7.7725,-2.955;2.2369,7.591,-5.1142;1.9071,8.6109,-5.2914;2.5364,6.7643,-6.1719;2.4503,7.124,-7.1945)|",4.008237017865
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"[H]C1=NC2=C(C3N=C(C([H])([H])C([H])([H])[H])NC(=O)[C@]3([H])S2)C([H])=C1[H] |(4.8108,-5.3659,7.9523;4.5834,-4.7774,7.0655;5.1704,-3.5751,7.0056;4.8944,-2.8429,5.9301;4.0335,-3.2514,4.8858;3.9359,-2.2727,3.8266;3.4153,-2.4633,2.6589;3.6027,-1.4195,1.7304;2.7379,-1.5264,0.5046;3.0331,-0.7388,-0.1927;2.9434,-2.497,0.0337;1.2379,-1.4396,0.838;0.6418,-1.5629,-0.0719;0.9869,-0.4672,1.2762;0.9492,-2.2222,1.5463;4.4681,-0.4522,1.8155;5.2465,-0.3369,2.9785;6.295,0.2666,3.017;4.5954,-0.9645,4.2112;3.7958,-0.2665,4.5078;5.6489,-1.2555,5.6872;3.4443,-4.5169,4.965;2.7903,-4.8602,4.1689;3.7245,-5.2936,6.0832;3.292,-6.2809,6.2064)|",3.55380688753
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"[H]OC([H])([H])[C@@]12C([H])([H])N([H])C([H])([H])[C@@]1([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H] |(1.5289,-0.7893,-1.963;0.6508,-0.8163,-2.4024;-0.2566,-1.3237,-1.4412;-1.2536,-1.0392,-1.7886;-0.2325,-2.4296,-1.4238;-0.0399,-0.8538,0.0253;1.3749,-1.2563,0.4789;1.6557,-2.2746,0.1825;1.4597,-1.1882,1.5776;2.2139,-0.2696,-0.2413;3.1448,-0.1834,0.1552;1.4854,1.0318,-0.292;1.9588,1.7952,0.3403;1.474,1.4039,-1.3221;0.0739,0.6822,0.2142;0.0953,0.8317,1.3064;-1.1579,1.428,-0.2923;-1.0442,2.5121,-0.1602;-1.2941,1.2537,-1.3671;-2.3799,0.9335,0.5141;-2.2755,1.2954,1.5477;-3.3024,1.3808,0.1234;-2.5239,-0.6036,0.5362;-2.8842,-0.9459,-0.4417;-3.3034,-0.8849,1.2557;-1.2141,-1.3501,0.8888;-1.3648,-2.4322,0.7731;-0.9702,-1.1807,1.9473)|",8.12531957593
"[H]OC1=NN=C2C([H])([H])C([H])([H])N([H])C([H])([H])[C@@]12[H] |(5.924,-1.9641,0.3886;5.9534,-1.0056,0.2505;4.7047,-0.5522,0.005;4.4782,0.696,-0.2143;3.0663,0.8469,-0.4489;2.4878,-0.299,-0.3167;1.0711,-0.6176,-0.6543;0.5361,-0.9918,0.2308;0.5528,0.2789,-1.0092;1.079,-1.733,-1.7454;1.3994,-1.2924,-2.6975;0.0677,-2.126,-1.8915;1.9724,-2.8555,-1.4539;1.6158,-3.3911,-0.6636;3.3481,-2.4482,-1.2062;3.9529,-3.3384,-0.9979;3.7403,-1.9903,-2.1217;3.4664,-1.4069,-0.0402;3.2831,-1.9163,0.9163)|",5.483094087575
"[H]OC1=N[C@]2([H])N(C([H])([H])[H])C(=O)N=C(O[H])[C@]2([H])N1C([H])([H])[H] |(4.3836,-0.2125,0.3499;4.0507,0.3223,-0.3922;2.958,0.9529,0.0669;2.4727,0.826,1.2443;1.317,1.7404,1.2912;1.5936,2.6119,1.8988;0.1462,1.1502,1.9042;0.2809,0.8555,3.329;0.6361,1.7552,3.8428;0.9924,0.0408,3.5082;-0.6946,0.568,3.7187;-0.7312,0.3529,1.1777;-1.4304,-0.4907,1.7084;-0.8846,0.6269,-0.2054;-0.1325,1.4672,-0.7874;-0.4162,1.7503,-2.0839;0.1708,2.4505,-2.409;1.0648,2.1937,-0.183;0.8964,3.2769,-0.2566;2.3477,1.8318,-0.8072;2.6238,1.875,-2.2321;3.6848,1.6704,-2.3824;2.4233,2.8841,-2.6155;2.044,1.1452,-2.8136)|",5.298056669235001
"[H]C1=C([H])C(/C([H])=N/N2C(=O)N=NC2=O)=C([H])C([H])=C1Cl |(1.1167,-2.4702,-0.1067;0.565,-1.5372,-0.0678;1.2286,-0.3183,-0.1049;2.3109,-0.2856,-0.1732;0.5078,0.8889,-0.0541;1.169,2.1933,-0.0899;0.5337,3.0768,-0.0472;2.4531,2.2599,-0.1682;3.0414,3.5108,-0.1995;4.4335,3.6059,-0.2924;5.2514,2.7383,-0.3557;4.738,5.07,-0.2993;3.68,5.7208,-0.2216;2.5082,4.7899,-0.1516;1.3652,5.1544,-0.0714;-0.8921,0.8446,0.0345;-1.4591,1.7713,0.0746;-1.5681,-0.3724,0.0726;-2.6498,-0.4061,0.1414;-0.8312,-1.5549,0.0209;-1.6686,-3.094,0.068)|",2.8898490923100004
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"[H]OC([H])([H])[C@@]1([H])O[C@]([H])(O[H])[C@]([H])(N([H])[C@@]([H])(O[H])C([H])([H])[H])[C@]([H])(O[H])[C@]1([H])O[H] |(1.2667,-6.5065,-0.5812;1.7244,-7.1467,-0.0077;2.6316,-6.3857,0.7649;2.1358,-5.9409,1.6435;3.3984,-7.0729,1.1407;3.2866,-5.3015,-0.104;3.8153,-5.8206,-0.9132;2.2741,-4.5495,-0.794;1.5465,-3.5667,-0.0125;0.9988,-4.0688,0.7941;0.6177,-2.9749,-0.8417;1.1138,-2.2649,-1.3003;2.5584,-2.5461,0.5464;2.0225,-1.8297,1.1809;3.0559,-1.8172,-0.6311;3.4948,-2.4779,-1.2712;3.8996,-0.6581,-0.4616;3.4187,0.0173,0.2534;5.1893,-0.9477,0.1304;5.5106,-1.7759,-0.2784;4.1048,0.0435,-1.8013;3.1507,0.3849,-2.2141;4.5683,-0.6398,-2.5245;4.7693,0.9022,-1.673;3.5621,-3.3113,1.4457;2.9453,-3.8527,2.1772;4.4148,-2.5059,2.2162;4.8353,-1.853,1.6125;4.2925,-4.4017,0.6402;4.8832,-5.0076,1.341;5.189,-3.7632,-0.2959;5.7884,-4.4386,-0.6487)|",7.611024398485
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"[H]OC1=NC(=S)NC2=NC(C([H])([H])C#N)=NC21 |(4.7282,-3.103,0.4776;5.6584,-2.8209,0.3912;5.6897,-1.4984,0.2296;6.8305,-0.8933,0.1126;6.8665,0.4841,-0.0538;8.337,1.2341,-0.2063;5.7266,1.331,-0.1063;4.6007,0.7169,0.0127;3.3048,1.2515,-0.0005;2.555,0.1994,0.1439;1.0543,0.2477,0.1643;0.7334,1.2933,0.149;0.6681,-0.2368,-0.7438;0.4871,-0.438,1.3312;0.0326,-0.9743,2.2536;3.2153,-1.0794,0.262;4.4626,-0.7336,0.184)|",1.7496920587149989
"[H]OC1=NC(=O)N/C2=N/C(C(=O)OC([H])([H])C([H])([H])[H])=N\C12 |(6.4427,-3.6076,-0.5906;7.0665,-3.5975,-1.3413;7.2236,-2.3383,-1.7494;8.0058,-2.0645,-2.7328;8.1658,-0.7238,-3.1595;8.9043,-0.4699,-4.0814;7.4774,0.4141,-2.5425;6.712,0.1024,-1.5708;5.9142,0.9507,-0.769;5.3523,0.1265,0.0559;4.4063,0.5086,1.1597;4.0184,-0.2967,1.9775;4.0936,1.8036,1.1037;3.1995,2.3035,2.1371;3.468,1.8325,3.0858;3.4268,3.3702,2.1807;1.7437,2.0507,1.7787;1.0968,2.5106,2.5341;1.5291,0.9788,1.7519;1.501,2.4881,0.8052;5.6786,-1.2888,-0.0804;6.4998,-1.2693,-1.0706)|",2.9823678014800006
"[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])C1=NC2C(=N1)NC(=O)N=C2O[H] |(0.0182,3.4097,1.9307;0.361,2.7833,2.6079;-0.1837,1.554,2.4775;0.1495,0.647,3.203;-1.1835,1.3693,1.3343;-1.8601,0.5628,1.629;-1.7826,2.2749,1.1913;-0.4905,0.9625,0.0169;0.0163,0.0027,0.1637;-1.2536,0.8086,-0.7549;0.5807,1.962,-0.5074;1.0159,1.5714,-1.431;1.3768,2.0565,0.2389;0.0275,3.3167,-0.7738;-0.2505,3.7326,-2.1472;-0.7227,4.9194,-1.9832;-0.7755,5.2896,-0.5545;-0.268,4.1853,0.1516;-1.2117,6.4012,-0.1051;-1.6657,7.3358,-1.1326;-2.0767,8.4059,-0.7516;-1.6451,7.0464,-2.5215;-1.2029,5.9138,-2.9377;-1.1807,5.6354,-4.242;-0.8157,4.7394,-4.367)|",2.0789498178200008
"[H]OC1=NC(=O)NC2=NC(C([H])([H])C#N)=NC21 |(4.7002,-2.9882,0.3731;5.637,-2.7302,0.2832;5.7059,-1.4136,0.0847;6.8459,-0.8347,-0.0494;6.9125,0.5654,-0.2617;7.9867,1.0991,-0.4051;5.7355,1.4333,-0.3219;4.6227,0.8268,-0.1816;3.3155,1.3624,-0.1987;2.5686,0.3213,-0.0307;1.0688,0.358,-0.0006;0.7384,1.4,-0.0382;0.6826,-0.1515,-0.8951;0.5193,-0.3078,1.1861;0.0826,-0.8288,2.1256;3.2379,-0.9686,0.1131;4.4767,-0.6294,0.0232)|",3.0558385411149995
"[H]NC1=NC(S[H])N=C(O[H])/C1=C(/[H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.8256,1.126,-0.236;5.5298,0.3992,-0.0797;5.0058,-0.765,-0.2129;5.8402,-1.881,-0.114;5.3898,-2.9947,-0.5852;6.35,-4.4786,-0.461;7.3579,-3.9057,0.2287;4.2052,-3.2337,-1.2769;3.3243,-2.2943,-1.2132;2.2098,-2.4689,-1.942;1.6106,-1.7129,-1.8192;3.5415,-1.0567,-0.4338;2.5928,-0.2452,0.0954;2.9732,0.6404,0.6051;1.125,-0.352,0.1258;0.371,0.8375,0.0695;0.8878,1.7899,-0.0189;-1.0197,0.8057,0.1122;-1.5822,1.7331,0.0537;-1.6881,-0.4145,0.2415;-2.7732,-0.4401,0.2847;-0.9552,-1.5992,0.3345;-1.4667,-2.5487,0.4629;0.4375,-1.573,0.2792;0.9933,-2.4982,0.3897)|",3.5755759955700004
"[H]C1=C([H])C([C@]2([H])N=NC3=NC([H])([H])C([H])([H])C([H])([H])N32)=C([H])C([H])=C1C([H])([H])[H] |(2.4417,-1.678,-2.4272;2.939,-1.1487,-1.6176;4.3262,-1.2068,-1.513;4.9014,-1.7781,-2.2367;4.9875,-0.5358,-0.4775;6.4978,-0.5697,-0.3736;6.8071,-0.1082,0.5792;7.1428,0.2726,-1.4516;7.9952,-0.3832,-2.0845;8.0826,-1.7339,-1.5643;8.9348,-2.5842,-1.9792;8.8976,-3.8739,-1.2812;9.1761,-4.6551,-1.9976;9.6886,-3.8677,-0.5158;7.5417,-4.221,-0.6364;7.6332,-5.1179,-0.0142;6.8135,-4.4434,-1.4258;7.0072,-3.0536,0.2086;7.5819,-2.9503,1.1426;5.9583,-3.2147,0.4818;7.112,-1.8499,-0.5947;4.2302,0.1915,0.4445;4.7288,0.7164,1.2564;2.8412,0.2486,0.3343;2.2693,0.8153,1.0654;2.1714,-0.4179,-0.6991;0.6694,-0.3322,-0.8361;0.2567,-1.2329,-1.3026;0.3779,0.521,-1.463;0.1841,-0.2007,0.1367)|",4.34293705398
"[H]C1=NC([H])=C([C@@]2([H])/N=N\C3=NC([H])([H])C([H])([H])C([H])([H])N32)C([H])=C1[H] |(0.9302,1.7413,2.4296;0.4349,1.0077,1.7959;-0.7757,0.604,2.1998;-1.4066,-0.2995,1.4421;-2.3886,-0.6153,1.7887;-0.8812,-0.8318,0.2588;-1.6277,-1.8798,-0.5407;-1.3067,-1.8289,-1.595;-1.2543,-3.269,-0.0713;-2.2724,-3.9291,0.2155;-3.4672,-3.141,-0.0059;-4.6434,-3.6011,0.151;-5.696,-2.6284,-0.1554;-6.6036,-3.1797,-0.4219;-5.9302,-2.0619,0.7596;-5.3148,-1.6599,-1.2904;-6.1092,-0.9247,-1.4561;-5.2019,-2.2358,-2.217;-3.9968,-0.9308,-0.9831;-4.16,-0.097,-0.2875;-3.5667,-0.5082,-1.9002;-3.0563,-1.8837,-0.4018;0.3831,-0.3951,-0.1446;0.8325,-0.784,-1.0556;1.0565,0.5399,0.6367;2.0398,0.9035,0.3541)|",4.378311854545
"[H]C1=NC([H])=C([H])C([C@]2([H])N([H])N([H])C3=NC([H])([H])C([H])([H])C([H])([H])N32)=C1[H] |(-1.4643,-5.4936,-2.2263;-1.3556,-4.41,-2.2364;-1.1449,-3.8512,-3.4321;-1.0077,-2.5173,-3.4581;-0.8382,-2.0753,-4.4385;-1.069,-1.7048,-2.328;-0.9424,-0.6316,-2.4285;-1.2958,-2.296,-1.0785;-1.3777,-1.4801,0.2067;-1.6314,-2.1622,1.0272;-2.4037,-0.4158,0.1464;-2.8112,-0.3313,1.0764;-1.6935,0.8184,-0.064;-1.9388,1.2457,-0.9516;-0.3068,0.5789,0.0693;0.5853,1.4543,-0.1878;1.9673,1.003,-0.0379;2.5917,1.5729,-0.736;2.325,1.2588,0.9721;2.1568,-0.5032,-0.2862;3.186,-0.805,-0.0619;1.9754,-0.7176,-1.3468;1.1805,-1.3228,0.567;1.5175,-1.363,1.6119;1.1206,-2.3544,0.2034;-0.1565,-0.7295,0.5113;-1.4386,-3.6846,-1.0454;-1.6191,-4.2024,-0.1061)|",4.8517899544150005
"[H]C1=C(C(=O)C([H])([H])[H])C(=O)C2=C([H])C([H])=C([H])[C@@]([H])(OC([H])([H])C([H])([H])[H])C2=N1 |(7.183,1.4849,-3.9746;6.3,1.7759,-3.412;5.3694,0.8063,-3.1372;5.6505,-0.5819,-3.6462;6.6791,-0.7986,-4.2731;4.663,-1.6958,-3.3761;4.5249,-1.8406,-2.2996;5.0426,-2.6097,-3.838;3.6729,-1.4465,-3.7701;4.1731,1.1817,-2.373;3.2524,0.4188,-2.0684;4.1389,2.6206,-1.97;3.13,3.0808,-1.166;2.3666,2.3655,-0.8684;3.0793,4.4339,-0.6736;2.284,4.7137,0.0108;4.0324,5.3205,-1.0285;4.0473,6.3349,-0.6404;5.1113,4.9917,-2.021;4.7891,5.4857,-2.964;6.3193,5.5687,-1.5762;7.1887,6.0655,-2.6055;7.5062,5.2402,-3.2496;6.6369,6.7949,-3.2221;8.3732,6.7253,-1.9227;9.06,7.1332,-2.6724;8.9188,5.9971,-1.3145;8.0438,7.5415,-1.2712;5.2115,3.5054,-2.3711;6.2477,3.1066,-3.0567)|",2.9469930009150005
"[H]C1=NC2=C(C(=O)[C@@]1([H])C(=O)C([H])([H])[H])C([H])=C([H])C(Cl)=C2C([H])([H])[H] |(2.1312,-4.3108,0.1064;2.7983,-3.4472,0.0768;2.2509,-2.2939,0.0529;3.0584,-1.1384,0.0369;4.4707,-1.2,-0.0158;5.1624,-2.5027,-0.0544;6.3755,-2.6074,-0.1797;4.2738,-3.747,0.0816;4.5175,-4.3965,-0.7723;4.6579,-4.5415,1.3687;3.9288,-4.521,2.3365;5.9522,-5.316,1.3096;6.7507,-4.6863,0.9049;5.834,-6.1671,0.6256;6.2061,-5.6864,2.3047;5.2298,-0.0256,-0.0473;6.3108,-0.1049,-0.093;4.6002,1.2093,-0.0159;5.1707,2.1312,-0.035;3.2046,1.2571,0.0403;2.4501,2.844,0.0792;2.3957,0.1108,0.0654;0.8938,0.2052,0.1231;0.508,0.8002,-0.7123;0.5718,0.7057,1.0439;0.4523,-0.7898,0.0898)|",4.527974472319999
"[H]C1=C([H])C(F)=C2/N=C(/[H])[C@]([H])(C(=O)C([H])([H])[H])C(=O)C2=C1[H] |(8.7147,-0.7792,-1.4398;7.8677,-0.1001,-1.4277;8.086,1.2793,-1.4937;9.086,1.696,-1.5569;7.0012,2.1472,-1.4788;7.2294,3.466,-1.5466;5.6796,1.6837,-1.395;4.6466,2.6345,-1.3876;3.4415,2.2235,-1.2806;2.6593,2.9843,-1.2586;2.9791,0.7925,-1.1687;2.2907,0.5698,-1.998;2.1676,0.5904,0.1492;2.2951,1.3722,1.0663;1.2421,-0.6014,0.186;0.4191,-0.4461,-0.5244;0.8349,-0.7215,1.1917;1.7764,-1.501,-0.1357;4.1064,-0.2486,-1.2594;3.8587,-1.4462,-1.2719;5.4828,0.2849,-1.3293;6.5705,-0.5971,-1.3488;6.3726,-1.6626,-1.301)|",4.421850070625
"[H]C1=C([H])C2=C(/N=C(/[H])[C@]([H])(C(=O)C([H])([H])[H])C2=O)C(Cl)=C1[H] |(3.913,-6.3735,-0.0801;4.2662,-5.3521,-0.1842;3.3757,-4.3254,-0.4762;2.3151,-4.5108,-0.6101;3.842,-3.0121,-0.6024;5.2158,-2.7047,-0.4483;5.7423,-1.4114,-0.5831;4.947,-0.4357,-0.7992;5.3949,0.556,-0.8842;3.4493,-0.5041,-0.9379;3.1483,-0.0634,-1.8996;2.7646,0.3474,0.1793;3.3696,0.62,1.1926;1.3482,0.7885,-0.0995;1.3539,1.5465,-0.8943;0.9107,1.2177,0.804;0.7552,-0.0565,-0.4634;2.8832,-1.9308,-0.9183;1.7,-2.1412,-1.1444;6.095,-3.765,-0.1577;7.8047,-3.467,0.0445;5.6254,-5.0712,-0.0245;6.3303,-5.8641,0.2025)|",4.383754131555
"[H]N1C([H])([H])C([H])([H])C(=O)C([H])([H])[C@]1([H])OC([H])([H])[H] |(1.4187,1.6919,2.5049;1.9181,2.1324,1.7369;0.9912,2.8402,0.8461;0.3226,2.1568,0.2998;0.366,3.5003,1.4564;1.798,3.6709,-0.1648;2.3275,4.4646,0.3815;1.1534,4.1381,-0.9146;2.8502,2.8273,-0.8726;3.0593,2.9034,-2.0666;3.6577,1.9117,0.0418;4.3659,2.5351,0.6032;4.2176,1.1985,-0.5666;2.7657,1.1792,1.0528;3.3788,0.6758,1.816;2.0724,0.1804,0.2927;1.357,-0.7509,1.0783;0.9442,-1.4957,0.3935;0.5251,-0.2865,1.6302;2.0146,-1.2584,1.8027)|",6.13888846728
"[H]C1=NC2=C(C(=S)[C@@]1([H])C(=O)OC([H])([H])C([H])([H])[H])C([H])=C([H])C([H])=C2[H] |(4.7058,1.3176,-0.7058;5.3986,1.0106,0.0793;5.7313,1.8752,0.958;6.6092,1.4816,1.9877;7.2686,0.222,2.0256;7.0324,-0.7415,0.9536;7.9308,-2.0975,0.6809;5.8544,-0.4343,0.0451;6.1078,-0.6977,-0.9871;4.6262,-1.3,0.4108;4.4387,-1.8327,1.4772;3.7821,-1.3336,-0.6382;2.5387,-2.0635,-0.4469;2.2441,-2.3486,-1.4593;2.7518,-2.9615,0.1373;1.4846,-1.1972,0.2234;0.5394,-1.7486,0.2841;1.3105,-0.2801,-0.3487;1.79,-0.9303,1.2389;8.1526,-0.0447,3.0879;8.6526,-1.0073,3.1102;8.3691,0.8918,4.0897;9.0444,0.6612,4.9085;7.7176,2.1311,4.0427;7.8902,2.8674,4.8227;6.8509,2.4236,2.994;6.3413,3.3793,2.9272)|",3.24359709796
"[H]C1=C([H])C(=S)C2=C([H])[C@]([H])(F)C([H])=C([H])C2=N1 |(3.1918,-1.4222,-0.0912;2.3218,-0.7707,-0.0528;2.4818,0.5843,-0.0018;3.4735,1.023,0.0011;1.3477,1.4657,0.0202;1.528,3.1254,0.0291;0.0355,0.7707,0.0028;-1.1477,1.4294,0.0614;-1.1579,2.5132,0.1504;-2.4719,0.7508,-0.0591;-2.8812,1.0091,-1.0539;-3.3658,1.2888,0.8737;-2.4212,-0.7396,0.0609;-3.375,-1.2555,0.1233;-1.2546,-1.4007,0.029;-1.2028,-2.4843,0.0695;0.0278,-0.7053,-0.0292;1.112,-1.434,-0.0539)|",2.413649853934999
"[H]C1=C([H])C(=S)/C2=C([H])/C([H])=C(/[H])[C@]([H])(F)C2=N1 |(3.7463,-2.6814,-4.5593;3.3298,-2.8163,-3.5641;2.6468,-3.9537,-3.2475;2.5176,-4.7434,-3.9794;2.0976,-4.1485,-1.9329;1.3185,-5.5728,-1.5137;2.3022,-3.0114,-1.0182;1.7399,-2.9505,0.2358;1.1388,-3.7963,0.5603;1.8645,-1.8061,1.1024;1.3326,-1.8158,2.0493;2.5896,-0.7315,0.7306;2.666,0.1577,1.3501;3.3835,-0.7279,-0.5378;4.45,-0.819,-0.2653;3.2423,0.4996,-1.1733;3.0751,-1.8712,-1.4974;3.5662,-1.7694,-2.6957)|",2.1551416959600003
"[H]C1=C([H])C(=S)/C2=C([H])/C([H])=C(/[H])[C@]([H])(Cl)C2=N1 |(3.2431,5.4158,1.38;2.9043,4.3996,1.5648;2.3306,4.0644,2.7566;2.2151,4.8031,3.5421;1.8725,2.724,3.0094;1.2102,2.2919,4.4864;2.0389,1.8048,1.8714;1.4817,0.547,1.8316;0.9122,0.2158,2.6962;1.5912,-0.3213,0.6905;1.0761,-1.2769,0.7167;2.3179,0.0347,-0.3922;2.4237,-0.624,-1.2486;3.0624,1.3232,-0.4246;2.9897,1.8357,-1.3848;4.8729,0.8696,-0.3258;2.7409,2.2895,0.6926;3.1256,3.5216,0.524)|",2.07350754081
"[H]C1C(=S)C2=C(N=C1[H])C(C([H])[H])=C([H])C(C([H])([H])[H])=C2[H] |(7.2488,-3.3922,0.0855;6.6629,-2.4797,0.0844;5.2356,-2.5877,0.0831;4.4697,-4.0876,0.0795;4.5331,-1.2942,0.0842;5.321,-0.0703,0.0838;6.6353,-0.0514,0.083;7.2802,-1.2565,0.0836;8.3665,-1.1931,0.0834;4.5959,1.2235,0.0848;5.2707,2.4017,0.0853;4.7415,3.3501,0.0861;6.3547,2.4151,0.0847;3.1498,1.2057,0.0855;2.634,2.1632,0.0862;2.437,0.0411,0.0855;0.9292,0.013,0.0862;0.5101,1.0234,0.0764;0.5447,-0.5058,0.9729;0.5443,-0.5232,-0.7899;3.1565,-1.1987,0.0857;2.5953,-2.1312,0.087)|",1.896633537985
"[H]C1=C([H])C(=S)[C@]2([H])C(=N1)C(OC([H])([H])[H])=C([H])C([H])=C2OC([H])([H])[H] |(1.3364,0.4494,-6.2146;1.8277,-0.0303,-5.3705;1.6262,-1.3645,-5.1362;0.9692,-1.9402,-5.7796;2.1767,-2.0098,-3.987;1.6506,-3.5024,-3.4761;3.1726,-1.1676,-3.1866;2.6942,-1.0491,-2.1948;3.3091,0.2887,-3.6474;2.6007,0.8141,-4.6116;4.2621,1.0892,-2.9094;4.3258,2.4479,-3.0175;3.1123,3.1908,-2.8492;2.4328,3.0321,-3.6886;3.4169,4.2384,-2.8006;2.615,2.9158,-1.9096;5.2802,0.4501,-2.249;6.041,1.0727,-1.7871;5.4614,-0.9728,-2.2532;6.3928,-1.3748,-1.8726;4.5113,-1.7848,-2.7961;4.5968,-3.1067,-2.9589;5.7272,-3.7902,-2.4232;5.5553,-4.8468,-2.6285;5.8043,-3.626,-1.3421;6.6511,-3.4594,-2.9129)|",2.6721580119099992
"[H]C1=C([H])C(=S)C2=C([H])[C@]([H])(Br)C([H])=C([H])C2=N1 |(3.2022,-1.4854,0.0417;2.3358,-0.8289,0.0104;2.5032,0.526,-0.0471;3.4972,0.9592,-0.0665;1.3744,1.4125,-0.0953;1.5662,3.0693,-0.1956;0.0602,0.7272,-0.0545;-1.121,1.4011,-0.0648;-1.1081,2.4868,-0.1049;-2.4335,0.7273,-0.019;-3.1254,1.1345,-0.7603;-3.3564,1.3845,1.7118;-2.408,-0.7523,-0.0048;-3.3616,-1.2706,0.0017;-1.2431,-1.4248,0.0073;-1.2082,-2.5096,0.0197;0.0427,-0.7435,-0.0027;1.1234,-1.4821,0.0266)|",2.217727881575001
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"[H]C1C(F)=C([H])[C@@]([H])(F)C2=C1/C1=C(C(=O)N(C([H])([H])[H])N1)\C([H])=N/2 |(3.3041,-1.0698,-3.9164;4.3636,-1.062,-3.6802;5.2977,-1.3779,-4.7265;4.7477,-1.674,-5.9197;6.6312,-1.3755,-4.5663;7.2991,-1.5967,-5.3912;7.2305,-1.0654,-3.2309;7.8336,-1.9172,-2.8856;8.1185,0.0076,-3.3674;6.2314,-0.7341,-2.1235;4.798,-0.736,-2.4208;3.9603,-0.3714,-1.3028;4.5809,-0.0674,-0.0439;3.4637,0.2504,0.8736;3.4405,0.5719,2.052;2.3441,0.093,0.0392;0.9696,0.2915,0.4425;0.9849,0.5636,1.4994;0.392,-0.628,0.3045;0.5094,1.096,-0.1399;2.6464,-0.2728,-1.2461;5.9306,-0.1181,0.0935;6.4299,0.1037,1.0329;6.7583,-0.4595,-0.9613)|",2.01908477071
"[H]OC(NN([H])[H])[C@]1([H])C(=O)C2=C(N=C1[H])/C(F)=C([H])\C(F)=C/2[H] |(2.2804,2.3132,2.3394;3.1336,1.85,2.4873;3.4712,1.2427,1.3364;4.716,1.0694,1.1046;4.9563,0.385,-0.1616;5.5654,1.0304,-0.6708;5.588,-0.3815,0.0828;2.3272,0.7538,0.4304;2.6313,1.0262,-0.5967;0.9836,1.4259,0.6556;0.888,2.5362,1.1774;-0.1945,0.677,0.1787;-0.0458,-0.7062,-0.0751;1.1475,-1.4167,0.1108;2.2147,-0.7615,0.3702;3.1366,-1.3222,0.5033;-1.191,-1.4121,-0.4818;-1.0983,-2.7234,-0.7249;-2.4222,-0.7923,-0.6416;-3.2877,-1.3603,-0.9624;-2.5194,0.572,-0.3686;-3.7132,1.1698,-0.5204;-1.4321,1.3191,0.053;-1.5274,2.376,0.2732)|",3.55924916454
"[H]NC1=NC(N2C([H])([H])C([H])([H])C([H])([H])C2([H])[H])N=C(O[H])C1=S |(2.1041,6.1621,-1.2708;2.2598,5.1915,-0.9777;1.1403,4.5441,-1.0219;1.1172,3.2165,-0.6683;-0.0229,2.5708,-0.7154;-0.0479,1.2688,-0.3572;-1.2334,0.3982,-0.3808;-1.8878,0.6236,0.4721;-1.8124,0.5571,-1.292;-0.6278,-1.0081,-0.2664;-1.3281,-1.7241,0.1735;-0.35,-1.3768,-1.2612;0.6323,-0.7836,0.5872;1.3733,-1.5811,0.4821;0.3618,-0.7172,1.648;1.1631,0.571,0.0935;1.8616,0.4649,-0.7469;1.6742,1.1586,0.861;-1.2764,3.0593,-1.1046;-1.3364,4.298,-1.4513;-2.5139,4.7906,-1.8299;-2.3745,5.7386,-2.0516;-0.1484,5.2005,-1.4493;-0.3202,6.7786,-1.8939)|",2.0027579396800004
"[H]SC1N[C@]([H])(N([H])[H])N([H])[C@@]([H])(N2C([H])([H])C([H])([H])SC([H])([H])C2([H])[H])N1[H] |(-1.193,7.0998,0.0346;-1.2735,6.2892,1.1073;-1.1745,4.7801,0.1365;-1.3444,4.7942,-1.1313;-1.1317,3.4889,-1.7385;-0.0555,3.2645,-1.7439;-1.5984,3.5406,-3.1034;-2.5033,4.0059,-3.1352;-1.7011,2.5978,-3.4726;-1.7789,2.3882,-0.9673;-2.785,2.5479,-1.0303;-1.4086,2.3364,0.456;-2.3359,2.1073,0.9993;-0.464,1.3339,0.8995;-1.0064,-0.0054,1.121;-0.3801,-0.502,1.8743;-2.0116,0.1001,1.5511;-1.0798,-0.9012,-0.1239;-1.7331,-0.4573,-0.881;-1.4675,-1.8905,0.1408;0.5863,-1.1841,-0.8439;1.0778,0.5828,-0.9768;2.1355,0.5803,-1.2601;0.5065,1.0485,-1.7839;0.8921,1.3415,0.3474;1.1977,2.3857,0.2213;1.5578,0.895,1.0982;-0.9034,3.6546,0.88;-0.7401,3.749,1.874)|",5.651804674885001
"[H]OC1=N[C@]([H])(N2C([H])([H])C([H])([H])SC([H])([H])C2([H])[H])N([H])[C@@]([H])(N([H])[H])C1([H])[H] |(-4.9773,3.8259,2.3706;-4.6729,2.9523,2.0807;-3.5553,3.1035,1.3147;-2.9995,2.0495,0.8843;-1.8449,2.1341,0.0085;-2.1809,1.7555,-0.9672;-0.7745,1.2098,0.411;-1.1293,-0.193,0.1569;-1.8932,-0.5572,0.8625;-1.5607,-0.2497,-0.8509;0.0983,-1.1023,0.2039;0.8278,-0.7888,-0.5509;-0.1902,-2.1377,-0.0023;0.9011,-1.1151,1.8543;1.0324,0.7056,2.0321;1.3732,0.8884,3.0561;1.794,1.0838,1.3408;-0.2987,1.4165,1.7838;-0.1374,2.4907,1.9271;-1.0418,1.0881,2.5288;-1.327,3.4943,-0.2137;-0.336,3.4293,-0.4325;-1.5964,4.545,0.768;-1.0671,4.4028,1.7262;-1.2087,5.864,0.29;-1.5116,5.9517,-0.6812;-0.1932,5.9483,0.2809;-3.0988,4.5214,1.0537;-3.6389,4.9261,0.1866;-3.3131,5.1755,1.9073)|",6.247734007479999
"[H]SC1=NC([H])([H])C([H])([H])[C@@]([H])(C2=C([H])SC([H])=C2[H])N1[H] |(-1.8811,-3.01,0.1715;-0.5878,-3.0757,-0.1966;-0.3431,-1.2899,-0.0668;-1.3409,-0.5276,0.1542;-1.0818,0.9092,0.2616;-1.9361,1.4388,-0.1766;-1.0674,1.1849,1.326;0.2144,1.3607,-0.4218;0.4518,2.3989,-0.1677;0.088,1.3034,-1.5098;1.3984,0.4495,-0.0366;2.2266,0.6635,-0.7228;1.9084,0.6596,1.3782;3.0607,1.3467,1.6623;3.7482,1.8045,0.9626;3.3683,1.441,3.366;1.9325,0.5304,3.7134;1.6704,0.2988,4.7371;1.2648,0.1859,2.5727;0.3514,-0.3983,2.5734;0.9751,-0.9328,-0.29;1.6712,-1.6457,-0.1112)|",5.575612796744999
"[H]C1=N[C@@]2([H])C(NON2[H])N([H])C(C2=C([H])C([H])=C([H])C([H])=C2[H])=C1[H] |(4.028,-0.788,-1.7141;4.9302,-1.3364,-1.425;6.0336,-0.8336,-1.8551;7.2582,-1.4322,-1.363;7.3312,-1.2691,-0.2789;7.381,-2.9013,-1.7235;8.2074,-3.1312,-2.6755;8.8151,-1.9062,-3.0131;8.4539,-0.8984,-2.016;9.225,-0.9812,-1.3463;6.639,-3.8764,-1.0913;6.9082,-4.8341,-1.2804;5.3973,-3.6709,-0.5085;4.8912,-4.8436,0.2528;3.5319,-5.1945,0.215;2.849,-4.618,-0.4011;3.0631,-6.2927,0.9328;2.0098,-6.5549,0.8857;3.9435,-7.0633,1.6955;3.5767,-7.9214,2.2517;5.2977,-6.728,1.7358;5.9886,-7.3175,2.332;5.7696,-5.6293,1.0189;6.8204,-5.3595,1.077;4.6678,-2.5179,-0.6141;3.7179,-2.5139,-0.089)|",4.0272849874
"[H]OC1=NC(N(C([H])([H])C([H])=C([H])[H])C([H])([H])C([H])=C([H])[H])=N[C@]2([H])N=NC([H])=C12 |(9.8534,0.3474,5.1918;9.6398,-0.2074,4.4238;8.3083,-0.1683,4.2136;7.8154,-0.927,3.3021;6.4225,-0.8548,3.0651;6.0395,-1.4662,1.9013;6.9315,-2.2993,1.0875;7.8307,-2.4934,1.6737;6.4302,-3.2576,0.9;7.2959,-1.6385,-0.2179;7.8042,-0.6783,-0.1295;7.0414,-2.1538,-1.4211;7.3393,-1.65,-2.3367;6.5345,-3.1103,-1.5357;4.6278,-1.4252,1.5016;4.2099,-0.4855,1.8724;4.5948,-1.4158,0.4069;3.8444,-2.594,2.0436;3.7969,-2.659,3.1295;3.2392,-3.508,1.2844;2.6775,-4.3324,1.716;3.2686,-3.458,0.1972;5.5156,-0.2991,3.8223;6.0093,0.3582,4.9849;5.8243,-0.2797,5.8797;5.3309,1.6089,5.4571;6.2049,2.4635,5.7633;7.5244,1.9976,5.4285;8.3593,2.6765,5.5537;7.4518,0.7402,4.9567)|",3.2027800203850005
"[H]OC(=O)C1=NC(=S)N=C([H])[C@@]1([H])N([H])[H] |(0.5958,-4.4009,-0.0491;0.2012,-3.5535,0.234;0.8507,-2.5895,-0.4352;1.7569,-2.7777,-1.2153;0.3057,-1.2069,-0.1745;-0.9614,-1.022,-0.2357;-1.4283,0.3111,-0.1857;-2.8715,0.6856,-0.8689;-0.6588,1.312,0.4358;0.609,1.13,0.4936;1.212,1.9044,0.976;1.3362,-0.1122,0.0341;1.8349,0.0664,-0.9295;2.3547,-0.5692,0.9829;2.0462,-0.4381,1.9439;3.2306,-0.0692,0.8607)|",2.9796466629750005
"[H]O/C(=N/[C@]1([H])[C@@]([H])(N([H])[H])N([H])N([H])[C@]1([H])N([H])C([H])([H])[H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(2.2027,-2.3976,0.5815;2.3092,-1.5575,1.0555;2.7338,-0.6076,0.1623;2.6915,0.5971,0.5586;3.2631,1.6908,-0.1818;3.6494,1.4058,-1.1742;4.4516,2.3401,0.586;4.1881,2.4,1.6503;5.7341,1.6705,0.4797;5.83,1.2191,-0.4291;5.8097,0.9407,1.1826;4.522,3.7029,0.0395;5.0967,3.6723,-0.8041;3.1896,4.0763,-0.3616;2.8243,4.7122,0.3456;2.2856,2.8917,-0.3546;1.7668,2.8256,-1.3203;1.2621,3.0374,0.6714;1.5999,2.6194,1.537;-0.0333,2.4424,0.345;-0.7303,2.6381,1.1669;-0.0073,1.354,0.173;-0.4364,2.9243,-0.5533;3.2098,-1.1673,-1.1442;2.7047,-0.6967,-2.3656;1.9553,0.0893,-2.368;3.1452,-1.2475,-3.5687;2.7418,-0.8801,-4.5081;4.0968,-2.2698,-3.5673;4.4409,-2.6942,-4.5063;4.6024,-2.7472,-2.3565;5.3438,-3.5413,-2.3495;4.1563,-2.2053,-1.1514;4.5541,-2.5743,-0.2092)|",4.264024037335
"[H]C1=NC([H])=C([H])C(=O)/C1=N/C(=O)C1([H])C([H])([H])C([H])([H])N([H])C([H])([H])C1([H])[H] |(-2.6587,0.9099,-2.495;-3.5918,1.1949,-2.0095;-4.6947,0.9127,-2.6086;-5.9052,1.2924,-2.0007;-6.7853,1.012,-2.5733;-6.0208,1.9409,-0.8215;-6.9874,2.2035,-0.4034;-4.8265,2.3035,-0.0506;-4.8624,2.8685,1.0303;-3.5067,1.9013,-0.7077;-2.4122,2.1909,-0.1116;-1.1249,1.9322,-0.623;-0.6818,2.5536,-1.5708;-0.3439,0.9049,0.1862;-0.6025,1.0531,1.2439;1.1737,1.0766,-0.0019;1.4104,1.0266,-1.0692;1.488,2.0634,0.3564;1.9341,-0.025,0.7434;1.7898,0.1089,1.8349;3.0077,0.0673,0.5448;1.4869,-1.3382,0.2758;2.0453,-2.0701,0.709;0.0709,-1.5627,0.5542;-0.1973,-2.5731,0.2246;-0.1736,-1.4919,1.6338;-0.7713,-0.5317,-0.2066;-1.8347,-0.6945,0.0084;-0.617,-0.6773,-1.2832)|",2.37283277636
"[H]C1=NNC(=O)[C@@]2([H])C1N(C([H])([H])[H])N=C2SC([H])([H])C([H])([H])[H] |(10.167,-2.4259,2.5234;9.3937,-2.1843,3.243;9.6445,-2.5701,4.5287;8.8103,-2.5073,5.5188;7.5092,-1.9836,5.3435;6.6608,-2.0355,6.2069;7.2576,-1.2289,4.013;7.568,-0.2071,4.2976;8.1436,-1.6918,2.9175;7.4253,-1.6429,1.7848;7.8323,-2.0752,0.4615;8.0822,-3.1428,0.4648;8.7019,-1.5024,0.1253;6.9964,-1.8974,-0.2144;6.0694,-1.3352,2.0132;5.9381,-1.1361,3.2931;4.442,-0.7241,4.0834;3.3218,-0.5763,2.6266;3.7393,0.1745,1.9503;3.3181,-1.5358,2.103;1.9238,-0.1914,3.107;1.2524,-0.1078,2.2457;1.9272,0.7725,3.6266;1.5096,-0.9449,3.7849)|",3.1537995272950003
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"[H]OC1=N[C@]([H])(N([H])C([H])([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])C([H])([H])C(=O)N1C([H])([H])[H] |(8.4211,-2.9513,-2.6352;9.0733,-2.3439,-2.2411;8.6261,-2.1313,-0.9824;7.5825,-2.7321,-0.5592;7.1099,-2.344,0.7691;6.5448,-3.206,1.1639;6.2719,-1.1459,0.6577;6.0674,-0.8044,1.5973;5.0086,-1.3619,-0.0554;5.2564,-1.7717,-1.0384;4.3739,-2.1188,0.4447;4.2143,-0.0592,-0.1973;3.935,0.2961,0.807;3.267,-0.2907,-0.7066;4.9294,1.0841,-0.9445;5.8611,1.2973,-0.4044;5.3003,0.6983,-2.3839;5.7715,1.5413,-2.9034;4.4086,0.4128,-2.9587;6.0038,-0.1404,-2.4124;4.0616,2.3513,-0.929;4.5745,3.1895,-1.4154;3.8173,2.658,0.0954;3.115,2.188,-1.4615;8.287,-2.0439,1.7022;8.8356,-2.9757,1.8956;7.9699,-1.6429,2.669;9.2579,-1.0579,1.0799;9.8835,-0.229,1.7146;9.4392,-1.2319,-0.2961;10.4365,-0.4235,-1.0042;10.9161,0.2085,-0.2584;9.9551,0.1986,-1.7637;11.1762,-1.0661,-1.4872)|",5.964735602959999
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"[H]O[C@@]([H])([C@@]([H])(O[H])[C@@]([H])(N([H])[H])C1([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C1([H])[H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(-2.8093,1.8186,3.0878;-2.2384,1.0891,2.7423;-3.0959,0.2139,2.0276;-3.2999,0.5946,1.0133;-2.3544,-1.1419,1.953;-2.362,-1.5358,2.9769;-3.1053,-2.1254,1.204;-3.0681,-1.8694,0.2677;-0.8598,-1.1204,1.5235;-0.4192,-0.245,2.0068;-0.1562,-2.3073,2.0487;-0.6902,-3.1392,1.7924;-0.1535,-2.2783,3.0675;-0.5813,-1.0736,-0.0095;-0.9072,-2.049,-0.4057;0.9366,-0.9571,-0.2925;1.4699,-1.7053,0.2992;1.1053,-1.1949,-1.3532;1.4914,0.4488,-0.0074;1.4376,0.6578,1.0699;2.5569,0.4824,-0.27;0.7231,1.5353,-0.7754;0.9116,1.4155,-1.8532;1.0945,2.5319,-0.5036;-0.7897,1.4428,-0.5193;-1.322,2.1688,-1.1483;-1.0102,1.7077,0.5229;-1.3078,0.0258,-0.8201;-1.1345,-0.1891,-1.8851;-2.3964,-0.0147,-0.6887;-4.4705,0.0719,2.7315;-4.2767,-0.3049,3.755;-5.3474,-0.79,2.0274;-4.8108,-1.5552,1.7408;-5.2169,1.4088,2.8345;-6.2342,1.201,3.1976;-5.3139,1.8284,1.8266;-4.4756,2.3797,3.664;-4.5518,2.1311,4.6495;-4.878,3.3094,3.5662)|",7.458640642204999
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"[H]OC([H])([H])[C@@]([H])(O[H])[C@@]([H])(O[H])[C@@]([H])(O[H])[C@@]([H])(N([H])[H])C([H])([H])C([H])([H])C([H])([H])[H] |(7.0554,4.9521,2.7637;6.093,4.865,2.8681;5.8352,3.4741,2.7906;6.1737,2.9455,3.6953;4.7514,3.3579,2.7091;6.5149,2.8606,1.5674;6.0953,3.3183,0.6646;7.9141,3.2169,1.6995;8.3108,3.2327,0.8151;6.4357,1.3248,1.4699;6.8891,1.0485,0.497;7.1703,0.7323,2.5299;7.9715,1.2793,2.6209;5.0061,0.7662,1.5131;4.5391,1.0985,2.4471;4.2161,1.369,0.4789;4.5677,1.0616,-0.3728;4.8961,-0.7802,1.489;5.4775,-1.1535,2.3394;3.5022,-1.215,1.6715;2.9141,-0.6462,1.0605;3.2014,-1.0024,2.622;5.4619,-1.4209,0.2076;4.8417,-1.1265,-0.6553;6.4728,-1.0363,0.0153;5.515,-2.9527,0.2741;6.1717,-3.2468,1.1052;4.5156,-3.3244,0.5227;6.0157,-3.5939,-1.0235;6.0488,-4.6862,-0.939;7.0264,-3.2507,-1.2792;5.3603,-3.3478,-1.8686)|",7.366121933035
"[H]O[C@@]([H])(C([H])([H])C([H])=C([H])[H])[C@]([H])(O[H])C([H])([H])OC(=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(4.5385,-0.4118,4.5088;5.3907,-0.0246,4.2389;5.7318,-0.6896,3.0294;6.4893,-0.0633,2.5422;6.3319,-2.085,3.3131;5.5439,-2.6901,3.7827;6.6003,-2.5835,2.3713;7.5391,-2.015,4.207;7.3933,-1.475,5.1411;8.7292,-2.5479,3.9276;9.5676,-2.4665,4.6145;8.913,-3.0911,3.0019;4.4765,-0.7335,2.1359;4.1921,0.3121,1.9311;3.4802,-1.3804,2.915;2.7336,-1.5801,2.3164;4.7229,-1.4232,0.789;4.5553,-2.5007,0.8777;5.7411,-1.2404,0.4381;3.9113,-0.9047,-0.3002;2.5937,-1.1728,-0.3117;2.014,-1.7348,0.6037;1.9216,-0.6966,-1.6027;0.4143,-0.9831,-1.5171;-0.0765,-0.6476,-2.4373;-0.0431,-0.4598,-0.6719;0.219,-2.0519,-1.3897;2.1686,0.8202,-1.7681;1.6819,1.1717,-2.6848;3.236,1.0469,-1.8354;1.7483,1.3849,-0.9279;2.5436,-1.4602,-2.7944;2.0631,-1.1402,-3.7258;2.3947,-2.5417,-2.6949;3.6167,-1.2656,-2.8761)|",6.2803876695400005
"[H]C1C2C3=C([H])C([H])=C([H])C([H])=C3[C@@]([H])(C([H])([H])[H])[C@@]2([H])OC1=O |(0.6448,0.4175,1.0088;0.0266,-0.0925,1.7355;-0.7575,-1.1723,1.5977;-1.0242,-2.2936,0.7067;-1.0095,-2.4066,-0.6853;-0.7934,-1.5438,-1.309;-1.298,-3.6459,-1.2578;-1.3025,-3.7529,-2.3388;-1.5891,-4.752,-0.4501;-1.8153,-5.7084,-0.9137;-1.5926,-4.6429,0.9449;-1.8126,-5.5114,1.5607;-1.3072,-3.4124,1.528;-1.1836,-3.0663,3.0142;-1.9881,-3.5249,3.6004;0.1716,-3.4891,3.6048;0.2801,-4.5782,3.5719;0.2529,-3.1601,4.6451;1.0029,-3.0495,3.0435;-1.3486,-1.5226,2.9384;-2.4202,-1.2794,2.9635;-0.6909,-0.6839,3.8792;0.0818,0.2471,3.1809;0.6939,1.1151,3.7464)|",4.87628020096
"[H]OC([H])([H])C1=C([H])[C@]([H])(O[H])[C@]([H])(O[H])[C@@]([H])(OC(=O)C([H])([H])[H])C1=O |(5.2693,-2.5953,-1.7474;6.1956,-2.4058,-1.9772;6.2514,-1.0062,-2.1832;7.2677,-0.774,-2.5168;5.5622,-0.6961,-2.9861;5.9308,-0.2007,-0.9375;6.6425,0.8676,-0.5422;7.5342,1.1723,-1.0884;6.2342,1.7674,0.5858;5.5724,2.5458,0.1849;7.4311,2.3543,1.1257;7.1804,3.1702,1.5871;5.505,0.9966,1.7092;4.9901,1.7169,2.3677;6.4569,0.2435,2.4399;7.2775,0.7703,2.4141;4.4629,-0.0199,1.2061;4.5193,-0.8734,1.8868;3.0992,0.4255,1.3608;2.6417,1.3419,0.4805;3.3389,1.8555,-0.371;1.1733,1.61,0.6893;0.8813,2.4993,0.1298;0.9479,1.7325,1.7522;0.5997,0.7494,0.327;4.7041,-0.5826,-0.2087;3.9009,-1.3855,-0.6693)|",4.922539555545001
"[H]O/N=C1/C([H])=C(C#CC2=C([H])C([H])=C([H])C(C([H])([H])[H])=N2)C([H])([H])C([H])([H])C1([H])[H] |(5.8338,-10.1051,-0.1398;6.5258,-9.4296,-0.0655;5.7809,-8.2413,-0.1303;6.5425,-7.1958,-0.0802;5.853,-5.9176,-0.1077;4.7699,-5.9464,-0.175;6.5077,-4.7294,-0.0233;5.7946,-3.5051,0.001;5.213,-2.4358,0.0229;4.5346,-1.1784,0.0494;5.2725,0.0154,0.1626;6.3547,-0.0209,0.2312;4.5792,1.2192,0.1845;5.1144,2.161,0.2725;3.1881,1.2006,0.0928;2.6168,2.124,0.1067;2.5294,-0.0326,-0.0178;1.0264,-0.1192,-0.127;0.5543,0.864,-0.0415;0.6254,-0.7722,0.6556;0.7401,-0.5606,-1.0886;3.1884,-1.1989,-0.0383;8.0263,-4.6752,0.0623;8.388,-3.7774,-0.4513;8.3218,-4.5625,1.1166;8.6673,-5.9327,-0.538;9.7489,-5.9272,-0.3602;8.5226,-5.9192,-1.6262;8.0496,-7.2173,0.0331;8.3163,-7.3265,1.095;8.4403,-8.1049,-0.4719)|",3.82592073803
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"[H]O[C@@]([H])([C@@]([H])(O[H])[C@@]([H])(N([H])[H])C([H])([H])C([H])([H])C([H])([H])[H])[C@]([H])(O[H])C([H])([H])N([H])[H] |(7.4271,0.2019,1.0327;7.0824,-0.3687,0.3196;6.246,0.5111,-0.4429;6.8795,1.1764,-1.0493;5.4815,-0.4164,-1.4014;6.265,-0.884,-2.0216;4.7966,-1.4347,-0.6735;5.4377,-1.7263,-0.0013;4.4719,0.2111,-2.3773;4.2109,-0.6114,-3.0701;3.2917,0.6876,-1.6403;2.5733,0.9734,-2.3032;2.91,-0.1088,-1.132;5.089,1.3401,-3.2182;5.3036,2.2016,-2.5728;6.0532,0.9919,-3.6162;4.2094,1.7958,-4.3913;3.9562,0.9249,-5.0127;3.258,2.1951,-4.0132;4.8818,2.8651,-5.2595;4.2317,3.1727,-6.0861;5.8186,2.4915,-5.6906;5.122,3.7593,-4.6717;5.4802,1.4213,0.5321;4.915,2.1689,-0.0413;6.5128,2.0531,1.3009;6.0564,2.288,2.1386;4.5259,0.7164,1.5121;5.0467,-0.1324,1.9653;3.6461,0.3386,0.9838;4.2149,1.6737,2.594;3.4992,2.3294,2.2831;3.8337,1.1893,3.4031)|",8.220559423605
"[H]C(=O)C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(OC([H])([H])C1=C([H])C([H])=C(OC([H])([H])[H])C([H])=C1[H])C([H])([H])[H] |(1.0375,0.1254,3.1421;1.3446,1.1338,3.5077;0.5399,2.0371,3.5696;2.8033,1.2392,3.8935;3.0032,2.2651,4.2223;2.9602,0.5704,4.7537;3.743,0.8083,2.75;3.4847,-0.2034,2.416;4.7676,0.7542,3.1409;3.7019,1.7547,1.5439;3.9916,2.7678,1.854;2.6761,1.8277,1.1572;4.6133,1.3141,0.3946;5.6441,1.21,0.7775;4.1469,0.0279,-0.0229;5.1001,-0.7631,-0.7224;5.4406,-0.2349,-1.6281;5.9916,-0.9309,-0.0956;4.4715,-2.0823,-1.0966;3.2348,-2.1194,-1.7616;2.7165,-1.1881,-1.971;2.6594,-3.3249,-2.1347;1.702,-3.3599,-2.6453;3.3152,-4.5356,-1.8586;2.6693,-5.6658,-2.2727;3.2781,-6.9192,-2.0109;2.6004,-7.6725,-2.4169;3.4054,-7.09,-0.9334;4.2546,-7.0062,-2.5062;4.5463,-4.5175,-1.1964;5.0708,-5.4369,-0.9626;5.1061,-3.2918,-0.8191;6.0602,-3.2907,-0.2963;4.6057,2.32,-0.7606;4.9796,3.2945,-0.4261;3.5868,2.4527,-1.1409;5.2401,1.9897,-1.5897)|",5.38241196289
"[H]C([H])([H])O/N=C1\[C@@]2([H])C([H])([H])[C@@]3([H])C([H])([H])[C@]1([H])C([H])([H])[C@@](F)(C2([H])[H])C3([H])[H] |(0.97,-0.5941,1.1462;1.3206,-0.4667,0.1187;1.0258,-1.3345,-0.4839;0.8783,0.4386,-0.3144;2.7347,-0.358,0.2102;3.2541,-0.1869,-1.0896;4.5294,-0.077,-1.093;5.4789,-0.1096,0.0883;4.9219,-0.2337,1.0181;6.4646,-1.2874,-0.1236;7.1728,-1.3239,0.7151;5.9154,-2.2371,-0.1247;7.222,-1.1048,-1.4564;7.9249,-1.9353,-1.5974;6.2106,-1.0759,-2.6227;5.6545,-2.0207,-2.6684;6.7357,-0.9613,-3.5805;5.2252,0.1033,-2.426;4.4776,0.1233,-3.2248;6.019,1.4334,-2.3886;5.3413,2.2853,-2.2573;6.5612,1.5826,-3.3308;7.0165,1.3841,-1.2258;7.7341,2.5907,-1.1956;6.271,1.2232,0.1041;6.993,1.2245,0.9304;5.5965,2.0747,0.2534;8.0024,0.2285,-1.4231;8.5567,0.3804,-2.358;8.7342,0.233,-0.6053)|",6.557943797049999
"[H]C1([H])OO[C@@]2(O1)[C@@]1([H])C([H])([H])[C@]3([H])C([H])([H])[C@](F)(C1([H])[H])C([H])([H])[C@@]2([H])C3([H])[H] |(-3.5518,-0.5529,0.8226;-3.0371,-0.5682,-0.1483;-3.6138,-1.1124,-0.9025;-2.8436,0.7417,-0.6384;-1.7126,1.135,0.2013;-0.8454,-0.0021,0.0763;-1.7439,-1.1181,-0.0152;0.0447,0.0681,-1.1812;-0.608,0.1286,-2.0581;0.9505,1.3149,-1.0899;1.5764,1.3763,-1.9895;0.3345,2.2206,-1.0601;1.834,1.2255,0.1719;2.4858,2.1059,0.23;2.699,-0.0538,0.105;3.3397,-0.1403,0.9919;3.3559,-0.0338,-0.7736;1.7799,-1.2762,0.0237;2.5678,-2.4365,-0.0316;0.9136,-1.2076,-1.2392;1.56,-1.1874,-2.1254;0.2858,-2.1022,-1.3039;0.8867,-1.3521,1.2673;0.2564,-2.2461,1.2155;1.5124,-1.4345,2.1642;0.0182,-0.0769,1.3466;-0.6518,-0.1262,2.2117;0.929,1.1698,1.4214;0.3221,2.0798,1.493;1.539,1.1154,2.3327)|",7.20013248423
"[H]OC([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])/C([H])=C(\[H])C(=O)C([H])([H])[H] |(2.8797,-7.1143,-12.1489;2.459,-7.7676,-11.5686;1.6969,-7.0502,-10.6033;0.8978,-6.462,-11.0857;1.2072,-7.8172,-9.9929;2.5538,-6.142,-9.7194;3.3442,-6.7506,-9.2607;3.0614,-5.4003,-10.3561;1.746,-5.4157,-8.6363;0.9483,-4.8235,-9.1089;1.2382,-6.1581,-8.0031;2.5997,-4.4981,-7.7512;3.3959,-5.0905,-7.2769;3.1091,-3.7554,-8.3827;1.7922,-3.7729,-6.6668;1.0006,-3.1752,-7.1425;1.2759,-4.5159,-6.0407;2.6465,-2.8639,-5.7743;3.1656,-2.1217,-6.3965;3.4356,-3.4622,-5.2945;1.8328,-2.1442,-4.6917;1.3142,-2.886,-4.0696;1.0474,-1.5371,-5.1644;2.6799,-1.2426,-3.7664;3.4557,-1.84,-3.2701;2.0229,-0.8541,-2.9733;3.2996,-0.0775,-4.4766;2.6249,0.5911,-5.014;4.6066,0.2206,-4.5146;5.3356,-0.4049,-4.0007;5.1121,1.4154,-5.2464;4.3681,2.1723,-5.8532;6.6132,1.6469,-5.2001;6.9495,1.7709,-4.1625;6.8692,2.5381,-5.7759;7.1489,0.7793,-5.6066)|",5.2844509767100005
"[H]OC1=N/C(=N\C([H])([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])[C@]([H])(N([H])[H])C(=O)N1C([H])([H])[H] |(9.446,0.7391,-0.8739;9.7465,1.654,-1.026;8.6444,2.3289,-1.4031;7.5197,1.7153,-1.5119;6.3861,2.4677,-1.87;5.1788,2.1308,-1.6418;4.8694,0.874,-0.9739;4.3131,0.2641,-1.6994;5.7646,0.3086,-0.688;3.9558,1.1141,0.2398;3.6287,0.1352,0.6213;3.0562,1.6368,-0.1107;4.5817,1.9216,1.3935;4.922,2.8811,0.9778;3.5244,2.2216,2.4663;3.9441,2.8206,3.2835;3.135,1.2921,2.903;2.6747,2.7741,2.0481;5.7959,1.2146,2.0152;6.2113,1.8051,2.8412;6.5962,1.0566,1.2846;5.5119,0.2344,2.4218;6.6445,3.7546,-2.6362;5.79,4.4205,-2.5095;6.7839,3.4689,-4.0781;7.5591,2.8266,-4.2401;7.0045,4.3354,-4.5684;7.8877,4.4934,-2.1364;8.0471,5.6923,-2.2753;8.9044,3.6721,-1.6162;10.209,4.2553,-1.2785;10.1434,5.3184,-1.5049;11,3.7902,-1.8718;10.4276,4.112,-0.2176)|",5.16472088249
"[H]OC1=N/C(=N/C([H])([H])C2([H])C([H])([H])C2([H])[H])[C@]([H])(N([H])[H])C(=O)N1C([H])([H])[H] |(3.4438,3.3371,0.5951;2.6045,2.8404,0.6084;2.9327,1.6012,0.1928;4.1434,1.3358,-0.1265;4.496,0.046,-0.5462;5.7396,-0.1924,-0.6954;6.2326,-1.4898,-1.1243;5.5033,-2.3101,-1.0352;6.4676,-1.4137,-2.1956;7.4912,-1.8435,-0.3549;8.2649,-1.0808,-0.4123;7.9519,-3.2781,-0.2888;7.339,-4.0356,-0.7721;9.0185,-3.481,-0.3348;7.3986,-2.6026,0.9447;8.0845,-2.3441,1.7469;6.4117,-2.9042,1.2894;3.3637,-0.951,-0.8236;3.5895,-1.8928,-0.3086;3.2991,-1.2618,-2.2607;3.1227,-0.4095,-2.7922;2.5038,-1.8806,-2.4198;1.9794,-0.5443,-0.2977;1.0416,-1.323,-0.3529;1.8297,0.7554,0.1777;0.5133,1.2176,0.6366;-0.1655,0.3708,0.5515;0.1625,2.0451,0.0152;0.5721,1.5528,1.6745)|",5.1619997439850005
"[H]OC1=N/C(=N\C([H])([H])C([H])([H])C([H])(C([H])([H])[H])C([H])([H])[H])[C@]([H])(NO)C(=O)N1C([H])([H])[H] |(9.5239,0.4656,1.4945;9.9104,-0.1088,2.1812;8.916,-0.9263,2.5636;7.7563,-0.8299,2.0168;6.7457,-1.6947,2.445;5.6517,-1.9285,1.8346;5.37,-1.2814,0.5597;6.102,-0.5005,0.3115;5.4443,-2.0581,-0.2136;3.9475,-0.7037,0.5765;3.8777,0.0057,1.4122;3.2386,-1.5126,0.8;3.5321,0.0113,-0.7239;4.2868,0.7845,-0.9383;3.4774,-0.9396,-1.93;3.162,-0.4056,-2.8343;2.7575,-1.7502,-1.7554;4.4499,-1.3975,-2.142;2.181,0.7171,-0.5335;1.8831,1.2575,-1.4399;2.2194,1.4383,0.2915;1.3898,-0.0085,-0.3037;6.945,-2.3475,3.8016;6.3012,-3.2135,3.9451;6.5465,-1.2588,4.8417;5.6149,-1.5965,5.5163;8.3887,-2.6931,4.1086;8.7078,-3.5969,4.855;9.3284,-1.8325,3.5273;10.7465,-1.9571,3.8912;10.8118,-2.7472,4.6372;11.1121,-1.0144,4.3035;11.3434,-2.2221,3.0152)|",3.583739411085
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"[H]C1=C(C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H])[C@]2([H])C(=NN=C2C([H])([H])OC([H])([H])[H])OC1=O |(3.8446,-3.615,0.0008;4.25,-3.2175,0.9235;5.2463,-3.8303,1.5893;5.9273,-5.0984,1.1504;6.9838,-4.8553,0.9544;5.9553,-5.7793,2.0131;5.3373,-5.8093,-0.0722;4.2819,-6.0495,0.1159;5.3527,-5.1349,-0.9391;6.0975,-7.0956,-0.4224;7.1528,-6.853,-0.6117;6.09,-7.7683,0.4468;5.5142,-7.8227,-1.6378;6.0766,-8.7357,-1.8619;5.5409,-7.1874,-2.5313;4.4695,-8.1085,-1.4654;5.6379,-3.2224,2.9225;5.0836,-3.7624,3.7103;5.3042,-1.7636,2.9479;6.2317,-1.0067,3.4088;7.3183,-1.8735,3.7801;7.0513,-3.0899,3.4459;8.0072,-4.209,3.7276;8.7628,-3.8515,4.4419;8.5379,-4.5084,2.8057;7.2724,-5.3082,4.2424;8.1045,-6.4004,4.5915;7.4515,-7.1903,4.9697;8.8246,-6.1219,5.3753;8.6627,-6.7784,3.7207;4.1455,-1.2858,2.4431;3.5544,-2.002,1.3908;2.5313,-1.5746,0.9266)|",5.191932267540001
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"[H]OC1=N[C@]([H])(N([H])[H])N([H])[C@@]([H])(N2C([H])([H])C([H])([H])C([H])([H])[C@@]([H])(N([H])[H])C2([H])[H])C1([H])[H] |(-5.6736,-1.8039,-0.9526;-5.8739,-1.7178,-0.0082;-4.7383,-1.3648,0.6544;-4.7991,-1.2218,1.9142;-3.5345,-0.8255,2.5299;-2.8345,-1.674,2.5174;-3.8065,-0.4858,3.9089;-4.6656,0.0584,3.9566;-3.0411,0.0707,4.2834;-2.8651,0.2831,1.8105;-3.4531,1.105,1.9422;-2.664,0.0536,0.3641;-3.0545,0.9335,-0.1583;-1.2639,-0.0486,-0.0532;-0.5953,1.2595,-0.0531;-1.2223,1.9643,-0.6134;-0.4939,1.6585,0.974;0.7844,1.1631,-0.7095;0.6543,0.8901,-1.7649;1.2724,2.1455,-0.6855;1.6499,0.1091,-0.0067;1.8822,0.4473,1.0155;2.6101,-0.018,-0.525;0.9091,-1.2363,0.0821;0.7579,-1.6192,-0.9361;1.6056,-2.2871,0.8354;1.8269,-1.9414,1.7702;2.5035,-2.4829,0.3938;-0.4784,-1.03,0.7048;-0.3614,-0.7169,1.7602;-0.9889,-1.997,0.7133;-3.4721,-1.1735,-0.1506;-2.8678,-2.0824,-0.0486;-3.6702,-1.0493,-1.2211)|",6.364742963195
"[H]OC1=N[C@]([H])(N([H])[H])N([H])[C@@]([H])(N2C([H])([H])C([H])([H])C([H])(N([H])[H])C([H])([H])C2([H])[H])C1([H])[H] |(-0.1824,-5.527,-0.9069;0.605,-5.3229,-1.4347;1.2737,-4.2813,-0.8543;2.3135,-3.8616,-1.4412;3.0699,-2.7789,-0.8306;2.8682,-1.8866,-1.4466;4.5074,-2.9978,-0.874;4.7778,-3.1311,-1.8479;4.6973,-3.8832,-0.4026;2.7335,-2.5845,0.5769;3.3159,-1.8581,0.979;1.3258,-2.4431,0.8741;1.2401,-2.3322,1.9715;0.6526,-1.2984,0.2034;1.4097,-0.0457,0.3298;1.4808,0.2753,1.3914;2.43,-0.2027,-0.0259;0.7611,1.0711,-0.4958;0.7868,0.7851,-1.5566;1.3512,1.9917,-0.3901;-0.6976,1.3221,-0.0777;-0.6944,1.6969,0.9572;-1.4218,2.3154,-0.8791;-1.4141,2.0249,-1.8579;-0.9232,3.2051,-0.8521;-1.4517,-0.0102,-0.0922;-2.4614,0.1302,0.3099;-1.5564,-0.3558,-1.1304;-0.7107,-1.085,0.7094;-1.2756,-2.0192,0.6503;-0.691,-0.7969,1.7819;0.6926,-3.7857,0.4553;-0.3968,-3.7193,0.3679;0.9054,-4.5304,1.2334)|",6.73753893838
"[H]OC1=N[C@]([H])(N([H])[H])N([H])[C@@]([H])(N2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C1([H])[H] |(-0.1538,-5.6782,-0.6613;0.647,-5.5507,-1.1927;1.3159,-4.4497,-0.7341;2.3782,-4.123,-1.3396;3.1324,-2.9762,-0.8543;2.9555,-2.1692,-1.5846;4.5678,-3.2125,-0.8314;4.8595,-3.4753,-1.7723;4.7341,-4.0311,-0.2444;2.7631,-2.5994,0.5075;3.3442,-1.8317,0.8271;1.3491,-2.3999,0.7351;1.2278,-2.1386,1.8032;0.7287,-1.3452,-0.1085;1.4886,-0.0866,-0.0654;1.5208,0.3324,0.9599;2.516,-0.2729,-0.3868;0.8693,0.9498,-1.0018;0.9557,0.5753,-2.0392;1.4382,1.884,-0.9312;-0.513,1.2025,-0.5996;-0.9292,1.9065,-1.2055;-1.2899,-0.033,-0.6614;-2.3162,0.173,-0.3353;-1.3352,-0.4657,-1.6786;-0.6639,-1.0688,0.2724;-1.2552,-1.9866,0.2116;-0.7288,-0.6978,1.3144;0.7073,-3.778,0.4816;-0.3789,-3.7142,0.3597;0.8925,-4.4128,1.3578)|",6.745702353895
"[H]C1=N[C@]2([H])C(=C1[H])C(=O)C(F)=C([H])N2[H] |(0.3173,2.4286,1.3942;1.1292,1.8487,0.9587;2.2554,2.4104,0.6594;3.0395,1.3594,0.0182;3.0542,1.6381,-1.0553;2.2584,0.0766,0.1432;1.0615,0.4066,0.6708;0.2269,-0.2533,0.8732;2.8796,-1.2316,-0.1807;2.2533,-2.2688,-0.3668;4.3388,-1.1299,-0.1453;5.044,-2.2587,-0.3649;5.011,0.0053,0.1938;6.0933,-0.0201,0.2717;4.4026,1.2005,0.4939;4.9809,2.0307,0.4527)|",3.67353698175
"[H]OC([H])([H])C([H])([H])C([H])([H])[C@]1([H])C(=O)C([H])=C([H])N=C1[H] |(3.271,1.8676,-1.0954;2.4165,1.4122,-1.0663;2.5275,0.324,-0.1509;3.281,-0.4035,-0.4924;2.8369,0.6774,0.8468;1.1675,-0.3559,-0.0555;0.8855,-0.7044,-1.0587;1.2658,-1.2429,0.582;0.082,0.571,0.5075;0.0528,1.4942,-0.0814;0.3338,0.8448,1.5373;-1.3238,-0.0659,0.5219;-2.0108,0.6886,0.9529;-1.4396,-1.2642,1.4725;-0.8361,-1.2978,2.5372;-2.3351,-2.334,1.0241;-2.5242,-3.156,1.7077;-2.8344,-2.3266,-0.2328;-3.4658,-3.14,-0.5831;-2.5883,-1.3439,-1.2077;-1.8783,-0.3337,-0.8529;-1.6796,0.4243,-1.6159)|",4.5170899183
"[H]OC1=NC([H])([H])[C@]([H])(C(=O)OC([H])([H])[H])C([H])([H])[C@@]1([H])C1=C([H])C([H])=NC([H])=C1[H] |(3.8507,-1.3039,6.9715;4.0902,-1.9909,6.3299;3.8193,-1.5404,5.0688;3.9727,-2.3644,4.1201;3.7065,-1.9671,2.7486;4.6739,-1.8323,2.2422;3.2103,-2.8023,2.2433;2.8544,-0.6829,2.5867;1.819,-0.9285,2.8553;2.8596,-0.2304,1.1397;3.447,0.7379,0.7054;2.1394,-1.0755,0.368;2.1163,-0.7632,-1.0355;1.5043,-1.5384,-1.4973;3.1285,-0.7741,-1.4483;1.6768,0.224,-1.2007;3.3965,0.403,3.5158;2.8277,1.3322,3.4153;4.4287,0.632,3.2287;3.3685,-0.0845,4.9847;4.1062,0.5102,5.5426;2.014,0.1365,5.6542;1.0279,-0.8542,5.722;1.2083,-1.8502,5.3282;-0.1976,-0.5534,6.3206;-0.9727,-1.3156,6.3824;-0.5024,0.6387,6.8455;0.4456,1.5829,6.7814;0.1894,2.5482,7.2151;1.7008,1.3842,6.2086;2.4253,2.195,6.193)|",5.727996553024999
"[H]C1=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C([H])=C(C#N)C1=O |(-0.8368,2.3776,0.0454;-0.4366,1.3693,0.0894;0.8769,1.1394,0.3034;1.5348,1.9902,0.4372;1.4425,-0.2069,0.3662;2.7827,-0.4415,0.5807;3.4154,-1.8117,0.6413;3.8287,-1.9557,1.6495;2.7129,-2.6247,0.4562;4.5884,-1.9173,-0.3583;4.1807,-1.8718,-1.3863;5.0759,-2.8905,-0.2381;5.5583,-0.8643,-0.0775;6.3747,-0.97,-0.6757;4.9787,0.4636,-0.2463;5.7487,1.2156,-0.0433;4.5949,0.6455,-1.2685;3.8165,0.6455,0.7549;3.4075,1.6508,0.6517;4.2373,0.5614,1.7668;0.4931,-1.2942,0.1959;0.8445,-2.3174,0.2441;-0.8351,-1.086,-0.0207;-1.7227,-2.1972,-0.182;-2.4225,-3.1175,-0.3101;-1.4101,0.2858,-0.0984;-2.6033,0.4873,-0.3002)|",3.7578922754050006
"[H]C1=C([H])C(C#N)=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C1=O |(-2.3729,0.6933,-0.1164;-1.3318,0.3873,-0.0994;-0.3321,1.2698,0.1118;-0.5377,2.3229,0.281;1.0494,0.8255,0.1093;2.0952,1.7804,0.3166;2.9368,2.5663,0.4882;1.351,-0.4933,-0.0821;2.3962,-0.7724,-0.0731;0.3415,-1.5135,-0.2855;0.7012,-2.8447,-0.3962;2.1295,-3.3414,-0.3484;2.3574,-3.7816,-1.3293;2.866,-2.5604,-0.1615;2.3008,-4.4581,0.7046;2.1553,-4.0191,1.7108;3.3271,-4.8376,0.6562;1.3751,-5.5436,0.4094;1.5295,-6.3192,1.0502;-0.0109,-5.0927,0.4882;-0.6731,-5.9389,0.277;-0.2808,-4.7032,1.4886;-0.2726,-3.986,-0.555;-1.3013,-3.6417,-0.5045;-0.1093,-4.4229,-1.5517;-1.095,-1.0403,-0.3585;-2.0564,-1.7641,-0.6292)|",3.3742117462000003
"[H]C1=C([H])C(Cl)=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C1=O |(-2.4146,0.8166,-0.0795;-1.3695,0.526,-0.0419;-0.3893,1.4217,0.2124;-0.6116,2.4686,0.396;0.9846,0.9872,0.2349;2.2166,2.2052,0.5443;1.3307,-0.3048,0.0304;2.3807,-0.5615,0.0597;0.3389,-1.3427,-0.2238;0.722,-2.6613,-0.3621;2.1569,-3.1371,-0.2788;2.4336,-3.5359,-1.265;2.8702,-2.3509,-0.0322;2.3087,-4.2881,0.7384;2.116,-3.8895,1.7536;3.3428,-4.6494,0.717;1.4151,-5.3794,0.3704;1.5581,-6.1712,0.9939;0.0184,-4.9541,0.4124;-0.6192,-5.8043,0.1476;-0.2937,-4.6064,1.4162;-0.224,-3.8147,-0.5979;-1.2601,-3.4906,-0.5714;-0.0183,-4.2123,-1.6034;-1.1026,-0.893,-0.3193;-2.0508,-1.6252,-0.6188)|",3.36876946919
"[H]C1=C([H])C(C([H])([H])[H])=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C1=O |(4.8584,-2.4753,-0.2218;4.3789,-1.5022,-0.178;3.043,-1.3627,-0.0237;2.4111,-2.2449,0.0687;2.4119,-0.0582,0.0141;0.9137,0.0203,0.1624;0.5775,-0.4691,1.0863;0.403,-0.487,-0.667;0.5658,1.0576,0.1859;3.1935,1.0482,-0.0837;2.6995,2.0124,-0.0539;4.646,1.0158,-0.2261;5.384,2.1799,-0.2525;4.7825,3.5659,-0.1429;4.9748,4.0918,-1.0889;3.7048,3.5669,0.0192;5.4555,4.382,0.9806;5.2007,3.9173,1.9535;5.0434,5.3975,0.9827;6.8917,4.453,0.7373;7.3337,5.0409,1.4412;7.4957,3.1222,0.7466;8.5731,3.2223,0.5754;7.3614,2.5982,1.7127;6.8885,2.2518,-0.3714;7.334,1.2613,-0.3726;7.125,2.7337,-1.3323;5.2798,-0.3539,-0.3342;6.4791,-0.5637,-0.5493)|",3.4313556548050004
"[H]C1=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C(=O)C(F)=C1[H] |(1.7562,1.7539,0.4583;0.9887,1.0125,0.2583;1.3284,-0.285,0.0627;2.3768,-0.5496,0.1095;0.3462,-1.331,-0.2066;0.7258,-2.6496,-0.3516;2.1595,-3.1262,-0.2514;2.4511,-3.519,-1.2358;2.8677,-2.3409,0.0113;2.2952,-4.2836,0.7609;2.0857,-3.891,1.7753;3.3296,-4.6448,0.7544;1.4084,-5.3731,0.3721;1.5416,-6.169,0.9928;0.0106,-4.9494,0.3928;-0.6211,-5.7982,0.1099;-0.3198,-4.6097,1.3933;-0.2145,-3.8024,-0.613;-1.2518,-3.4816,-0.6017;0.0105,-4.1922,-1.6174;-1.0973,-0.8965,-0.3146;-2.052,-1.6196,-0.6038;-1.3516,0.5335,-0.052;-2.6407,0.899,-0.1136;-0.3862,1.4394,0.2078;-0.6528,2.4794,0.371)|",3.33611580713
"[H]C1=C([H])C(F)=C([H])C(=C2C([H])([H])C([H])([H])N([H])C([H])([H])C2([H])[H])C1=O |(-2.4076,0.8308,-0.0963;-1.3628,0.542,-0.0476;-0.3814,1.4342,0.2153;-0.5856,2.4855,0.3984;0.982,0.987,0.2498;1.9198,1.9282,0.501;1.3403,-0.2978,0.0504;2.3949,-0.534,0.0979;0.3493,-1.3344,-0.2146;0.7253,-2.6537,-0.3536;2.1601,-3.1296,-0.2684;2.4423,-3.5186,-1.2572;2.8695,-2.3426,-0.0123;2.3114,-4.2888,0.7385;2.1166,-3.8994,1.7571;3.3456,-4.6501,0.7162;1.4186,-5.3776,0.3604;1.5603,-6.1734,0.9792;0.0215,-4.9522,0.4054;-0.6159,-5.8009,0.1352;-0.2895,-4.6121,1.4121;-0.2216,-3.8054,-0.5957;-1.2578,-3.4815,-0.5643;-0.0204,-4.196,-1.605;-1.0913,-0.8794,-0.3217;-2.0392,-1.6088,-0.6302)|",3.35244263816
"[H]OC1=NC([H])([H])[C@]([H])(Cl)C([H])([H])[C@@]1([H])C1=C([H])N=C([H])C([H])=C1[H] |(-1.7195,-1.3913,-2.2903;-1.7279,-2.3123,-2.598;-1.5092,-3.1364,-1.5356;-1.4064,-4.3746,-1.7817;-1.2101,-5.3311,-0.71;-1.857,-6.1951,-0.8959;-0.1766,-5.7074,-0.7735;-1.4221,-4.8228,0.7205;-0.9708,-5.5119,1.4375;-3.2097,-4.8253,1.1586;-0.8544,-3.4183,0.8777;0.2319,-3.4869,0.725;-1.0163,-3.0317,1.888;-1.4578,-2.4532,-0.164;-2.5004,-2.2588,0.1233;-0.7262,-1.1217,-0.1859;-1.314,0.0266,0.3626;-2.3178,-0.0316,0.7843;-0.7269,1.2272,0.4182;0.5053,1.3308,-0.0915;0.9654,2.3159,-0.0378;1.1897,0.2598,-0.6717;2.1868,0.4,-1.078;0.5642,-0.9825,-0.7183;1.0659,-1.8354,-1.1699)|",5.52391116515
"[H]C1=NC(=S)C(N([H])[H])=C([H])[C@@]1([H])C(F)(F)F |(0.6826,-2.2946,0.1863;0.3238,-1.2657,0.1285;-0.9258,-1.0938,-0.0955;-1.4603,0.203,-0.1691;-3.0995,0.3685,-0.3073;-0.5572,1.3782,-0.0975;-1.1125,2.616,-0.3193;-0.6052,3.4177,0.0297;-2.1205,2.6602,-0.2195;0.7616,1.1858,0.1674;1.4346,2.0333,0.264;1.3494,-0.1776,0.2836;2.098,-0.3417,-0.5131;2.1518,-0.3658,1.5779;3.111,0.5768,1.6794;1.3726,-0.2854,2.6702;2.761,-1.5724,1.5937)|",3.2272702669300006
"[H]C1=NC(=S)[C@@]([H])(N(=O)=O)C([H])=C1C(F)(F)F |(-0.5393,2.282,0.2985;-0.3453,1.2099,0.3213;-1.3497,0.4299,0.5469;-1.1319,-0.939,0.578;-2.1,-1.9326,1.4488;0.0584,-1.4841,-0.2106;0.3018,-2.4853,0.1386;-0.4143,-1.7251,-1.6902;-0.2428,-0.815,-2.4878;-0.9423,-2.8023,-1.906;1.2257,-0.5507,-0.2099;2.206,-0.9336,-0.4744;1.0231,0.7432,0.0782;2.1304,1.761,0.094;1.8468,2.7775,-0.7468;2.2718,2.29,1.3279;3.3132,1.2332,-0.2684)|",2.88984909231
"[H]OC(=O)[C@@]1([H])C([H])([H])C(O[H])=NC([H])([H])[C@]1([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(5.6049,0.1963,-0.2992;5.6059,-0.2286,-1.1766;4.4839,-0.9685,-1.3753;4.2909,-1.4927,-2.4449;3.487,-1.0906,-0.2144;2.8565,-1.939,-0.4882;4.1415,-1.3658,1.1516;4.9443,-2.1096,1.0591;3.3891,-1.7914,1.8281;4.688,-0.0974,1.781;5.5653,-0.2698,2.8084;5.7235,-1.2176,2.9414;4.4117,1.0995,1.4531;3.4338,1.3717,0.3996;2.7731,2.1655,0.7658;3.9784,1.8122,-0.4458;2.5877,0.1813,-0.1003;2.2423,0.4228,-1.1131;1.3493,-0.1149,0.7411;0.2764,-0.7989,0.1483;0.3401,-1.0854,-0.8997;-0.8731,-1.1051,0.8756;-1.6937,-1.6284,0.3919;-0.9724,-0.7315,2.2181;-1.8676,-0.9666,2.7871;0.0846,-0.0488,2.8197;0.0177,0.2517,3.862;1.2355,0.258,2.0882;2.0442,0.7956,2.575)|",6.397396625255001
"[H]OC(=O)[C@@]1([H])C([H])([H])N=C(O[H])[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2[H])C1([H])[H] |(6.0376,2.181,0.0612;6.2426,2.8638,-0.5986;5.5251,2.6541,-1.7405;5.6596,3.402,-2.6753;4.5981,1.4432,-1.7461;4.0465,1.5148,-2.6882;5.393,0.1154,-1.7328;6.1391,0.127,-0.9221;5.9689,0.0253,-2.6611;4.5931,-1.0876,-1.5868;3.4032,-1.0172,-1.1574;2.713,-2.1889,-1.0504;1.7843,-1.9896,-0.8504;2.6334,0.2245,-0.7173;2.243,0.0079,0.2873;1.4302,0.5079,-1.6167;1.4413,0.2174,-2.9883;2.3094,-0.2622,-3.4327;0.3389,0.5228,-3.7888;0.3648,0.288,-4.8492;-0.7908,1.1219,-3.2311;-1.6487,1.3573,-3.8547;-0.8154,1.411,-1.8653;-1.6931,1.8717,-1.4201;0.2853,1.103,-1.0666;0.2581,1.3292,-0.0024;3.595,1.4306,-0.5818;3.0204,2.3612,-0.5367;4.1297,1.3342,0.375)|",6.111677082229999
"[H]OC(=O)C1=C([H])C(=O)N=C([H])[C@@]1([H])C1=C([H])C([H])=C(F)C([H])=C1[H] |(-0.243,5.84,-0.2579;-0.1048,5.6253,-1.1957;0.6354,4.4994,-1.3138;0.8065,3.9793,-2.3912;1.2594,3.951,-0.0612;1.6391,4.7233,0.9706;1.4795,5.7986,0.9991;2.3544,4.1667,2.148;2.6028,4.8621,3.1134;2.7615,2.7994,2.1233;2.4041,2.0775,1.128;2.7215,1.0308,1.1373;1.5591,2.4754,-0.0596;2.134,2.2516,-0.9694;0.2882,1.616,-0.1142;-0.5294,1.4852,1.0168;-0.26,1.984,1.9443;-1.689,0.7131,0.9749;-2.3286,0.5987,1.8436;-2.0211,0.0743,-0.2148;-3.1396,-0.6744,-0.2633;-1.234,0.1864,-1.3551;-1.5307,-0.3239,-2.2652;-0.0767,0.9618,-1.2972;0.5366,1.0755,-2.1858)|",4.22320695976
"[H]OC(=O)C1=C([H])C(=O)N=C([H])[C@@]1([H])C1=C([H])C([H])=C([H])C(F)=C1[H] |(-0.6975,-6.3258,0.093;-0.4724,-6.0435,-0.8094;-0.7572,-4.7315,-0.9744;-0.4212,-4.1438,-1.9753;-1.5361,-4.0535,0.1176;-2.4402,-4.6905,0.8802;-2.641,-5.7562,0.8005;-3.2792,-3.9702,1.873;-4.0162,-4.5779,2.6246;-3.2167,-2.5458,1.9233;-2.359,-1.9492,1.1836;-2.3225,-0.858,1.2495;-1.3537,-2.5634,0.2368;-1.522,-2.1281,-0.7583;0.0706,-2.1764,0.6601;0.4954,-2.3802,1.9801;-0.1862,-2.8063,2.7111;1.7908,-2.0305,2.363;2.1119,-2.1883,3.3885;2.6784,-1.4763,1.4402;3.69,-1.1951,1.7127;2.2379,-1.2853,0.1357;3.0832,-0.7523,-0.7675;0.9531,-1.6242,-0.2732;0.6641,-1.475,-1.3078)|",4.302119976405001
"[H]OC(=O)[C@@]1([H])C([H])([H])C(O[H])=NC([H])([H])[C@]1([H])C1=C(F)C([H])=C([H])C([H])=C1[H] |(2.9925,0.3862,-0.2881;3.6501,0.9727,-0.7016;3.6418,2.1879,-0.0897;4.4175,3.0471,-0.4271;2.5856,2.3379,1.0063;2.6841,1.4609,1.6568;2.8085,3.6029,1.8463;3.8821,3.82,1.8941;2.4623,3.4291,2.8741;2.0788,4.8227,1.3183;2.3643,5.9873,1.9703;3.0882,5.833,2.5967;1.2093,4.8755,0.3998;0.8881,3.6638,-0.3372;-0.1679,3.7119,-0.6277;1.4595,3.6801,-1.2759;1.1543,2.3252,0.3775;1.1356,1.5482,-0.3955;0.116,1.9298,1.413;0.045,0.591,1.8078;0.9326,-0.2869,1.248;-0.866,0.1048,2.7314;-0.8632,-0.9504,2.9839;-1.7662,1.0051,3.3062;-2.4916,0.6495,4.0321;-1.7297,2.3523,2.9453;-2.4281,3.0539,3.3917;-0.798,2.8083,2.0087;-0.7743,3.8595,1.7378)|",6.125282774755
"[H]OC(=O)C1=C([H])[C@]([H])(C2=C([H])C([H])=C(F)C([H])=C2[H])C(=O)N=C1[H] |(1.4751,2.9972,-4.019;1.7195,3.9024,-4.2705;1.6692,4.729,-3.1954;1.764,5.9245,-3.3374;1.5075,4.0998,-1.8451;1.8061,2.8212,-1.5459;2.1724,2.1179,-2.2935;1.6535,2.301,-0.1496;2.5978,1.8252,0.1443;0.562,1.2316,-0.039;0.7389,0.1601,0.8466;1.6527,0.0902,1.4291;-0.2495,-0.8093,1.0022;-0.1238,-1.6438,1.6839;-1.4209,-0.6944,0.2615;-2.3782,-1.6316,0.401;-1.632,0.3593,-0.6195;-2.5627,0.418,-1.1739;-0.6346,1.3237,-0.7615;-0.7986,2.1552,-1.4411;1.4305,3.4184,0.8969;1.6739,3.2084,2.0644;0.9266,4.6761,0.4814;1.0103,4.9774,-0.7675;0.6671,5.9661,-1.0732)|",3.76605569092
"[H]OC(=O)C1=C([H])[C@]([H])(C2=C([H])C(F)=C([H])C([H])=C2[H])C(=O)N=C1[H] |(-2.6935,-3.9317,-3.8408;-2.0789,-4.636,-4.1048;-1.3678,-5.0674,-3.0326;-0.6878,-6.0624,-3.1041;-1.4564,-4.2442,-1.7839;-1.6771,-2.915,-1.7653;-1.8166,-2.3401,-2.6806;-1.673,-2.1565,-0.4733;-2.6061,-1.5807,-0.416;-0.5214,-1.1518,-0.3602;-0.6923,-0.0301,0.4607;-1.6167,0.1371,1.0033;0.3559,0.8696,0.5968;0.1833,1.9498,1.3838;1.5734,0.699,-0.0542;2.3623,1.4314,0.0797;1.7356,-0.424,-0.8634;2.6787,-0.5833,-1.3779;0.6996,-1.3486,-1.0162;0.8485,-2.2212,-1.6447;-1.7173,-3.0866,0.7618;-2.1891,-2.6927,1.8055;-1.179,-4.3916,0.6564;-1.09,-4.9087,-0.5183;-0.6874,-5.9184,-0.6054)|",3.855853261585001
"[H]OC(=O)C1=C([H])[C@]([H])(C2=C([H])C([H])=C([H])C([H])=C2F)C(=O)N=C1[H] |(0.4658,3.58,-3.8385;1.1832,4.1014,-4.2357;1.9563,4.648,-3.2629;2.7703,5.5003,-3.5257;1.759,4.1262,-1.8744;1.3803,2.8663,-1.5864;1.1678,2.133,-2.3623;1.3275,2.3959,-0.1543;2.329,1.9844,0.0676;0.3164,1.3008,0.1002;0.6573,0.0919,0.714;1.6886,-0.0757,1.0149;-0.3038,-0.8925,0.95;-0.0167,-1.823,1.4302;-1.63,-0.6772,0.5728;-2.3822,-1.4392,0.7547;-1.9979,0.5213,-0.0417;-3.0195,0.7238,-0.3454;-1.0198,1.4795,-0.2601;-1.365,2.6444,-0.8639;1.1685,3.6009,0.8024;0.4869,3.5542,1.7988;1.9127,4.7671,0.4866;2.1537,5,-0.7561;2.6956,5.9106,-1.0147)|",4.114361419560002
"[H]C1=NC(=O)[C@@]([H])(C2=C([H])C([H])=C(F)C([H])=C2[H])C([H])=C1[H] |(-0.0149,2.5101,-0.5395;0.1306,1.4547,-0.2943;-0.9385,0.7383,-0.1889;-0.7755,-0.6229,0.1636;-1.7067,-1.2555,0.6161;0.5952,-1.3086,-0.0521;0.7135,-1.9616,0.8228;0.5499,-2.2201,-1.2833;1.076,-1.8242,-2.5186;1.552,-0.8533,-2.62;0.9967,-2.6613,-3.6326;1.4044,-2.367,-4.5941;0.3764,-3.8962,-3.4965;0.2986,-4.7129,-4.5675;-0.1674,-4.3155,-2.2869;-0.6489,-5.2857,-2.2221;-0.0768,-3.4707,-1.1834;-0.5136,-3.7753,-0.2371;1.7387,-0.3366,-0.0697;2.7491,-0.7395,-0.0367;1.5086,0.9836,-0.1494;2.3134,1.7123,-0.1722)|",3.989189048330001
"[H]C1=NC(=O)[C@@]([H])(C2=C(F)C([H])=C([H])C([H])=C2[H])C([H])=C1[H] |(5.0905,4.1555,0.4364;4.1962,3.5346,0.3349;3.8429,3.2309,-0.8697;2.7239,2.3733,-1.0281;2.581,1.741,-2.0502;1.6749,2.3324,0.1096;1.0385,3.2176,-0.0728;0.7809,1.1139,0.0348;1.3126,-0.1594,0.2466;2.6341,-0.2592,0.5206;0.5481,-1.3157,0.196;1.0248,-2.2752,0.3668;-0.8161,-1.2033,-0.0794;-1.4298,-2.0983,-0.1247;-1.3842,0.0523,-0.2979;-2.4443,0.1425,-0.5146;-0.587,1.1973,-0.2402;-1.0307,2.1742,-0.4166;2.3072,2.5473,1.4641;1.7362,2.2566,2.3427;3.5111,3.1313,1.561;3.9786,3.3412,2.5182)|",4.296677699395001
"[H]C1=C([H])[C@]([H])(Cl)C(=O)N=C1C(=O)OC([H])([H])C([H])([H])[H] |(6.6828,1.1293,1.3314;6.6538,0.0899,1.0281;7.6116,-0.7921,1.3526;8.4849,-0.5086,1.9333;7.5129,-2.2012,0.869;7.7944,-2.9339,1.6261;8.7287,-2.4451,-0.4942;6.1086,-2.5652,0.3649;5.682,-3.6918,0.4847;5.318,-1.5501,-0.2204;5.5666,-0.3355,0.1369;4.6935,0.7845,-0.407;4.8004,1.9267,-0.0021;3.8428,0.3663,-1.341;2.9612,1.3672,-1.9169;3.5159,2.3025,-2.0246;2.7225,0.967,-2.9043;1.7128,1.5515,-1.0686;1.029,2.2467,-1.5689;1.9656,1.9664,-0.0888;1.1941,0.598,-0.9288)|",3.78782479896
"[H]C1=NC(=O)[C@@]([H])(Cl)C([H])=C1C(=O)OC([H])([H])C([H])([H])[H] |(4.9815,-1.6486,0.333;5.8878,-1.3921,-0.2141;6.5791,-2.3652,-0.6995;7.7751,-2.0389,-1.3757;8.685,-2.832,-1.4646;7.907,-0.635,-1.9912;8.9696,-0.4033,-2.0753;7.2885,-0.6615,-3.7239;7.147,0.4095,-1.2488;7.3413,1.459,-1.4499;6.2002,0.0406,-0.3643;5.4378,1.1,0.3664;5.6503,2.2887,0.2444;4.4973,0.5661,1.169;3.6894,1.4965,1.9397;3.492,2.3764,1.3235;2.7572,0.9549,2.1141;4.3763,1.8709,3.2434;3.7168,2.5184,3.8323;5.3047,2.4152,3.0494;4.6015,0.9794,3.8372)|",3.945650832250001
"[H]C1=NC(=O)[C@@]([H])(C(=O)OC([H])([H])C([H])([H])[H])C([H])=C1Cl |(8.7644,2.1079,-1.6925;8.0703,1.6391,-0.9925;7.081,2.3418,-0.5657;6.2064,1.7436,0.3741;5.5449,2.4232,1.1241;6.1053,0.2001,0.3925;5.8006,-0.0659,1.409;4.963,-0.233,-0.5538;5.1285,-0.6187,-1.6885;3.7827,-0.0978,0.0652;2.5928,-0.369,-0.727;2.7926,-1.2284,-1.3713;1.8394,-0.6368,0.0167;2.1811,0.8532,-1.5313;1.2362,0.6515,-2.0486;2.937,1.0966,-2.2833;2.0392,1.7189,-0.877;7.3916,-0.4721,0.0089;7.5178,-1.5299,0.2177;8.3286,0.2396,-0.6288;9.8534,-0.4434,-1.1455)|",4.42457120913
"[H]C1=C([H])[C@]([H])(Cl)C(=O)N=C1C(=O)OC([H])([H])[H] |(2.2653,-2.0334,3.4711;1.8066,-1.2081,4.0018;1.2455,-1.341,5.2134;1.2057,-2.2962,5.7295;0.6035,-0.1604,5.864;0.8193,-0.0901,6.9308;-1.2257,-0.3543,5.7665;1.0052,1.1737,5.2176;1.0812,2.1834,5.881;1.305,1.1911,3.8362;1.7112,0.0846,3.3115;2.1098,0.0697,1.8456;2.5845,-0.9176,1.3187;1.8857,1.2316,1.2345;2.2476,1.2753,-0.1584;1.6841,0.5268,-0.7213;3.3172,1.0854,-0.2787;1.9933,2.2815,-0.4894)|",3.785103660455001
"[H]C1=C([H])[C@@]([H])(Br)C(C(=O)OC([H])([H])[H])=NC1=O |(1.0648,-3.2296,-6.134;1.3476,-2.789,-5.1837;1.164,-1.4901,-4.9091;0.7174,-0.8083,-5.6274;1.5451,-0.9411,-3.5865;2.1104,-0.0097,-3.6328;-0.1397,-0.3589,-2.6222;2.2507,-1.9339,-2.7012;2.9065,-1.334,-1.4705;3.2599,-0.1721,-1.4406;3.058,-2.2149,-0.4846;3.7083,-1.7107,0.6986;3.1437,-0.8706,1.1107;4.7234,-1.3823,0.462;3.7249,-2.5479,1.3952;2.4183,-3.1775,-2.9481;1.9105,-3.7166,-4.1774;1.968,-4.9154,-4.3652)|",3.945650832250001
"[H]C1=NC([H])=C(C(=O)OC([H])([H])[H])C(=O)[C@@]1([H])N(=O)=O |(-0.2344,-5.5259,0.3534;0.4254,-4.7831,0.8009;1.6149,-4.6598,0.3433;2.4203,-3.6472,0.8824;3.4557,-3.6651,0.5553;2.0057,-2.6346,1.6908;3.0171,-1.5922,2.057;4.1336,-1.5545,1.5784;2.5701,-0.7509,2.9977;3.5046,0.2612,3.415;2.9812,0.8373,4.1775;3.778,0.8969,2.5692;4.4077,-0.1982,3.8249;0.5785,-2.5784,2.0806;-0.0324,-1.6139,2.4768;-0.1008,-3.972,1.9558;0.1133,-4.4947,2.8989;-1.5909,-3.8169,1.8888;-2.0667,-3.6583,0.7669;-2.2045,-3.835,2.9457)|",4.2096012672350005
"[H]C1=NC(=S)C(=C([H])[H])C([H])=C1Br |(5.9962,0.0427,0.0495;4.9062,0.0409,0.0509;4.3141,1.1949,0.053;2.922,1.2704,0.0547;2.2285,2.7776,0.0613;2.135,-0.0022,0.0508;0.7796,-0.0341,0.0472;0.2463,-0.9806,0.0448;0.1983,0.8815,0.0466;2.871,-1.2575,0.0504;2.3131,-2.1888,0.0501;4.2201,-1.2353,0.0505;5.2705,-2.8287,0.0498)|",2.4952840090850006
"[H]C1=NC([H])=C(/C([H])=C(\C#N)C(=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(-1.237,-5.7754,4.9288;-0.5469,-5.0092,4.5801;-0.2633,-4.0325,5.4525;0.5793,-3.0855,5.0453;0.7988,-2.2994,5.7678;1.189,-3.0373,3.7713;2.0804,-1.9165,3.5231;2.1887,-1.2282,4.3602;2.817,-1.5447,2.4378;2.8158,-2.3087,1.233;2.8017,-2.9459,0.2567;3.6404,-0.2654,2.5822;3.5911,0.3203,3.6508;4.5032,0.2787,1.4263;5.5944,-0.7517,1.0457;6.2221,-0.9961,1.9106;5.1846,-1.6802,0.6422;6.243,-0.3158,0.2771;5.1861,1.5735,1.9034;5.8009,1.9806,1.093;5.8295,1.3909,2.7689;4.4504,2.329,2.1939;3.6103,0.6058,0.2045;4.2312,1.054,-0.5798;2.8341,1.3328,0.4697;3.1287,-0.2767,-0.2222;0.8701,-4.0805,2.8774;1.2969,-4.1157,1.8829;-0.008,-5.0736,3.2925;-0.2737,-5.8905,2.6286)|",4.21776468275
"[H]/N=C(/S[H])N1C2=C([H])\C([H])=C(C([H])([H])[H])/C([H])=C\2C([H])([H])C1([H])[H] |(6.8617,-1.8399,-0.9007;7.7389,-1.6319,-0.4202;7.7351,-0.4442,0.053;9.2557,0.1825,0.8064;9.9658,-0.8999,0.4364;6.695,0.4677,0.0972;5.3188,0.1603,0.0087;4.6714,-1.0689,0.1295;5.2174,-1.9842,0.3256;3.2738,-1.0915,0.0409;2.7637,-2.0469,0.1373;2.5164,0.0673,-0.1556;1.0078,0.015,-0.2315;0.6495,-1.0141,-0.3344;0.627,0.5885,-1.0851;0.5437,0.4359,0.6705;3.1964,1.2953,-0.2585;2.6313,2.2145,-0.4018;4.578,1.3396,-0.1719;5.5241,2.5169,-0.2645;5.6271,2.8539,-1.3048;5.2032,3.3796,0.3274;6.8565,1.9312,0.2582;7.0047,2.1776,1.3184;7.7256,2.2818,-0.3015)|",5.1184615279050005
"[H]OC(=O)C1=C([H])S/C(=N\C2=C([H])C([H])=C([H])C([H])=C2F)N1[H] |(7.2141,-3.3299,-1.8902;7.6638,-3.1217,-1.0551;6.8296,-2.4923,-0.1973;7.2262,-2.0814,0.8695;5.4123,-2.3288,-0.6088;4.7504,-2.7913,-1.695;5.1353,-3.3962,-2.5048;3.0612,-2.3186,-1.6897;3.2805,-1.4761,-0.0764;2.4204,-0.9056,0.6638;1.085,-0.7314,0.2908;0.6787,-0.1208,-0.9081;1.439,0.2217,-1.6041;-0.673,0.0829,-1.1909;-0.9585,0.5608,-2.1236;-1.6498,-0.3133,-0.2774;-2.7025,-0.1574,-0.4936;-1.2703,-0.9101,0.9278;-2.0007,-1.2296,1.6642;0.0761,-1.1023,1.1965;0.4362,-1.6806,2.3592;4.6134,-1.5938,0.2501;4.9735,-1.2433,1.1296)|",3.91299717019
"[H]SC1=NN([H])C(C2C([H])=C([H])C(=O)C(OC([H])([H])[H])=C2[H])N1C([H])([H])[H] |(4.0214,-3.218,0.3216;2.7558,-2.7624,0.4054;3.2376,-1.0902,0.1167;4.4362,-0.699,-0.1955;4.3118,0.6699,-0.2923;5.0626,1.1982,-0.709;3.0395,1.1225,-0.0956;2.6122,2.4555,-0.1619;1.2497,2.8465,-0.3702;0.4779,2.0913,-0.4812;0.9101,4.16,-0.5177;-0.118,4.4513,-0.7129;1.8802,5.2484,-0.4914;1.5568,6.4337,-0.6815;3.2723,4.8225,-0.238;4.2996,5.7251,-0.2122;4.1297,6.9056,0.5837;5.0726,7.4516,0.4985;3.9648,6.6389,1.6372;3.2962,7.5065,0.2205;3.5951,3.5023,-0.0965;4.6364,3.2689,0.1143;2.3371,-0.0401,0.2042;0.986,-0.1202,0.7447;0.2388,-0.1826,-0.052;0.7964,0.768,1.3507;0.9107,-1.0063,1.3791)|",3.46673045537
"[H]/N=C(/SC([H])([H])[H])N([H])/N=C([H])/C1=C([H])/C([H])=C2/OC([H])([H])O/C2=C\1[H] |(2.707,2.2765,-0.6252;2.5399,1.3705,-0.1741;2.7048,1.4584,1.0895;2.6414,-0.0782,2.0088;1.9627,0.4467,3.6259;2.6444,1.1086,4.1631;0.9928,0.9314,3.4994;1.8296,-0.4752,4.1983;2.8228,2.667,1.8106;2.4053,3.4643,1.3458;3.8012,3.0907,2.6835;4.7734,2.3168,3.0197;4.8911,1.308,2.6179;5.7938,2.7652,3.97;6.8674,1.9088,4.2552;6.9098,0.9367,3.7715;7.8908,2.2692,5.1475;8.7198,1.6053,5.3659;7.791,3.5142,5.7351;8.6381,4.0996,6.6398;8.1029,5.4045,6.8998;8.8116,6.1647,6.5479;7.9176,5.5127,7.9741;6.8694,5.5317,6.1864;6.7247,4.374,5.4591;5.7131,4.0371,4.5925;4.8836,4.702,4.3841)|",4.244976067800001
"[H]/N=C1/SC([H])=C(C2=C(O[H])C([H])=C(F)C([H])=C2[H])N1[H] |(-0.1644,6.0185,1.0816;0.7532,6.0618,0.6312;1.1213,4.9196,0.201;2.6766,4.6856,-0.6639;2.4112,2.949,-0.7547;3.1303,2.3068,-1.2353;1.2284,2.5725,-0.2171;0.6188,1.2389,-0.1397;1.3874,0.0498,-0.1594;2.7415,0.1594,-0.2846;3.1368,-0.7256,-0.2429;0.7762,-1.2006,-0.0509;1.3626,-2.1152,-0.0594;-0.6057,-1.2751,0.0692;-1.1771,-2.4907,0.1718;-1.4036,-0.1402,0.0783;-2.4809,-0.2311,0.1566;-0.7764,1.0989,-0.0289;-1.3957,1.9899,-0.0589;0.4949,3.6744,0.2681;-0.2243,3.5389,0.9662)|",4.155178497135
"[H]C1=C(N/C([H])=C(\C#N)C(=O)C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])N([H])OC1C([H])([H])[H] |(3.0558,1.5503,0.8681;3.3133,0.6617,0.3149;4.2242,0.5971,-0.8017;4.9203,1.4677,-1.4625;5.2684,2.7022,-1.0504;5.2418,3.4749,-1.8178;5.7737,3.1125,0.1699;5.9653,2.1508,1.2003;6.1024,1.3256,2.0148;6.1555,4.5599,0.2948;5.9507,5.3057,-0.6549;6.7928,5.1123,1.5915;7.0598,6.6156,1.3982;7.5122,7.0274,2.3078;6.1351,7.1622,1.1918;7.7395,6.7947,0.5602;5.823,4.9257,2.7826;6.2622,5.3767,3.6805;5.6234,3.8749,3.0061;4.8673,5.4285,2.5918;8.1357,4.3966,1.8708;8.6058,4.8425,2.7555;8.8269,4.52,1.0288;8.0151,3.328,2.0639;4.1582,-0.724,-1.2568;5.0199,-1.2103,-1.4873;3.4353,-1.4944,-0.3176;2.8966,-0.6025,0.5583;1.9746,-1.1872,1.5676;1.6475,-0.416,2.2676;2.4753,-1.9845,2.1276;1.0948,-1.6225,1.0799)|",4.198716713214999
"[H]C1=C([H])C([H])=C2C(=C1[H])/N=C(/C1=C([H])C([H])=C([H])N(C([H])([H])[H])C1=O)N2C([H])([H])[H] |(6.7907,7.4503,1.5492;6.7577,6.4434,1.1421;7.7717,6.0299,0.2529;8.5638,6.7257,-0.0099;7.7776,4.753,-0.3024;8.5555,4.4464,-0.9956;6.7328,3.9003,0.0655;5.7093,4.2989,0.9582;5.7209,5.5905,1.503;4.9364,5.9018,2.1863;4.8149,3.2654,1.1465;5.266,2.2736,0.3978;4.5888,0.9675,0.3557;4.0295,0.4785,1.5151;4.1552,1.0541,2.4268;3.2967,-0.7318,1.5306;2.8684,-1.1225,2.4457;3.1427,-1.4091,0.3552;2.5985,-2.3454,0.2832;3.6651,-0.9425,-0.8159;3.4257,-1.6935,-2.0527;2.3561,-1.7129,-2.2849;3.7931,-2.7185,-1.9447;3.9618,-1.1862,-2.8524;4.3927,0.275,-0.9143;4.7879,0.6687,-2.0133;6.4356,2.5921,-0.2753;7.2934,1.7346,-1.0781;8.3341,2.0209,-0.8998;7.0564,1.8162,-2.1409;7.1617,0.6954,-0.7755)|",3.940208555240001
"[H]C1=C(C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])ON=C1C(=O)C([H])([H])C#N |(1.6274,-2.6634,-0.3642;2.2997,-2.2404,0.3646;2.6956,-0.9449,0.5194;2.3938,0.3493,-0.1927;1.7558,1.3417,0.8081;1.5462,2.292,0.3046;2.4252,1.5402,1.6506;0.8123,0.9508,1.2051;1.417,0.0742,-1.3504;1.1905,1.0098,-1.8724;0.4717,-0.3447,-0.9875;1.8453,-0.622,-2.08;3.7108,0.9426,-0.7467;3.5053,1.893,-1.2519;4.1752,0.2646,-1.471;4.4307,1.1296,0.0559;3.5567,-0.8736,1.5742;3.7404,-2.1273,2.1223;2.9908,-2.9353,1.3961;2.9243,-4.3837,1.7095;2.138,-5.1159,1.1454;3.8937,-4.9178,2.7848;3.9537,-4.2127,3.621;3.4991,-5.8748,3.139;5.236,-5.1144,2.2342;6.2923,-5.2749,1.7811)|",5.303498946245
"[H]C1=C([H])C(S(=O)(=O)N([H])[H])=C([H])C(C2=NNN(C([H])([H])[H])N2)=C1[H] |(9.3806,0.0955,-0.1551;8.5327,-0.5829,-0.1399;8.7436,-1.9576,-0.2435;9.7418,-2.3676,-0.3518;7.6367,-2.8086,-0.2255;7.8934,-4.5808,-0.3492;6.6335,-5.183,-0.7952;9.1614,-4.7997,-1.0518;8.1448,-5.0542,1.2636;7.3848,-5.6637,1.5595;9.0421,-5.5309,1.3317;6.3377,-2.3207,-0.1221;5.4934,-3.0006,-0.1364;6.1355,-0.9378,-0.0179;4.7784,-0.3931,0.0898;4.4988,0.936,0.1833;3.1965,1.039,0.2588;2.7256,-0.2064,0.2099;1.3068,-0.5115,0.2674;1.101,-1.1379,1.1385;1.0083,-1.0388,-0.6416;0.7755,0.4365,0.3477;3.6678,-1.136,0.1057;7.2404,-0.0735,-0.0246;7.0721,0.9954,0.0542)|",5.44227701
"[H]/N=C(\N=C1C2=C(C([H])([H])[H])/C([H])=C(C([H])([H])[H])\N=C2/N(C([H])([H])[H])N/1[H])S[H] |(0.6463,1.9235,-2.0356;-0.1456,1.897,-1.3846;0.2204,2.3802,-0.2546;1.4262,2.9366,0.1095;2.5731,2.3423,0.0778;3.0777,0.9702,-0.0678;2.5322,-0.3286,-0.0239;1.0827,-0.6353,0.2256;0.9417,-1.7075,0.3881;0.4537,-0.3168,-0.6136;0.7099,-0.0984,1.1047;3.45,-1.3736,-0.1843;3.0909,-2.3978,-0.1594;4.8265,-1.1399,-0.3379;5.799,-2.2794,-0.4979;5.3051,-3.2515,-0.4172;6.5844,-2.217,0.2634;6.295,-2.2196,-1.4737;5.3578,0.0936,-0.3337;4.4801,1.0849,-0.2181;4.8701,2.4127,-0.1983;6.1208,2.8048,0.4439;6.8814,2.0906,0.1269;6.0331,2.7952,1.5382;6.4048,3.8067,0.1098;3.7193,3.1372,0.2;3.6508,4.0597,-0.2184;-0.9898,2.5338,1.0693;-2.0142,2.0328,0.3488)|",4.111640281055
"[H]OC([H])([H])C1=C(OC([H])([H])[H])C([H])=C(F)C([H])=N1 |(3.061,5.3645,-1.3972;2.4585,5.4087,-0.6287;2.2495,4.074,-0.2402;2.5695,3.922,0.8033;1.1753,3.8298,-0.2675;2.9985,3.0994,-1.1313;2.9283,1.7021,-0.9275;2.1507,1.2937,0.108;2.0492,-0.1019,0.3659;3.029,-0.5353,0.603;1.6102,-0.6335,-0.4878;1.391,-0.1976,1.2305;3.6412,0.861,-1.7797;3.6396,-0.2179,-1.685;4.3874,1.4601,-2.7933;5.088,0.6771,-3.6359;4.416,2.8356,-2.9441;4.9967,3.3012,-3.7349;3.7228,3.6274,-2.1118)|",5.493978641595
"[H]OC([H])([H])C1=C(F)C([H])=C(OC([H])([H])[H])C([H])=N1 |(5.3131,-1.4911,5.4274;5.7473,-0.8429,6.0163;5.5748,0.4051,5.393;5.0287,1.0936,6.0582;6.5526,0.8748,5.1996;4.8183,0.2787,4.0828;4.524,1.3773,3.2678;4.9287,2.6018,3.6622;3.8357,1.2282,2.0826;3.606,2.0785,1.4502;3.4369,-0.0677,1.7167;2.7622,-0.1796,0.5434;2.3349,-1.475,0.1377;1.8235,-1.3335,-0.8155;1.6384,-1.9122,0.8643;3.1874,-2.1515,-0.0023;3.7585,-1.127,2.5731;3.4763,-2.1497,2.346;4.4311,-0.9407,3.7187)|",5.444998148505
"[H]C1=C(C(=O)N([H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])SC(C([H])([H])C([H])([H])C([H])([H])[H])=C1[H] |(7.0111,-2.8836,0.8054;6.1133,-2.5157,0.3231;6.1365,-1.3972,-0.4706;7.36,-0.591,-0.7403;8.4622,-1.0127,-0.3889;7.1725,0.5993,-1.3861;6.2221,0.9237,-1.5037;8.2401,1.574,-1.7173;7.5497,2.7372,-2.4446;8.2884,3.4913,-2.7344;6.8084,3.2276,-1.8007;7.0461,2.39,-3.3547;8.9164,2.0759,-0.4274;9.7022,2.8021,-0.6669;9.3636,1.239,0.1131;8.1859,2.5656,0.2271;9.2741,0.9145,-2.649;10.0551,1.6363,-2.9152;8.7952,0.5686,-3.572;9.7387,0.0581,-2.1574;4.5347,-1.0684,-1.1047;3.8475,-2.4417,-0.2677;2.3921,-2.792,-0.3999;2.1384,-3.4839,0.4137;1.7724,-1.8978,-0.2456;2.0155,-3.4353,-1.7512;2.27,-2.7431,-2.5643;2.6345,-4.3286,-1.9032;0.5311,-3.8039,-1.8296;0.2858,-4.2507,-2.7993;-0.1052,-2.92,-1.6993;0.2586,-4.527,-1.0511;4.8231,-3.1027,0.4366;4.6133,-3.9882,1.0285)|",5.156557466975
"[H]O/C(=N\[C@]([H])(/C(=N\N([H])[H])O[H])C([H])([H])[H])OC([H])([H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.2948,0.222,0.0491;4.7026,-0.2584,0.8021;3.762,-0.7114,1.6638;2.5037,-0.5047,1.699;1.866,0.4068,0.738;0.7874,0.2682,0.8777;2.1266,-0.0091,-0.6964;1.591,-0.9629,-1.3446;0.5802,-1.6867,-0.6898;0.4438,-2.5394,-1.2234;0.8685,-1.944,0.2566;3.147,0.6929,-1.3154;3.2577,0.2857,-2.1959;2.1813,1.8837,1.0245;1.8894,2.1115,2.0537;1.6238,2.5396,0.3475;3.2462,2.1089,0.9171;4.2809,-1.5237,2.6066;5.712,-1.6565,2.7265;5.8201,-2.5564,3.3397;6.1592,-1.847,1.7479;6.3638,-0.4673,3.3957;7.5205,0.1012,2.8546;7.9258,-0.2927,1.9256;8.1509,1.1718,3.4912;9.0497,1.604,3.0596;7.6196,1.6922,4.6716;8.1057,2.5289,5.1662;6.4579,1.1356,5.2137;6.0386,1.5392,6.1315;5.836,0.0593,4.5822;4.9296,-0.3707,4.9996)|",5.63819898236
"[H]OC1=NC2=C(O1)C(C([H])([H])C([H])([H])O[H])=C([H])C([H])=C2[H] |(4.0105,-0.8221,-0.0416;3.6049,0.0615,-0.0041;2.2886,-0.127,-0.0108;1.6437,-1.247,-0.0592;0.3027,-0.8392,-0.0397;0.2522,0.5601,0.0224;1.565,1.0186,0.0394;-0.9079,1.3213,0.0638;-0.9048,2.8308,0.0769;-1.7585,3.1937,0.6604;0.0085,3.2031,0.5559;-0.9985,3.4265,-1.3422;-0.1116,3.132,-1.9257;-1.0047,4.5192,-1.2775;-2.1982,3.0709,-2.0101;-2.1972,2.1046,-2.1066;-2.0904,0.5594,0.0285;-3.0422,1.0822,0.0726;-2.0776,-0.8436,-0.0372;-3.0245,-1.3759,-0.0555;-0.8853,-1.5696,-0.0726;-0.8744,-2.6534,-0.1207)|",5.78514046163
"[H]C1=C([H])C([H])=C(OC([H])([H])C(=O)C([H])([H])N([H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])C([H])=C1[H] |(8.8277,-0.0701,6.0271;8.5355,-0.348,5.0188;8.9738,0.4031,3.9228;9.612,1.2693,4.0757;8.6023,0.0495,2.6302;8.9368,0.6175,1.7677;7.7817,-1.0669,2.4202;7.4729,-1.3295,1.1058;6.7443,-2.5074,0.8021;6.883,-2.6612,-0.2751;7.1404,-3.3881,1.3205;5.2355,-2.4119,1.0542;4.593,-3.4249,1.2591;4.597,-1.035,0.995;5.0493,-0.4768,0.1626;4.9422,-0.5147,1.9097;3.1595,-1.1392,0.858;2.8673,-2.039,1.2374;2.3237,-0.0546,1.4119;2.4504,0.0841,2.9465;1.8098,0.8902,3.3235;3.4768,0.314,3.2548;2.1479,-0.8454,3.4448;0.8711,-0.4153,1.0562;0.1756,0.3471,1.4244;0.5841,-1.3733,1.5088;0.7547,-0.5063,-0.0284;2.7034,1.2698,0.7288;2.0257,2.0716,1.0427;2.64,1.1683,-0.36;3.7226,1.5817,0.9854;7.3336,-1.8236,3.5085;6.6903,-2.6857,3.3704;7.7186,-1.4549,4.8022;7.3672,-2.0463,5.6434)|",5.0177794032200005
"[H]OC(=O)C1=C(N(=O)=O)C([H])=C2OC([H])([H])OC2=C1C([H])([H])[H] |(3.2499,3.4206,-0.6357;2.3626,3.4242,-0.2171;2.2612,2.3534,0.5783;1.4343,2.2981,1.4582;3.1243,1.1299,0.2716;4.5336,1.0802,0.2692;5.3723,2.2796,0.3706;6.4695,2.1704,0.9056;4.9661,3.3416,-0.129;5.2718,-0.1157,0.1637;6.3525,-0.1041,0.1963;4.5394,-1.264,0.0051;4.9892,-2.5427,-0.1538;3.8202,-3.3619,-0.3013;3.7919,-4.1054,0.5017;3.8319,-3.842,-1.2858;2.6715,-2.5074,-0.2003;3.1463,-1.2383,-0.0227;2.3941,-0.0851,0.1272;0.887,-0.1771,0.1287;0.4327,0.6231,-0.4619;0.4944,-0.0837,1.1452;0.5718,-1.1382,-0.2845)|",3.82047846102
"[H]OC1=NC([H])([H])[C@@]2([H])C([H])([H])N(C(=O)OC(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H])[C@]1([H])C2([H])[H] |(6.6314,-1.2314,2.3867;7.0439,-2.0338,2.0098;7.1398,-1.8286,0.6727;7.0925,-2.8151,-0.1262;7.1577,-2.5443,-1.5635;8.0691,-3.0163,-1.9589;6.3176,-3.0748,-2.0323;7.1423,-1.0544,-1.9873;7.4027,-0.9721,-3.0477;5.7943,-0.3523,-1.6866;4.9327,-1.0174,-1.8009;5.6383,0.5207,-2.3311;5.9508,0.0735,-0.2779;4.9433,0.2594,0.6224;5.121,0.2038,1.8404;3.78,0.526,-0.0006;2.5435,0.7944,0.761;2.7313,2.0373,1.6372;1.7772,2.3011,2.107;3.0561,2.8871,1.0268;3.4714,1.8598,2.4185;2.152,-0.4435,1.5727;1.1782,-0.2774,2.047;2.8881,-0.6561,2.3496;2.0651,-1.317,0.9172;1.5263,1.0651,-0.3502;0.5475,1.2917,0.0849;1.4211,0.1933,-1.0043;1.8412,1.9181,-0.9596;7.2619,-0.3662,0.2223;7.6012,0.2806,1.0331;8.108,-0.3082,-1.0505;8.2738,0.7301,-1.3577;9.0748,-0.8079,-0.939)|",6.854547894095001
"[H]C1=NC(C(=O)OC([H])([H])C([H])([H])[H])=C(C2([H])OC([H])([H])C([H])([H])O2)C([H])=C1[H] |(5.332,-2.6477,-1.0578;5.7164,-1.6306,-1.1109;4.9795,-0.6936,-0.5082;5.4079,0.5742,-0.558;4.4867,1.5981,0.0591;4.3028,2.7034,-0.4142;3.8898,1.1357,1.1667;2.9382,2.0215,1.8044;2.9327,1.698,2.848;3.3129,3.0461,1.7417;1.5592,1.895,1.1741;0.8415,2.5147,1.7242;1.2142,0.8566,1.2031;1.5777,2.233,0.1341;6.604,0.9684,-1.1916;7.0854,2.4199,-1.1731;6.3574,3.0761,-1.6533;7.2558,2.8889,0.1582;8.628,2.6743,0.4765;8.9004,3.3595,1.2824;8.8048,1.6384,0.8027;9.3136,2.9682,-0.8571;10.2439,2.4112,-1.0071;9.5101,4.0412,-0.9819;8.3504,2.533,-1.8207;7.3547,-0.0325,-1.8104;8.2751,0.2394,-2.3144;6.9082,-1.352,-1.7773;7.4722,-2.1473,-2.2559)|",5.6164298743200005
"[H]N1C(Cl)=NC(C(=O)OC([H])([H])[H])=C(Cl)/C1=N/C([H])([H])[H] |(1.129,2.3593,-0.1592;2.1339,2.259,-0.0986;2.9141,3.3743,-0.0979;2.0223,4.8789,-0.2036;4.1882,3.3852,-0.0279;4.8281,2.1532,0.0675;6.3282,2.2389,0.1883;7.0101,1.5066,0.8699;6.8085,3.2497,-0.5549;8.2322,3.4366,-0.4784;8.7554,2.5296,-0.7919;8.5292,3.6837,0.5443;8.4514,4.2637,-1.1534;4.145,0.9691,0.0716;4.9051,-0.5789,0.148;2.6773,0.951,-0.0301;1.9871,-0.1217,-0.0493;0.5434,-0.027,-0.1498;0.12,-1.034,-0.1776;0.2135,0.4927,-1.0661;0.0932,0.4939,0.7126)|",4.277629729859999
"[H]C(=O)/C1=C(\[H])C2=C(C([H])=C(Br)C([H])=C2[H])N1C([H])([H])[H] |(3.5043,3.1774,0.0235;2.7156,2.3958,0.0098;1.531,2.6975,0.0291;3.2556,1.0461,-0.0318;4.5989,0.7087,-0.0514;5.4196,1.4138,-0.0394;4.6806,-0.7078,-0.0882;3.3365,-1.1927,-0.0896;3.0496,-2.568,-0.1211;2.0363,-2.9516,-0.1218;4.1332,-3.4281,-0.1514;3.8036,-5.3204,-0.1941;5.4744,-2.9849,-0.1521;6.28,-3.7097,-0.1772;5.7468,-1.629,-0.1207;6.7753,-1.2791,-0.1216;2.4835,-0.1142,-0.0548;1.026,-0.1719,-0.0508;0.6166,0.3581,-0.9133;0.6229,0.2867,0.8549;0.7206,-1.2178,-0.0921)|",4.13068825059
"[H]OC1=NC([H])=C(C([H])=O)C([H])=C1C([H])([H])[H] |(1.5943,-2.3255,-0.0627;2.5586,-2.424,-0.0881;3.1418,-1.2017,-0.0815;4.4727,-1.2392,-0.1128;5.124,-0.078,-0.1077;6.2102,-0.1216,-0.1337;4.4853,1.169,-0.0715;5.2571,2.424,-0.0672;4.6351,3.3477,-0.0341;6.4717,2.4945,-0.0962;3.0834,1.1763,-0.0396;2.5493,2.1251,-0.0112;2.3693,-0.014,-0.044;0.8614,-0.0603,-0.0101;0.442,0.9496,0.0062;0.4846,-0.5792,0.8827;0.444,-0.5671,-0.8918)|",5.069481034815
"[H]C1=C2N=C(C([H])([H])C([H])([H])[H])N(C([H])([H])[H])C2=C([H])C([H])=C1C(=O)OC([H])([H])[H] |(2.7937,4.7378,1.7899;3.2017,4.0077,2.4785;3.2273,2.6517,2.1493;2.7863,2.0058,1.004;3.0417,0.734,1.2023;2.7626,-0.3389,0.1925;2.3485,-1.2303,0.681;1.9831,0.0529,-0.4665;3.9974,-0.7233,-0.6449;3.7303,-1.4739,-1.3969;4.7995,-1.1401,-0.0253;4.395,0.1547,-1.1632;3.6402,0.4873,2.4358;4.0487,-0.7815,3.0096;5.1228,-0.7774,3.2269;3.8401,-1.5882,2.3068;3.5032,-0.9779,3.9397;3.7645,1.7126,3.0618;4.2811,2.0844,4.3069;4.6913,1.3587,5.0031;4.2488,3.4368,4.6188;4.6349,3.7941,5.5672;3.7181,4.3926,3.7206;3.7354,5.8129,4.1597;4.169,6.2039,5.2277;3.2089,6.6522,3.234;3.1979,8.0372,3.6033;2.7452,8.5616,2.7608;4.2151,8.3982,3.7786;2.6088,8.1899,4.5119)|",5.276287561195
"[H]O/C(=N/C1=C(N([H])C([H])([H])[H])C([H])=C([H])C(C(=O)OC([H])([H])[H])=C1[H])C([H])([H])C([H])([H])[H] |(4.4291,-2.4193,-1.5395;3.8004,-1.7492,-1.2314;2.5296,-2.1754,-1.48;1.6071,-1.3674,-1.1389;0.2337,-1.6329,-1.2471;-0.5681,-0.5538,-1.7384;0.0773,0.5815,-2.161;1.0398,0.6245,-1.8443;-0.6102,1.8339,-2.3943;0.1319,2.6017,-2.6266;-1.2005,2.1697,-1.5269;-1.2874,1.7538,-3.2535;-1.9648,-0.7121,-1.7896;-2.5833,0.0955,-2.167;-2.5647,-1.8902,-1.3576;-3.6429,-1.995,-1.404;-1.7888,-2.9389,-0.8484;-2.366,-4.2037,-0.3503;-1.7223,-5.1425,0.087;-3.7238,-4.2274,-0.4291;-4.3385,-5.4329,0.0391;-5.4114,-5.2863,-0.0946;-4.1046,-5.6039,1.0937;-3.9953,-6.2948,-0.5405;-0.3914,-2.7882,-0.7928;0.1923,-3.5895,-0.3527;2.4036,-3.5338,-2.1478;2.5727,-4.318,-1.3965;1.371,-3.6504,-2.4844;3.3616,-3.742,-3.3346;3.1425,-4.6967,-3.8225;4.4177,-3.7819,-3.036;3.2526,-2.9478,-4.0803)|",4.525253333815
"[H]C1=NC(Cl)=C(C([H])([H])[H])C([H])=C1C([H])(F)F |(6.2322,-0.1171,-0.2101;5.1517,-0.0401,-0.1283;4.4698,-1.1905,-0.1784;3.1569,-1.1336,-0.0672;2.3131,-2.683,-0.1447;2.3988,0.04,0.1064;0.9002,0.0357,0.2419;0.5207,1.0542,0.3638;0.5823,-0.5573,1.1068;0.4234,-0.4087,-0.6388;3.139,1.2222,0.1506;2.626,2.1689,0.2913;4.5305,1.1973,0.0317;5.3322,2.472,0.0325;5.4786,2.896,-0.9681;4.7012,3.4234,0.7887;6.5726,2.2509,0.5656)|",5.978341295485
"[H]/N=C(/O[H])C1=C([H])C(Cl)=NC([H])=C1N(=O)=O |(-2.7313,0.2721,-2.3194;-3.4264,-0.1939,-1.7351;-2.9128,-0.6121,-0.6512;-3.704,-1.1757,0.2797;-3.1724,-1.8183,0.7886;-1.4595,-0.3753,-0.284;-1.0226,0.9529,-0.3274;-1.7238,1.7471,-0.5541;0.3191,1.2445,-0.0709;0.8355,2.9123,-0.1094;1.2484,0.3383,0.1974;0.8557,-0.9336,0.2288;1.6129,-1.6821,0.4387;-0.4683,-1.33,0.0186;-0.7377,-2.7645,0.1399;0.1806,-3.5373,-0.1011;-1.8649,-3.1202,0.5088)|",4.34293705398
"[H]C1=C2/N=C(/C([H])([H])[H])C(=O)N(C([H])([H])[H])C2=C([H])C([H])=C1C(=O)OC([H])([H])[H] |(4.5217,3.0285,-0.0693;5.0688,2.0937,-0.0952;4.336,0.9033,0.0065;2.9585,0.9898,0.135;2.269,-0.101,0.2313;0.7773,-0.0428,0.3727;0.2909,-0.5881,-0.4443;0.4487,0.998,0.3723;0.4588,-0.5337,1.2997;2.8681,-1.4634,0.2108;2.1896,-2.4817,0.3013;4.2603,-1.5021,0.08;4.9022,-2.8166,0.0515;5.6061,-2.9152,0.8839;5.4373,-2.9588,-0.8927;4.1141,-3.5613,0.1448;5.0153,-0.3419,-0.0225;6.4176,-0.3613,-0.1526;6.9547,-1.3018,-0.1764;7.1194,0.8287,-0.2509;8.1996,0.824,-0.3512;6.455,2.0689,-0.2235;7.2776,3.3031,-0.3343;8.4886,3.3126,-0.4472;6.5291,4.4287,-0.2959;7.2646,5.6575,-0.3976;6.5163,6.4494,-0.3541;7.8148,5.6989,-1.3413;7.9726,5.7509,0.4303)|",4.443619178664999
"[H]OC(=O)[C@]([H])(/N=C(/O[H])C([H])([H])[H])C([H])([H])C1=C([H])C([H])=C([H])C(O[H])=C1[H] |(1.7868,-1.1069,2.5869;1.267,-0.924,3.408;1.1917,0.4115,3.4927;0.6146,0.9873,4.3856;1.9385,1.1699,2.3706;1.2399,1.9328,2.0022;2.3482,0.2097,1.3671;2.344,0.4405,0.1179;2.7374,-0.5956,-0.6738;2.7254,-0.3157,-1.602;1.9552,1.6943,-0.6292;1.5942,2.4795,0.0333;1.1729,1.4643,-1.3638;2.8244,2.0905,-1.1671;3.1584,1.8922,3.0141;2.7834,2.4098,3.9029;3.8665,1.125,3.3469;3.8387,2.8697,2.0843;3.3596,4.1851,1.9718;2.5211,4.5064,2.5846;3.9639,5.0796,1.0913;3.5944,6.0987,1.0133;5.0509,4.6837,0.3099;5.5253,5.3867,-0.3725;5.5351,3.3766,0.4229;6.5989,2.9181,-0.3064;6.9555,3.6477,-0.8367;4.9292,2.4741,1.3045;5.3293,1.4672,1.3767)|",5.845005508740001
"[H]C1=C([H])C(N(=O)=O)=C(N(=O)=O)C([H])=C1C(=O)OC([H])([H])C([H])([H])[H] |(4.4988,1.8887,-0.0742;5.5116,1.5042,-0.0732;6.5938,2.3826,-0.0893;6.4498,3.4567,-0.0968;7.8904,1.8808,-0.0808;8.9916,2.8562,0.0311;8.8797,3.8915,-0.6183;9.8989,2.5816,0.8074;8.1108,0.4992,-0.0719;9.4609,-0.0821,-0.2017;9.6848,-1.1073,0.433;10.2313,0.4751,-0.9755;7.037,-0.3776,-0.0448;7.2075,-1.4477,-0.0377;5.7299,0.1228,-0.0443;4.61,-0.8732,-0.0232;4.7871,-2.0741,-0.0189;3.4057,-0.2746,-0.0142;2.2432,-1.1469,0.0146;1.4505,-0.5447,-0.4345;2.4459,-2.0141,-0.6178;1.8997,-1.5618,1.4364;0.9753,-2.1503,1.4334;1.7461,-0.6846,2.0731;2.6958,-2.1773,1.8641)|",4.781040353285
"[H]OC([H])([H])C1=C(C(F)(F)F)C([H])=C([H])N=C1Cl |(2.4204,-2.1692,1.8596;2.9937,-1.5307,1.4065;2.4974,-1.4047,0.0812;3.2378,-0.8189,-0.4638;2.4141,-2.3776,-0.414;1.157,-0.6845,0.0448;-0.0736,-1.3618,0.102;-0.1247,-2.8741,0.156;-1.3758,-3.3289,0.3451;0.3406,-3.4399,-0.9781;0.6294,-3.3651,1.1789;-1.2754,-0.6523,0.1088;-2.2271,-1.1668,0.1522;-1.2175,0.736,0.062;-2.126,1.3336,0.0605;-0.0669,1.4143,0.0209;1.0583,0.7214,0.0211;2.5242,1.6966,-0.0142)|",5.545680273190001
"[H]OC(OC([H])([H])C([H])([H])[H])C1C(=O)N=C(O[H])C(C#N)=C1[H] |(1.7737,-0.9367,1.643;2.3981,-0.464,1.0625;3.6011,-0.4835,1.6506;3.5179,-0.9702,2.8671;4.6109,-1.628,3.6059;5.41,-0.8982,3.7247;4.1345,-1.8376,4.5647;5.0607,-2.8999,2.9107;5.8143,-3.3898,3.5369;5.5213,-2.6905,1.9409;4.2292,-3.5985,2.7678;4.6931,-0.0075,0.9138;5.9586,0.4892,1.529;6.0748,0.6548,2.7438;7.0084,0.8095,0.6791;6.8315,0.8633,-0.6006;7.9035,1.1895,-1.3449;7.6632,1.208,-2.2888;5.565,0.5777,-1.2697;5.4758,0.7026,-2.6835;5.4925,0.8304,-3.8429;4.5247,0.1382,-0.4893;3.5805,-0.1271,-0.9517)|",3.975583355805
"[H]C1=C(Cl)N=C(OC([H])([H])[H])C([H])=C1C([H])(F)F |(4.6642,3.3945,3.1347;4.1601,2.7516,2.4257;4.3413,1.375,2.4355;5.4092,0.669,3.646;3.7721,0.5251,1.5923;2.9661,1.0333,0.6616;2.3722,0.2097,-0.2199;2.6504,-1.1919,-0.1042;2.0776,-1.6618,-0.9048;2.3324,-1.5742,0.8699;3.7186,-1.3895,-0.2302;2.6931,2.4069,0.5383;2.035,2.7639,-0.2437;3.3057,3.2644,1.4393;3.0104,4.7453,1.4015;2.1694,5.023,2.0479;4.1012,5.4571,1.8169;2.7007,5.1388,0.1303)|",5.423229040464999
"[H]C1=NC(Cl)=C(C([H])(F)F)C(Cl)=N1 |(-1.1934,2.0576,-0.0009;-0.6441,1.1199,-0.0016;-1.3639,0.0037,0.1146;-0.6774,-1.1282,0.1131;-1.6451,-2.5849,0.2714;0.7178,-1.2046,-0.0032;1.4782,-2.5123,-0.0026;0.8174,-3.3738,0.0985;2.1781,-2.6453,-1.1666;2.3691,-2.5333,1.0308;1.3408,0.0531,-0.1188;3.0712,0.1773,-0.2749;0.6823,1.2023,-0.1186)|",6.008273819039999
"[H]C1=C([H])C(Cl)=C(C(=O)N(OC([H])([H])[H])C([H])([H])[H])C(Cl)=N1 |(8.3704,0.5975,-2.9105;7.5541,0.3286,-2.2443;7.5313,0.7928,-0.9343;8.3199,1.4259,-0.545;6.4557,0.4105,-0.1321;6.3889,0.976,1.5232;5.442,-0.4025,-0.6426;4.327,-0.9457,0.226;4.5043,-1.9145,0.9462;3.1426,-0.2491,0.1918;2.9665,0.631,-0.8897;2.9249,1.9877,-0.4316;2.1053,2.1433,0.28;2.7458,2.5815,-1.3309;3.8737,2.2803,0.0303;1.9042,-0.8362,0.6766;2.1654,-1.5518,1.4563;1.3776,-1.3481,-0.1373;1.2619,-0.0555,1.0934;5.6033,-0.8041,-1.9805;4.3882,-1.8555,-2.6927;6.605,-0.4602,-2.7658)|",5.621872151330001
"[H]O[C@@]([H])(C1=C([H])N(C([H])([H])C2=C([H])C([H])=C([H])C([H])=C2[H])N=N1)C([H])([H])[H] |(2.8057,-0.5692,1.5633;2.1143,-0.9172,0.9743;2.3315,-0.3022,-0.2936;1.3995,-0.4412,-0.8534;2.5847,1.1734,-0.1154;2.0683,2.2883,-0.7375;1.3343,2.4203,-1.5178;2.6969,3.3344,-0.1388;2.5059,4.7698,-0.3369;3.327,5.2379,0.2142;2.6434,4.9988,-1.3988;1.1583,5.2683,0.1491;0.3561,6.0621,-0.6761;0.69,6.3038,-1.6829;-0.8716,6.5458,-0.2175;-1.4874,7.1598,-0.8691;-1.3086,6.2311,1.0689;-2.2658,6.6017,1.4255;-0.5117,5.4364,1.8977;-0.8471,5.1857,2.9003;0.717,4.9617,1.4429;1.3367,4.3441,2.088;3.5445,2.8969,0.8187;3.4806,1.5933,0.8262;3.4793,-0.989,-1.0466;4.4251,-0.8335,-0.5156;3.5878,-0.5869,-2.0611;3.2849,-2.0642,-1.1107)|",6.408281179275001
"[H]O/C(=N/C([H])([H])[C@]([H])(O[H])C([H])([H])O[H])N([H])C1=NC(C([H])([H])[H])=C([H])C(C([H])([H])[H])=N1 |(0.2807,-2.5039,0.4051;1.163,-2.1,0.3258;1.1747,-1.0695,1.217;0.1215,-0.7699,1.8582;0.1177,0.1651,2.9733;0.9477,-0.0543,3.6617;0.2523,1.1936,2.6197;-1.2174,0.0271,3.7156;-1.1431,0.5053,4.6991;-1.5101,-1.3506,3.9738;-1.2605,-1.8038,3.1458;-2.3989,0.6448,2.9581;-2.4029,0.2648,1.9223;-2.299,1.7361,2.9138;-3.6154,0.3547,3.6232;-3.5148,-0.572,3.9075;2.4743,-0.5661,1.3014;3.2004,-1.2227,1.0401;2.8992,0.7564,1.2277;4.2349,0.8897,1.2147;4.7033,2.1403,1.1225;6.2001,2.2992,1.0987;6.6411,1.8519,1.9962;6.4958,3.3504,1.0445;6.6217,1.769,0.2373;3.8355,3.2359,1.0498;4.2166,4.2485,0.9753;2.4637,2.9814,1.0659;1.4409,4.0824,0.9863;0.8504,4.1177,1.9095;0.741,3.8883,0.1667;1.9075,5.0594,0.8339;1.9863,1.7277,1.1501)|",5.16472088249
"[H]C1=C([H])C([H])=C(C2=C([H])C(C([H])([H])[H])=C(N([H])[H])C(Cl)=C2[H])O1 |(4.8295,5.7587,0.3169;5.308,4.801,0.1809;6.5986,4.4053,0.0097;7.4699,5.0445,-0.0252;6.5653,2.9809,-0.1152;7.4122,2.3268,-0.2665;5.2504,2.6027,-0.0108;4.5623,1.3192,-0.0544;3.1634,1.2343,0.0498;2.59,2.1484,0.1645;2.4867,0.0196,0.0058;0.9814,-0.0317,0.1029;0.5608,0.9753,0.1711;0.6427,-0.5849,0.9918;0.5386,-0.5246,-0.7726;3.2154,-1.1859,-0.1447;2.556,-2.4038,-0.2517;1.6667,-2.4621,0.226;3.1337,-3.2213,-0.1024;4.6154,-1.0883,-0.2452;5.5666,-2.5693,-0.4208;5.2835,0.1262,-0.2039;6.3649,0.1348,-0.2833;4.4753,3.7208,0.1716)|",4.498041948765
"[H]C1=NC(C(=O)OC([H])([H])C([H])([H])[H])=C(Cl)C(C#N)=C1[H] |(5.6355,-3.1526,0.0573;6.1918,-2.222,-0.0256;5.4761,-1.0978,0.0495;6.1053,0.0802,-0.0324;5.2322,1.3015,0.158;5.5254,2.2232,0.8871;4.1038,1.1982,-0.5557;3.1302,2.2634,-0.3879;3.6612,3.2142,-0.299;2.5632,2.2485,-1.3212;2.2397,2.0037,0.8161;1.4605,2.7726,0.8701;2.8199,2.0386,1.7424;1.755,1.0256,0.7369;7.4957,0.179,-0.2171;8.3037,1.7073,-0.3778;8.2424,-1.0102,-0.3103;9.6615,-0.9963,-0.5129;10.8113,-1.0358,-0.6777;7.5714,-2.2356,-0.2034;8.1245,-3.1663,-0.2658)|",5.058596480795
"[H]C1=C([H])C(C([H])([H])N2N=NC(C(=O)C([H])([H])[H])=C2[H])=C([H])C([H])=C1Cl |(0.486,6.5075,-1.9161;1.5402,6.255,-1.8798;1.9803,4.9845,-2.2419;1.2584,4.2362,-2.5584;3.342,4.658,-2.1948;3.8143,3.2785,-2.6099;4.8991,3.1908,-2.5009;3.5616,3.0688,-3.6531;3.1884,2.2,-1.8439;2.153,1.4991,-2.393;1.7487,0.643,-1.5042;2.5071,0.7746,-0.3726;2.336,-0.045,0.8478;3.0677,0.1374,1.8098;1.2385,-1.0863,0.8352;1.3824,-1.7854,0.0037;0.264,-0.6107,0.6749;1.2411,-1.6238,1.7851;3.4332,1.7808,-0.5868;4.202,2.1956,0.046;4.2578,5.6279,-1.7787;5.3181,5.3902,-1.7357;3.8333,6.9077,-1.4173;4.5466,7.659,-1.0959;2.4746,7.2088,-1.4714;1.9261,8.8142,-1.0163)|",5.600103043290002
"[H]C1=C([H])C(C([H])([H])N2N=NC(C(=O)C([H])([H])[H])=C2[H])=C([H])C([H])=C1F |(-1.6241,4.793,-1.307;-0.8571,4.9017,-0.5473;0.4667,4.5454,-0.7884;0.7467,4.1408,-1.7575;1.4419,4.6933,0.2084;2.8805,4.3086,-0.0694;3.5189,4.5506,0.7856;3.2748,4.8327,-0.9442;3.0479,2.8869,-0.3816;3.3015,2.5079,-1.6684;3.369,1.2112,-1.6857;3.159,0.7314,-0.4212;3.1722,-0.7034,-0.06;2.9674,-1.0425,1.0964;3.4502,-1.6943,-1.1692;4.4392,-1.5088,-1.6041;2.7267,-1.5748,-1.9835;3.4028,-2.7076,-0.7664;2.9472,1.8093,0.4211;2.7408,1.8636,1.4784;1.0681,5.2069,1.454;1.8144,5.3265,2.2359;-0.2539,5.5738,1.7115;-0.5584,5.9742,2.6726;-1.1949,5.4128,0.703;-2.4745,5.7618,0.9399)|",5.60010304329
"[H]O[C@]([H])(C1=C([H])N(C([H])([H])C2=C([H])C([H])=C(OC([H])([H])[H])C([H])=C2[H])N=N1)C([H])([H])[H] |(1.4197,1.2154,-0.8365;2.1463,1.6539,-1.313;3.0606,2.0969,-0.3161;4.0387,2.1642,-0.808;3.1377,1.0852,0.8007;4.1754,0.6454,1.5932;5.2254,0.8915,1.6348;3.6049,-0.2646,2.426;4.209,-1.0358,3.515;3.4602,-1.7923,3.7657;5.0909,-1.5526,3.123;4.5773,-0.1909,4.7162;3.597,0.5213,5.4136;2.5636,0.4776,5.0781;3.9196,1.2957,6.5279;3.1347,1.8359,7.0445;5.2499,1.3604,6.9662;5.6772,2.0789,8.0425;4.7203,2.8282,8.7766;5.277,3.3223,9.5747;3.9525,2.1795,9.2182;4.235,3.5871,8.1491;6.2409,0.649,6.2757;7.2657,0.7104,6.6277;5.9026,-0.1129,5.1633;6.6828,-0.6598,4.6381;2.2849,-0.3839,2.1723;2.0049,0.4326,1.1914;2.669,3.4877,0.2054;3.4115,3.8713,0.9155;1.7,3.4399,0.7146;2.5887,4.185,-0.6345)|",5.804188431165
"[H]O[C@]([H])(C1=C([H])N(C([H])([H])C2=C([H])C([H])=C(F)C([H])=C2[H])N=N1)C([H])([H])[H] |(3.9541,-1.4224,-0.5007;3.0381,-1.7087,-0.342;2.3739,-0.5975,0.2537;1.3029,-0.7686,0.0927;2.7766,0.6794,-0.4398;2.054,1.7651,-0.8832;1.0026,2.0077,-0.8596;2.9846,2.5943,-1.4261;2.8131,3.9139,-2.0316;3.7742,4.1303,-2.5065;2.0576,3.8397,-2.82;2.4285,4.9897,-1.0355;1.2513,5.7244,-1.2108;0.6035,5.5115,-2.058;0.8934,6.735,-0.3168;-0.0158,7.3135,-0.4415;1.7329,6.9929,0.7587;1.4008,7.9642,1.6327;2.9098,6.2785,0.966;3.5337,6.517,1.821;3.2533,5.2772,0.0616;4.1681,4.7093,0.2103;4.2201,2.0568,-1.3254;4.0922,0.9028,-0.729;2.6514,-0.5375,1.7622;2.0726,0.2601,2.2433;3.7147,-0.3451,1.9437;2.3859,-1.4947,2.2218)|",6.23140717645
"[H]O[C@]([H])(C1=C([H])N(C([H])([H])C2=C([H])C([H])=C(Cl)C([H])=C2[H])N=N1)C([H])([H])[H] |(0.4522,-1.6259,0.9907;-0.0218,-1.6005,0.1415;0.9308,-1.1848,-0.8322;0.5299,-1.5089,-1.7998;2.2518,-1.871,-0.5905;3.1665,-2.4557,-1.4384;3.1976,-2.5854,-2.5095;4.1541,-2.9024,-0.6177;5.4116,-3.5666,-0.9526;5.7604,-4.0027,-0.0121;5.1974,-4.3866,-1.6449;6.4473,-2.6277,-1.5408;6.828,-1.4698,-0.8498;6.3609,-1.2348,0.103;7.7929,-0.6131,-1.3732;8.0868,0.2836,-0.8383;8.3853,-0.9221,-2.5992;9.6048,0.1542,-3.2643;8.0245,-2.0678,-3.3039;8.4917,-2.2928,-4.2566;7.0516,-2.9135,-2.7677;6.7659,-3.8073,-3.3176;3.8732,-2.6098,0.6712;2.7259,-1.9875,0.6846;1.0787,0.3437,-0.8391;1.7445,0.6766,-1.6444;1.4955,0.6875,0.1142;0.0966,0.8062,-0.9785)|",6.0953502512
"[H]C(=O)C1=C(C([H])([H])[H])C([H])=C(OC(=O)C([H])([H])[H])C([H])=C1C([H])([H])[H] |(1.0155,-2.7895,-0.1884;2.1207,-2.8656,-0.1967;2.6354,-3.9726,-0.2113;2.832,-1.5704,-0.189;4.2517,-1.5085,-0.1952;5.1251,-2.7404,-0.213;6.1808,-2.4522,-0.2172;4.9231,-3.3651,-1.0881;4.9368,-3.3784,0.6556;4.8819,-0.2609,-0.1867;5.9623,-0.2047,-0.1835;4.1199,0.9039,-0.1683;4.6656,2.1811,-0.2342;5.8017,2.5296,0.4588;6.4111,1.7895,1.1878;6.1387,3.9709,0.1682;7.0586,4.2383,0.6888;5.3218,4.6207,0.4991;6.2589,4.1209,-0.9095;2.728,0.8526,-0.1675;2.1706,1.784,-0.1642;2.0672,-0.3734,-0.1775;0.5502,-0.3614,-0.1727;0.1841,0.6692,-0.1663;0.1347,-0.8626,0.7088;0.128,-0.8541,-1.0558)|",4.968798910129999
"[H]C([H])=C(C1=C([H])C([H])=C([H])C([H])=C1[H])C1=C([H])C(N([H])[H])=C([H])C([H])=C1[H] |(0.1102,0.8057,-0.3156;0.8107,0.4341,0.4262;0.4243,0.2714,1.4276;2.0986,0.1898,0.1208;2.6269,0.4921,-1.2403;2.2105,1.6384,-1.9383;1.5408,2.3405,-1.4499;2.6647,1.8964,-3.231;2.3331,2.7926,-3.749;3.5519,1.0157,-3.8525;3.9102,1.218,-4.8583;3.9847,-0.1213,-3.167;4.6766,-0.8127,-3.6412;3.5324,-0.377,-1.8734;3.8731,-1.2642,-1.3481;3.0251,-0.3894,1.1365;2.5788,-1.3897,2.0107;1.5689,-1.7776,1.9013;3.4132,-1.9173,3.0078;2.9304,-2.8772,3.9048;3.6409,-3.477,4.3061;2.1458,-3.4184,3.5624;4.7262,-1.4303,3.1172;5.3865,-1.8304,3.8836;5.1784,-0.4443,2.2436;6.1969,-0.0763,2.3385;4.3461,0.0758,1.2537;4.709,0.8478,0.5834)|",4.658589120559999
"[H]OC([H])([H])C1=C(OC([H])([H])[H])C([H])=C([H])C(OC([H])([H])[H])=C1N(=O)=O |(0.3505,-4.1238,1.1071;0.085,-3.1919,1.1973;0.4038,-2.5907,-0.0466;-0.0944,-3.1025,-0.882;0.0223,-1.5711,-0.0031;1.9066,-2.5427,-0.2913;2.6464,-1.3905,0.0542;1.9213,-0.3432,0.5438;2.6083,0.8355,0.9311;3.3328,0.6347,1.7313;1.8415,1.5171,1.3025;3.1257,1.2998,0.0808;4.0298,-1.3642,-0.1266;4.6037,-0.483,0.1342;4.7079,-2.4732,-0.6394;5.7843,-2.4251,-0.7507;4.0131,-3.6251,-1.0035;4.5806,-4.7636,-1.4759;5.9898,-4.7904,-1.6528;6.2167,-5.7822,-2.0459;6.5175,-4.6448,-0.7013;6.3173,-4.0297,-2.3729;2.6146,-3.625,-0.8166;1.8721,-4.836,-1.2058;1.1302,-5.3534,-0.3598;2.0243,-5.2622,-2.3421)|",3.812315045505
"[H]C1=C([H])C([H])=C(C(F)(F)F)C(ON([H])[H])=N1 |(-1.9638,1.3281,-0.2269;-1.1036,0.6726,-0.109;-1.263,-0.7,0.0135;-2.2497,-1.1493,-0.0052;-0.1121,-1.4825,0.1625;-0.1818,-2.5597,0.2648;1.1367,-0.8782,0.1809;2.4,-1.6814,0.3435;2.1204,-2.9992,0.4808;3.2178,-1.5533,-0.7193;3.0934,-1.3034,1.4371;1.1766,0.5298,0.0479;2.3999,1.1098,0.0407;2.4305,2.5517,-0.0765;2.4191,2.8589,0.9008;1.5002,2.7915,-0.4362;0.0937,1.2809,-0.092)|",5.387854239900001
"[H]SC1=NC2=C([H])C([H])=C(C(F)(F)F)C([H])=C2O1 |(4.2894,1.2199,-0.0003;4.0467,-0.1058,-0.0006;2.3051,0.1066,0.0002;1.618,1.2034,0.001;0.2926,0.7595,0.0014;-0.9145,1.4583,0.0022;-0.9317,2.5429,0.0026;-2.0922,0.7102,0.0024;-3.0504,1.2166,0.003;-2.0691,-0.6945,0.0019;-3.3505,-1.4836,0.0024;-4.4443,-0.694,0.0017;-3.4344,-2.292,-1.0808;-3.4346,-2.2904,1.0868;-0.8641,-1.4115,0.0011;-0.8398,-2.4953,0.0006;0.282,-0.6436,0.0009;1.5944,-1.0659,0.0001)|",5.333431469800001
"[H]C(=O)C1=C(Cl)C([H])=C(OC(=O)C([H])([H])[H])C([H])=C1Cl |(9.9398,1.6762,2.1373;9.6657,0.8473,1.4623;10.527,0.1653,0.9462;8.1948,0.6813,1.2756;7.2883,1.536,1.9391;7.8643,2.8173,3.0049;5.9045,1.4473,1.8173;5.2599,2.1271,2.3507;5.3851,0.4516,0.9879;4.0466,0.2154,0.7414;2.9965,0.9073,1.3042;3.1107,1.8128,2.0902;1.7013,0.3466,0.7761;0.8677,0.8876,1.2242;1.6273,-0.7196,1.0134;1.6668,0.4426,-0.3141;6.2333,-0.4229,0.3057;5.8085,-1.187,-0.3337;7.6092,-0.3074,0.449;8.5679,-1.4549,-0.4543)|",4.718454167669999
"[H]/N=C1\C2=C([H])C([H])=C(OC([H])([H])[H])C(F)=C2N([H])N1[H] |(4.1298,5.2021,-3.5524;4.8117,4.6582,-4.0843;4.8802,3.4662,-3.6217;4.1825,2.7416,-2.5482;3.2904,3.1386,-1.5486;2.9861,4.177,-1.4522;2.8073,2.1851,-0.6657;2.1153,2.4473,0.1273;3.1965,0.8266,-0.7487;2.6499,0.0203,0.1957;2.8646,-1.3931,0.1861;2.2678,-1.7728,1.0172;3.9176,-1.6416,0.3459;2.5201,-1.844,-0.7496;4.0863,0.4439,-1.76;4.5304,-0.8342,-1.9037;4.5507,1.3999,-2.6585;5.3949,1.1933,-3.7569;6.234,0.6632,-3.5254;5.7621,2.5149,-4.194;5.8081,2.5665,-5.2071)|",4.756550106740001
"[H]OC([H])([H])C1=NN2C(=C1C(=O)OC([H])([H])[H])C([H])([H])C([H])([H])C2([H])[H] |(2.5523,4.2855,4.1898;2.8159,4.5744,3.3;3.0174,3.4016,2.5344;2.7,3.598,1.5044;4.087,3.1381,2.477;2.2593,2.2243,3.0874;1.6929,2.3276,4.2895;1.1571,1.1042,4.5072;1.3381,0.2377,3.4953;2.0707,0.9221,2.5272;2.5597,0.4555,1.2351;3.2193,1.1184,0.4548;2.1899,-0.8316,0.9888;2.6361,-1.3604,-0.2685;3.7283,-1.3645,-0.3208;2.2457,-0.7654,-1.0986;2.2479,-2.3788,-0.312;0.6418,-1.0537,3.7938;-0.2542,-1.1596,3.169;1.2677,-1.9286,3.5989;0.2834,-0.8951,5.3062;1.0408,-1.3981,5.9153;-0.6858,-1.3344,5.5541;0.3184,0.629,5.5989;0.7645,0.8774,6.5655;-0.6715,1.0982,5.5433)|",6.027321788575
"[H]OC([H])([H])C1=NN2C(=C1C(=O)OC([H])([H])[H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H] |(3.3123,-6.0232,0.8991;4.135,-5.6066,0.5917;3.8311,-4.2492,0.3313;4.6706,-3.6278,0.6636;3.7285,-4.0639,-0.7506;2.5674,-3.8083,1.0201;1.8124,-4.7173,1.6219;0.7403,-4.0313,2.1041;0.7988,-2.7055,1.8392;1.9818,-2.5141,1.1157;2.5487,-1.295,0.5486;3.5962,-1.2416,-0.0716;1.7752,-0.1984,0.7811;2.2899,1.0302,0.2465;3.2667,1.2614,0.68;1.5604,1.7949,0.5167;2.3935,0.9653,-0.8399;-0.2951,-1.7773,2.2845;0.1458,-0.8403,2.635;-0.9068,-1.5095,1.4113;-1.1808,-2.4198,3.3666;-0.6537,-2.4089,4.3305;-2.0909,-1.8245,3.4975;-1.5331,-3.8679,2.9992;-2.0654,-3.8876,2.0389;-2.2009,-4.3115,3.7459;-0.2675,-4.7226,2.9019;-0.4586,-5.6886,2.4268;0.1478,-4.9178,3.8992)|",5.972899018475
"[H]OC(=O)C1=C(C([H])([H])C([H])([H])[H])N=C(N2C([H])=C([H])C([H])=C2[H])S1 |(4.4422,-3.7357,-2.8617;3.8441,-3.9309,-2.1212;3.2944,-2.7912,-1.6119;2.6958,-2.8385,-0.5601;3.4626,-1.5554,-2.3974;3.2823,-0.2578,-1.9599;2.8604,0.174,-0.5829;3.1995,-0.5584,0.1524;3.3485,1.1327,-0.3753;1.3326,0.3328,-0.4698;1.0678,0.7187,0.5206;0.8392,-0.6342,-0.603;0.951,1.0314,-1.2222;3.5042,0.7156,-2.905;3.8468,0.2063,-4.0546;4.1131,0.9733,-5.1727;4.0415,2.3668,-5.1984;3.7671,2.9015,-4.3036;4.3641,2.7777,-6.4613;4.3992,3.8044,-6.7987;4.6441,1.6092,-7.2458;4.9297,1.5825,-8.2884;4.4847,0.5188,-6.4368;4.598,-0.5362,-6.6376;3.9237,-1.548,-4.1017)|",4.465388286705
"[H]OC1=C([H])C([H])=C(Cl)C([H])=C1/N=C(/O[H])C([H])([H])C([H])([H])Cl |(-5.1783,1.376,1.0512;-4.2226,1.2475,1.1544;-3.841,0.1677,0.3942;-4.763,-0.6295,-0.284;-5.8246,-0.4003,-0.2106;-4.3462,-1.7183,-1.0501;-5.0678,-2.3352,-1.5734;-2.9842,-1.9936,-1.1311;-2.4296,-3.3589,-2.095;-2.0485,-1.2011,-0.4718;-0.9874,-1.404,-0.5622;-2.4606,-0.1179,0.3163;-1.4907,0.7179,0.8844;-1.3101,0.8342,2.1335;-0.3699,1.7396,2.5235;-0.2134,1.6585,3.4771;-2.0003,0.0868,3.2639;-2.6188,-0.7109,2.848;-1.2372,-0.3864,3.8966;-2.8642,1.0231,4.1133;-3.657,1.4611,3.5092;-2.282,1.8216,4.5779;-3.6599,0.1176,5.4701)|",5.123903804915
"[H]OC(=O)C1=C2N(C(=O)C([H])=C1[H])C([H])([H])C([H])([H])C([H])([H])C([H])([H])C2([H])[H] |(-2.461,-3.4547,0.7416;-1.8453,-3.9298,0.1604;-0.8328,-3.099,-0.2293;0.1332,-3.5772,-0.7819;-1.0563,-1.6495,0.0426;-0.006,-0.7709,0.2966;-0.2674,0.5616,0.4832;-1.5848,1.1401,0.4218;-1.7453,2.3457,0.5859;-2.6424,0.1972,0.1534;-3.6421,0.6091,0.0811;-2.384,-1.1246,-0.0242;-3.2054,-1.7944,-0.2694;0.8225,1.5325,0.731;0.3085,2.4593,0.9792;1.3907,1.2127,1.611;1.7469,1.7468,-0.4776;1.1285,1.8212,-1.3813;2.2287,2.7248,-0.3522;2.8357,0.6797,-0.6483;3.4506,0.929,-1.5225;3.508,0.7263,0.2215;2.317,-0.7566,-0.7871;3.1739,-1.4373,-0.8519;1.757,-0.8815,-1.7219;1.432,-1.2239,0.3937;1.8612,-0.8649,1.3386;1.4418,-2.3101,0.424)|",4.66947367458
"[H]C([H])([H])OC(=O)C1=C(C(=O)OC([H])([H])[H])N(C([H])([H])C(=O)C([H])([H])[H])N=N1 |(-2.0456,-6.5562,-5.6204;-1.0288,-6.7307,-5.2687;-0.9871,-7.6201,-4.6342;-0.3458,-6.8631,-6.1122;-0.6801,-5.5579,-4.5165;0.5622,-5.5601,-4.002;1.3499,-6.4764,-4.1164;0.8566,-4.2874,-3.2984;1.8419,-4.0232,-2.3521;2.8534,-4.8376,-1.6365;3.9205,-4.3886,-1.2582;2.4431,-6.0909,-1.4523;3.3855,-6.9669,-0.8072;3.6074,-6.6071,0.2005;4.3101,-7.021,-1.3867;2.8944,-7.9385,-0.7732;1.7434,-2.6835,-2.1495;2.4648,-1.8268,-1.2306;3.5406,-1.9636,-1.3734;2.1989,-0.7991,-1.4924;2.1301,-2.1151,0.2425;1.4952,-3.0934,0.5698;2.6625,-1.0997,1.2307;2.1049,-0.1592,1.1327;3.7175,-0.8732,1.0358;2.545,-1.4809,2.2466;0.7476,-2.1559,-2.9003;0.2202,-3.1185,-3.597)|",5.54023799618
"[H]C(=O)C1=C2C(=O)N([H])C([H])=C([H])N2N=C1C1=C([H])C([H])=C([H])C([H])=C1[H] |(4.9149,-3.2205,-0.9362;3.8833,-2.8535,-0.803;2.946,-3.6371,-0.8327;3.8215,-1.3979,-0.6012;4.9945,-0.6231,-0.6097;6.4174,-0.9287,-0.7883;6.8971,-2.0381,-0.9827;7.2305,0.2054,-0.7148;8.2183,0.0175,-0.8358;6.7894,1.4981,-0.5;7.5407,2.2755,-0.4698;5.4757,1.7517,-0.3386;5.0333,2.7208,-0.1668;4.6061,0.6716,-0.3974;3.2779,0.7962,-0.2522;2.7655,-0.4418,-0.3698;1.2982,-0.5714,-0.2387;0.6219,-1.7933,-0.382;1.1857,-2.6934,-0.5936;-0.7662,-1.85,-0.2483;-1.2704,-2.8058,-0.3628;-1.5026,-0.6993,0.0279;-2.5834,-0.75,0.1315;-0.8381,0.5221,0.1719;-1.3996,1.4274,0.3871;0.5442,0.5873,0.0404;1.0556,1.5363,0.1525)|",4.138851666105
"[H]O/C(=N/N([H])C1=NC([H])([H])C([H])([H])C1([H])[H])C1=C([H])C(N([H])[H])=C([H])C([H])=C1[H] |(-0.366,-2.5138,2.9736;-0.374,-2.4948,2.0034;-0.543,-1.1869,1.6119;-1.0127,-1.0396,0.4302;-1.1108,0.205,-0.1204;-0.6478,1.0011,0.3122;-1.4116,0.3302,-1.4598;-1.2878,1.4553,-2.0612;-1.7307,1.2918,-3.4472;-2.5576,1.9857,-3.6515;-0.9157,1.5777,-4.1258;-2.1582,-0.1984,-3.6447;-3.2024,-0.2761,-3.963;-1.5525,-0.6891,-4.4128;-1.9387,-0.8437,-2.2566;-1.2157,-1.6663,-2.2597;-2.8537,-1.2429,-1.8049;-0.0973,-0.1549,2.5851;1.0929,-0.3746,3.2944;1.6764,-1.2698,3.0931;1.5573,0.5546,4.2375;2.7213,0.3086,4.9646;3.1754,1.1303,5.3433;3.3856,-0.3128,4.52;0.7934,1.7114,4.4745;1.1337,2.4377,5.2089;-0.3939,1.9237,3.7801;-0.9757,2.8187,3.9832;-0.85,1.0058,2.833;-1.7911,1.1687,2.3185)|",4.160620774145
"[H]OC1=NN=C2C(=O)N(C([H])([H])C([H])([H])[H])C([H])([H])C([H])([H])N12 |(2.2278,-6.4,-3.7519;1.9614,-5.4703,-3.6409;2.632,-5.0099,-2.5739;3.4693,-5.676,-1.811;3.9066,-4.8005,-0.8364;3.3195,-3.6462,-1.0548;3.4357,-2.3883,-0.2749;3.996,-2.3343,0.8092;2.8092,-1.3009,-0.8717;2.9244,0.0119,-0.2279;3.623,-0.1098,0.6013;3.372,0.7156,-0.9434;1.5818,0.543,0.2791;1.7149,1.5311,0.734;1.1639,-0.1301,1.0343;0.8485,0.646,-0.53;2.4019,-1.3286,-2.2778;1.7297,-0.485,-2.4577;3.2734,-1.2059,-2.9399;1.6706,-2.6234,-2.6302;1.5237,-2.7084,-3.71;0.6919,-2.6649,-2.1372;2.4994,-3.7244,-2.1668)|",5.72527541452
"[H]C1=C(C([H])(F)F)C(Cl)=C([H])C(Cl)=N1 |(1.2699,-2.0117,-0.1578;0.669,-1.1054,-0.1002;-0.7224,-1.2038,-0.0787;-1.3568,-2.5669,-0.1406;-0.6004,-3.3579,-0.1966;-2.1262,-2.7944,0.9649;-2.1652,-2.6763,-1.2363;-1.4376,0.0027,-0.0015;-3.179,0.0357,0.0354;-0.7498,1.2095,0.0483;-1.2751,2.1542,0.1076;0.6471,1.1529,0.0186;1.5396,2.6623,0.0826;1.3562,0.0414,-0.0538)|",5.99194698801
"[H]C1=C([H])C(C([H])(F)F)=C(Cl)C(Cl)=N1 |(-2.0492,1.2191,0.08;-1.1184,0.658,0.0585;-1.1134,-0.7314,0.1265;-2.0464,-1.2823,0.2019;0.1054,-1.4106,0.0967;0.0882,-2.9219,0.171;-0.9368,-3.302,0.2441;0.6622,-3.4658,-0.9398;0.7797,-3.3601,1.2612;1.2845,-0.6578,-0.001;2.843,-1.4242,-0.0417;1.1607,0.7436,-0.0653;2.5848,1.7519,-0.1922;0.0019,1.3785,-0.0363)|",5.518468888139999
"[H]OC([H])([H])C([H])([H])N1C(=O)C2=C(C1=O)C([H])=C([H])C([H])=C2[H] |(-2.814,-3.0572,-1.336;-3.1628,-2.4651,-2.024;-4.0039,-1.5074,-1.4109;-4.0306,-0.6429,-2.0835;-5.0386,-1.8754,-1.3025;-3.5205,-1.0695,-0.0229;-3.6298,-1.8924,0.6923;-4.1092,-0.2187,0.3315;-2.1216,-0.6684,-0.0098;-1.6754,0.6648,-0.0614;-2.3938,1.6422,-0.1124;-0.1843,0.5873,-0.0243;0.1826,-0.7586,0.0507;-1.062,-1.5797,0.0542;-1.1821,-2.7918,0.1137;1.5131,-1.1466,0.0997;1.787,-2.1955,0.155;2.4811,-0.1335,0.0705;3.5342,-0.3974,0.1055;2.1134,1.2159,-0.0041;2.8871,1.978,-0.0258;0.7655,1.5971,-0.0528;0.4694,2.6399,-0.1108)|",4.740223275710001
"[H]C1=C([H])C([H])=C(C([H])([H])N2C(=O)C([H])([H])C([H])([H])[C@@]2([H])C([H])([H])C#N)C([H])=C1[H] |(-2.561,0.3702,-1.6036;-1.5008,0.3557,-1.365;-0.5604,0.0977,-2.367;-0.8884,-0.0835,-3.3874;0.7997,0.0834,-2.063;1.533,-0.1066,-2.8435;1.2388,0.3281,-0.7535;2.7216,0.307,-0.4322;2.8987,0.7251,0.5627;3.2845,0.903,-1.1561;3.3057,-1.0348,-0.5142;4.1557,-1.3871,-1.5403;4.5587,-0.6374,-2.4146;4.498,-2.8664,-1.3663;4.531,-3.3618,-2.3393;5.5005,-2.946,-0.9269;3.3987,-3.3895,-0.4309;3.7305,-4.1975,0.2277;2.556,-3.7682,-1.0179;2.9342,-2.139,0.363;1.849,-2.1415,0.5139;3.6181,-2.0761,1.7594;3.4147,-3.0125,2.294;4.7053,-1.9987,1.6386;3.1535,-0.972,2.6032;2.7617,-0.1034,3.2668;0.2912,0.5844,0.244;0.6228,0.7765,1.2619;-1.0729,0.599,-0.0601;-1.798,0.8022,0.7235)|",6.34569499366
"[H]C1=C(N([H])[H])C(N(=O)=O)=C([H])C([H])=C1N([H])C([H])([H])C([H])([H])N(C([H])([H])[H])C([H])([H])[H] |(5.9552,-1.3621,-0.5496;6.5049,-1.9671,0.1643;7.5026,-2.8289,-0.3373;7.7201,-2.8876,-1.6799;7.3312,-2.1663,-2.2671;8.5236,-3.4173,-1.993;8.2147,-3.6347,0.6009;9.2672,-4.5272,0.214;9.8336,-5.1966,1.0863;9.5923,-4.6072,-0.9921;7.9015,-3.5553,1.9736;8.4655,-4.1852,2.6499;6.9261,-2.7124,2.4401;6.7046,-2.6582,3.5021;6.2053,-1.8894,1.5278;5.2567,-1.0332,2.0241;4.9102,-1.218,2.9599;4.3025,-0.3096,1.2102;4.8262,0.3556,0.5119;3.6899,-0.9916,0.5976;3.4216,0.5513,2.124;2.6549,1.0625,1.5114;4.0502,1.3281,2.5738;2.8342,-0.2269,3.2166;2.3854,0.6286,4.3108;2.0165,0.0069,5.1332;1.5736,1.3203,4.0157;3.2241,1.225,4.6841;1.7596,-1.1065,2.7637;1.395,-1.7003,3.608;2.1279,-1.8006,2.0029;0.9029,-0.5496,2.3381)|",4.019121571885
"[H]O/C(=N/N([H])C(C([H])([H])[H])(C([H])([H])[H])C([H])([H])N(=O)=O)OC(C([H])([H])[H])(C([H])([H])[H])C([H])([H])[H] |(5.8228,1.2792,-2.6118;4.9585,0.8692,-2.7933;4.5796,0.3058,-1.6158;5.3259,0.4199,-0.5892;4.924,-0.2598,0.5786;3.9068,-0.3554,0.6076;5.3898,0.4141,1.8098;5.0102,1.9048,1.8634;5.3479,2.3773,2.7913;3.9195,2.0057,1.8126;5.4452,2.4481,1.0208;4.7601,-0.3518,2.9814;5.1544,0.0006,3.9373;4.9659,-1.4236,2.8953;3.6727,-0.2077,2.987;6.9298,0.2179,1.7788;7.3348,0.6277,0.8557;7.1746,-0.839,1.8838;7.6218,0.9335,2.9009;8.0803,2.0499,2.6661;7.6687,0.3682,3.9932;3.412,-0.3541,-1.6075;2.4945,-0.4926,-2.7561;1.3483,-1.3084,-2.1516;0.5802,-1.4958,-2.9088;0.8877,-0.7691,-1.3177;1.7142,-2.2712,-1.7816;3.1769,-1.2744,-3.8827;2.4472,-1.4891,-4.6713;3.561,-2.2276,-3.5046;4.0024,-0.7105,-4.3204;1.9979,0.8845,-3.2075;1.2146,0.7612,-3.9637;2.8032,1.4813,-3.6394;1.5681,1.4305,-2.3608)|",3.8912280621500006
"[H]C1=C(C(F)(F)F)N(C([H])([H])[H])N=C1C(=O)C([H])([H])[H] |(2.3135,2.7304,0.8913;2.8988,2.0236,1.4578;3.6877,2.2632,2.5608;3.9554,3.5459,3.2724;3.3048,4.5594,2.681;3.562,3.4965,4.569;5.2768,3.8489,3.2881;4.2335,1.0625,2.9381;5.1402,0.7706,4.0416;5.3239,1.6799,4.6105;6.0842,0.3872,3.6466;4.6871,0.017,4.6898;3.8384,0.0669,2.1383;3.0263,0.6345,1.2333;2.3752,-0.1525,0.1559;1.6461,0.4094,-0.6465;2.6527,-1.6405,0.105;3.7271,-1.8271,-0.0028;2.1103,-2.0773,-0.7354;2.3446,-2.12,1.0411)|",5.4966997801
"[H]O[C@]([H])(C1=NN(C([H])([H])[H])C(C(F)(F)F)=C1[H])C([H])([H])[H] |(2.7606,-1.8702,-0.699;2.7383,-1.4249,0.1655;2.5992,-0.0348,-0.0876;1.5317,0.2437,-0.0238;3.0778,0.3107,-1.4812;3.2824,-0.6788,-2.3535;3.6716,-0.0924,-3.502;3.9201,-0.9027,-4.6848;4.7462,-0.4747,-5.2548;3.0287,-0.954,-5.3182;4.1856,-1.9039,-4.3456;3.7019,1.2635,-3.3737;4.105,2.1728,-4.4829;3.9891,3.4583,-4.1102;5.3892,1.9674,-4.8683;3.3426,1.9868,-5.5879;3.3269,1.5658,-2.0784;3.2561,2.5502,-1.6412;3.3563,0.725,1.001;3.211,1.8069,0.9053;4.4266,0.503,0.9441;2.9887,0.4129,1.9833)|",6.370185240205
"[H]OC([H])([H])C([H])([H])C([H])([H])C1=NN(C([H])([H])[H])C(C(F)(F)F)=C1[H] |(3.371,2.0238,1.5615;3.8892,2.5681,2.1864;5.1567,2.7861,1.6026;5.0896,3.4364,0.7101;5.7368,3.3389,2.3507;5.9324,1.5106,1.2305;6.9891,1.7897,1.1162;5.8789,0.8082,2.0716;5.5146,0.8079,-0.0821;6.3113,0.116,-0.3787;5.4598,1.5629,-0.8803;4.2293,0.0216,-0.066;3.0755,0.5549,0.3531;2.1397,-0.4056,0.2227;0.7485,-0.1168,0.5395;0.2845,-1.0003,0.9803;0.1965,0.176,-0.3592;0.7364,0.7019,1.2591;2.6767,-1.5432,-0.2953;1.874,-2.7704,-0.5589;2.6352,-3.7232,-1.1222;1.3406,-3.2836,0.5772;0.8327,-2.5208,-1.3894;4.0248,-1.309,-0.4909;4.7539,-2.0067,-0.875)|",5.918476248375
"[H]OC([H])([H])C([H])([H])C1=NN(C([H])([H])[H])C(C(F)(F)F)=C1[H] |(3.2712,2.9688,-0.1712;4.0429,3.5607,-0.2396;5.0834,2.7826,-0.7994;4.8627,2.4984,-1.8424;5.9737,3.4202,-0.8143;5.384,1.5143,0.0183;5.6048,1.818,1.0505;6.287,1.028,-0.3717;4.2532,0.5213,0.0082;2.9806,0.9385,0.0564;2.2181,-0.1725,0.0424;0.7683,-0.0552,-0.007;0.3155,-0.8705,0.559;0.4089,-0.0842,-1.0406;0.5015,0.8998,0.4458;2.9863,-1.2944,-0.026;2.411,-2.6682,-0.0646;3.3828,-3.5894,-0.1711;1.6937,-2.9473,1.052;1.5644,-2.8309,-1.1104;4.3074,-0.8885,-0.0454;5.1793,-1.5244,-0.0852)|",6.2041957914
"[H]C1=C([H])C(C2=NC(C([H])([H])[H])=C(C(=O)C([H])([H])[H])O2)=C([H])C([H])=C1F |(9.474,-1.1009,-0.1227;8.7104,-0.3357,-0.0318;7.3558,-0.6527,-0.0433;7.0442,-1.6866,-0.1464;6.388,0.3582,0.0759;4.964,0.05,0.0616;3.9626,0.888,0.1695;2.8251,0.1113,0.0951;1.4586,0.7033,0.1782;0.7024,-0.0821,0.1585;1.2901,1.3874,-0.662;1.3569,1.2911,1.0976;3.1939,-1.2059,-0.0603;2.4527,-2.4616,-0.1895;1.227,-2.4665,-0.179;3.2631,-3.7372,-0.3309;3.9382,-3.6787,-1.1924;2.5778,-4.5778,-0.4525;3.8886,-3.8968,0.5556;4.5795,-1.2409,-0.0807;6.7972,1.6965,0.209;6.0418,2.4693,0.3011;8.1488,2.0215,0.221;8.4866,3.0474,0.3223;9.0849,0.9978,0.0995;10.3953,1.3055,0.1101)|",4.364706162020001
"[H]OC(=O)C([H])([H])[C@]([H])(C(=O)OC([H])([H])C([H])=C([H])[H])C1=C([H])C([H])=C([H])C([H])=C1[H] |(5.8565,-3.562,-1.6609;6.5502,-3.5036,-2.3458;5.9825,-3.6497,-3.5687;6.6482,-3.5778,-4.5718;4.4725,-3.8908,-3.5726;4.2107,-4.2318,-4.5761;4.2157,-4.6883,-2.8669;3.6302,-2.6255,-3.2626;3.9334,-1.8286,-3.9488;3.8124,-2.0272,-1.8675;3.6339,-0.8643,-1.5984;4.1494,-2.9673,-0.9306;4.2226,-2.4937,0.456;4.8541,-3.2283,0.9592;4.7168,-1.5187,0.4467;2.8626,-2.4178,1.0788;2.1851,-1.6812,0.6525;2.4858,-3.1773,2.1075;1.5016,-3.0813,2.5573;3.1517,-3.9159,2.5496;2.1411,-2.8944,-3.4856;1.4703,-2.2741,-4.5455;2.0093,-1.5861,-5.1921;0.1189,-2.5323,-4.7798;-0.3863,-2.0438,-5.6086;-0.5801,-3.4108,-3.9515;-1.6326,-3.6115,-4.1319;0.0798,-4.0287,-2.8873;-0.4582,-4.7107,-2.2343;1.4311,-3.7731,-2.6551;1.9321,-4.2516,-1.818)|",6.119840497745
"[H]/N=C(\C1=C([H])C(Cl)=C([H])C([H])=C1[H])N([H])C#N |(-2.442,-1.897,1.2665;-2.1881,-2.5991,0.5704;-1.1719,-2.2165,-0.1009;-0.4632,-0.9099,0.0043;0.9372,-0.8435,0.0211;1.539,-1.7396,-0.0768;1.5572,0.3924,0.1884;3.3093,0.4721,0.2332;0.8147,1.5654,0.3246;1.3211,2.5169,0.4458;-0.5784,1.4913,0.298;-1.1651,2.4001,0.3934;-1.2177,0.2635,0.1439;-2.3018,0.2116,0.1076;-0.6502,-3.1618,-1.0129;-1.1488,-4.0471,-0.9948;0.0586,-2.8765,-2.1165;0.6977,-2.667,-3.069)|",5.513026611130001
================================================
FILE: tests/test_architectures.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different architectures of graphium/nn/architectures...
The layers are not thoroughly tested due to the difficulty of testing them
"""
import torch
import unittest as ut
from copy import deepcopy
import sys
import traceback
from graphium.nn.architectures import FeedForwardNN, FeedForwardPyg, FullGraphMultiTaskNetwork
from graphium.nn.base_layers import FCLayer
from graphium.nn.residual_connections import (
ResidualConnectionConcat,
ResidualConnectionDenseNet,
ResidualConnectionNone,
ResidualConnectionSimple,
ResidualConnectionWeighted,
)
from torch_geometric.data import Data, Batch
from graphium.utils.spaces import LAYERS_DICT
class test_FeedForwardNN(ut.TestCase):
kwargs = {
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
}
norms = ["none", "batch_norm", "layer_norm"]
def test_forward_no_residual(self):
in_dim = 8
out_dim = 16
hidden_dims = [5, 6, 7]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="none",
residual_skip_steps=1,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_simple_residual_1(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="simple",
residual_skip_steps=1,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_norms(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
for normalization in self.norms:
this_kwargs = deepcopy(self.kwargs)
this_kwargs["normalization"] = normalization
err_msg = f"normalization = {normalization}"
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="simple",
residual_skip_steps=1,
**this_kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1, msg=err_msg)
self.assertEqual(lnn.layers[0].in_dim, in_dim, msg=err_msg)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0], msg=err_msg)
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1], msg=err_msg)
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2], msg=err_msg)
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim], msg=err_msg)
def test_forward_simple_residual_2(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="simple",
residual_skip_steps=2,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, hidden_dims[4])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_concat_residual_1(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="concat",
residual_skip_steps=1,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, 2 * hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, 2 * hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, 2 * hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, 2 * hidden_dims[4])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_concat_residual_2(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="concat",
residual_skip_steps=2,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, 1 * hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, 1 * hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, 2 * hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, 1 * hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, 2 * hidden_dims[4])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_densenet_residual_1(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="densenet",
residual_skip_steps=1,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, 2 * hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, 3 * hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, 4 * hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, 5 * hidden_dims[4])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_densenet_residual_2(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="densenet",
residual_skip_steps=2,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, 1 * hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, 2 * hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, 1 * hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, 3 * hidden_dims[4])
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_weighted_residual_1(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="weighted",
residual_skip_steps=1,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, hidden_dims[4])
self.assertEqual(len(lnn.residual_layer.residual_list), len(hidden_dims))
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
def test_forward_weighted_residual_2(self):
in_dim = 8
out_dim = 16
hidden_dims = [6, 6, 6, 6, 6]
batch = 2
lnn = FeedForwardNN(
in_dim=in_dim,
out_dim=out_dim,
hidden_dims=hidden_dims,
residual_type="weighted",
residual_skip_steps=2,
**self.kwargs,
)
self.assertEqual(len(lnn.layers), len(hidden_dims) + 1)
self.assertEqual(lnn.layers[0].in_dim, in_dim)
self.assertEqual(lnn.layers[1].in_dim, hidden_dims[0])
self.assertEqual(lnn.layers[2].in_dim, hidden_dims[1])
self.assertEqual(lnn.layers[3].in_dim, hidden_dims[2])
self.assertEqual(lnn.layers[4].in_dim, hidden_dims[3])
self.assertEqual(lnn.layers[5].in_dim, hidden_dims[4])
self.assertEqual(len(lnn.residual_layer.residual_list), (len(hidden_dims) // 2 + 1))
feat = torch.FloatTensor(batch, in_dim)
feat_out = lnn.forward(feat)
self.assertListEqual(list(feat_out.shape), [batch, out_dim])
class test_FeedForwardGraph(ut.TestCase):
kwargs = {
"activation": "relu",
"last_activation": "none",
"dropout": 0.2,
"name": "LNN",
}
in_dim = 7
out_dim = 11
in_dim_edges = 13
hidden_dims = [6, 6, 6, 6, 6]
edge_idx1 = (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3]))
edge_idx2 = (torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0]))
num_edges1 = len(edge_idx1[0])
num_nodes1 = max(edge_idx1[0].max(), edge_idx1[1].max()) + 1
num_edges2 = len(edge_idx2[0])
num_nodes2 = max(edge_idx2[0].max(), edge_idx2[1].max()) + 1
h1 = torch.zeros(num_nodes1, in_dim, dtype=torch.float32)
e1 = torch.ones(num_edges1, in_dim_edges, dtype=torch.float32)
h2 = torch.ones(num_nodes2, in_dim, dtype=torch.float32)
e2 = torch.zeros(num_edges2, in_dim_edges, dtype=torch.float32)
g1 = Data(feat=h1, edge_index=torch.stack(edge_idx1), edge_feat=e1)
g2 = Data(feat=h2, edge_index=torch.stack(edge_idx2), edge_feat=e2)
data_list = [g1, g2, deepcopy(g1), deepcopy(g2)]
batch_pyg = Batch.from_data_list(data_list)
num_nodes = batch_pyg.num_nodes
num_edges = batch_pyg.num_edges
batch_size = len(data_list)
virtual_nodes = ["none", "mean", "sum"]
norms = ["none", "batch_norm", "layer_norm"]
pna_kwargs = {"aggregators": ["mean", "max", "sum"], "scalers": ["identity", "amplification"]}
layers_kwargs = {
"pyg:gin": {},
"pyg:gine": {"in_dim_edges": in_dim_edges},
"pyg:gated-gcn": {"in_dim_edges": in_dim_edges, "hidden_dims_edges": hidden_dims},
"pyg:pna-msgpass#1": {"layer_kwargs": pna_kwargs, "in_dim_edges": 0},
"pyg:pna-msgpass#2": {"layer_kwargs": pna_kwargs, "in_dim_edges": in_dim_edges},
}
def test_forward_no_residual(self):
for residual_skip_steps in [1, 2]:
for virtual_node in self.virtual_nodes:
for normalization in self.norms:
for layer_name, this_kwargs in self.layers_kwargs.items():
err_msg = f"virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}"
layer_type = layer_name.split("#")[0]
# PYG
if layer_type.startswith("pyg:"):
layer_class = FeedForwardPyg
bg = deepcopy(self.batch_pyg)
gnn = layer_class(
in_dim=self.in_dim,
out_dim=self.out_dim,
hidden_dims=self.hidden_dims,
residual_type="none",
residual_skip_steps=residual_skip_steps,
layer_type=layer_type,
normalization=normalization,
**this_kwargs,
**self.kwargs,
)
# gnn.to(torch.float32)
self.assertIsInstance(gnn.residual_layer, ResidualConnectionNone)
self.assertEqual(len(gnn.layers), len(self.hidden_dims) + 1, msg=err_msg)
self.assertEqual(gnn.layers[0].out_dim, self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[1].out_dim, self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[2].out_dim, self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[3].out_dim, self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[4].out_dim, self.hidden_dims[4], msg=err_msg)
self.assertEqual(gnn.layers[5].out_dim, self.out_dim, msg=err_msg)
f = gnn.layers[0].out_dim_factor
self.assertEqual(gnn.layers[0].in_dim, self.in_dim, msg=err_msg)
self.assertEqual(gnn.layers[1].in_dim, f * self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[2].in_dim, f * self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[3].in_dim, f * self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[4].in_dim, f * self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[5].in_dim, f * self.hidden_dims[4], msg=err_msg)
out_g = gnn.forward(bg)
feat_out = out_g["feat"]
edge_feat_out = out_g["edge_feat"]
out_dim_edges = (
gnn.full_dims_edges[-1] if gnn.full_dims_edges is not None else self.in_dim_edges
)
self.assertListEqual(
list(feat_out.shape), [self.num_nodes, self.out_dim], msg=err_msg
)
self.assertListEqual(
list(edge_feat_out.shape), [self.num_edges, out_dim_edges], msg=err_msg
)
def test_forward_simple_residual(self):
for residual_skip_steps in [1, 2]:
for virtual_node in self.virtual_nodes:
for normalization in self.norms:
for layer_name, this_kwargs in self.layers_kwargs.items():
err_msg = f"virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}"
layer_type = layer_name.split("#")[0]
# PYG
if layer_type.startswith("pyg:"):
layer_class = FeedForwardPyg
bg = deepcopy(self.batch_pyg)
gnn = layer_class(
in_dim=self.in_dim,
out_dim=self.out_dim,
hidden_dims=self.hidden_dims,
residual_type="simple",
residual_skip_steps=1,
layer_type=layer_type,
**this_kwargs,
**self.kwargs,
)
# gnn.to(torch.float32)
self.assertIsInstance(gnn.residual_layer, ResidualConnectionSimple)
self.assertEqual(len(gnn.layers), len(self.hidden_dims) + 1, msg=err_msg)
self.assertEqual(gnn.layers[0].out_dim, self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[1].out_dim, self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[2].out_dim, self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[3].out_dim, self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[4].out_dim, self.hidden_dims[4], msg=err_msg)
self.assertEqual(gnn.layers[5].out_dim, self.out_dim, msg=err_msg)
f = gnn.layers[0].out_dim_factor
self.assertEqual(gnn.layers[0].in_dim, self.in_dim, msg=err_msg)
self.assertEqual(gnn.layers[1].in_dim, f * self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[2].in_dim, f * self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[3].in_dim, f * self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[4].in_dim, f * self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[5].in_dim, f * self.hidden_dims[4], msg=err_msg)
out_g = gnn.forward(bg)
feat_out = out_g["feat"]
edge_feat_out = out_g["edge_feat"]
out_dim_edges = (
gnn.full_dims_edges[-1] if gnn.full_dims_edges is not None else self.in_dim_edges
)
self.assertListEqual(
list(feat_out.shape), [self.num_nodes, self.out_dim], msg=err_msg
)
self.assertListEqual(
list(edge_feat_out.shape), [self.num_edges, out_dim_edges], msg=err_msg
)
def test_forward_weighted_residual(self):
for residual_skip_steps in [1, 2]:
for virtual_node in self.virtual_nodes:
for normalization in self.norms:
for layer_name, this_kwargs in self.layers_kwargs.items():
err_msg = f"virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}"
layer_type = layer_name.split("#")[0]
# PYG
if layer_type.startswith("pyg:"):
layer_class = FeedForwardPyg
bg = deepcopy(self.batch_pyg)
gnn = layer_class(
in_dim=self.in_dim,
out_dim=self.out_dim,
hidden_dims=self.hidden_dims,
residual_type="weighted",
residual_skip_steps=residual_skip_steps,
layer_type=layer_type,
**this_kwargs,
**self.kwargs,
)
# gnn.to(torch.float32)
self.assertIsInstance(gnn.residual_layer, ResidualConnectionWeighted)
self.assertEqual(len(gnn.layers), len(self.hidden_dims) + 1, msg=err_msg)
self.assertEqual(gnn.layers[0].out_dim, self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[1].out_dim, self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[2].out_dim, self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[3].out_dim, self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[4].out_dim, self.hidden_dims[4], msg=err_msg)
self.assertEqual(gnn.layers[5].out_dim, self.out_dim, msg=err_msg)
f = gnn.layers[0].out_dim_factor
self.assertEqual(gnn.layers[0].in_dim, self.in_dim, msg=err_msg)
self.assertEqual(gnn.layers[1].in_dim, f * self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[2].in_dim, f * self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[3].in_dim, f * self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[4].in_dim, f * self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[5].in_dim, f * self.hidden_dims[4], msg=err_msg)
out_g = gnn.forward(bg)
feat_out = out_g["feat"]
edge_feat_out = out_g["edge_feat"]
out_dim_edges = (
gnn.full_dims_edges[-1] if gnn.full_dims_edges is not None else self.in_dim_edges
)
self.assertListEqual(
list(feat_out.shape), [self.num_nodes, self.out_dim], msg=err_msg
)
self.assertListEqual(
list(edge_feat_out.shape), [self.num_edges, out_dim_edges], msg=err_msg
)
def test_forward_concat_residual(self):
for residual_skip_steps in [1, 2]:
for virtual_node in self.virtual_nodes:
for normalization in self.norms:
for layer_name, this_kwargs in self.layers_kwargs.items():
err_msg = f"virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}"
layer_type = layer_name.split("#")[0]
# PYG
if layer_type.startswith("pyg:"):
layer_class = FeedForwardPyg
bg = deepcopy(self.batch_pyg)
gnn = layer_class(
in_dim=self.in_dim,
out_dim=self.out_dim,
hidden_dims=self.hidden_dims,
residual_type="concat",
residual_skip_steps=residual_skip_steps,
layer_type=layer_type,
**this_kwargs,
**self.kwargs,
)
# gnn.to(torch.float32)
self.assertIsInstance(gnn.residual_layer, ResidualConnectionConcat)
self.assertEqual(len(gnn.layers), len(self.hidden_dims) + 1, msg=err_msg)
self.assertEqual(gnn.layers[0].out_dim, self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[1].out_dim, self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[2].out_dim, self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[3].out_dim, self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[4].out_dim, self.hidden_dims[4], msg=err_msg)
self.assertEqual(gnn.layers[5].out_dim, self.out_dim, msg=err_msg)
f = gnn.layers[0].out_dim_factor
f2 = [2 * f if ((ii % residual_skip_steps) == 0 and ii > 0) else f for ii in range(6)]
self.assertEqual(gnn.layers[0].in_dim, self.in_dim, msg=err_msg)
self.assertEqual(gnn.layers[1].in_dim, f2[0] * self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[2].in_dim, f2[1] * self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[3].in_dim, f2[2] * self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[4].in_dim, f2[3] * self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[5].in_dim, f2[4] * self.hidden_dims[4], msg=err_msg)
out_g = gnn.forward(bg)
feat_out = out_g["feat"]
edge_feat_out = out_g["edge_feat"]
out_dim_edges = (
gnn.full_dims_edges[-1] if gnn.full_dims_edges is not None else self.in_dim_edges
)
self.assertListEqual(
list(feat_out.shape), [self.num_nodes, self.out_dim], msg=err_msg
)
self.assertListEqual(
list(edge_feat_out.shape), [self.num_edges, out_dim_edges], msg=err_msg
)
def test_forward_densenet_residual(self):
for residual_skip_steps in [1, 2]:
for virtual_node in self.virtual_nodes:
for normalization in self.norms:
for layer_name, this_kwargs in self.layers_kwargs.items():
err_msg = f"virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}"
layer_type = layer_name.split("#")[0]
# PYG
if layer_type.startswith("pyg:"):
layer_class = FeedForwardPyg
bg = deepcopy(self.batch_pyg)
gnn = layer_class(
in_dim=self.in_dim,
out_dim=self.out_dim,
hidden_dims=self.hidden_dims,
residual_type="densenet",
residual_skip_steps=residual_skip_steps,
layer_type=layer_type,
**this_kwargs,
**self.kwargs,
)
# gnn.to(torch.float32)
self.assertIsInstance(gnn.residual_layer, ResidualConnectionDenseNet)
self.assertEqual(len(gnn.layers), len(self.hidden_dims) + 1, msg=err_msg)
self.assertEqual(gnn.layers[0].out_dim, self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[1].out_dim, self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[2].out_dim, self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[3].out_dim, self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[4].out_dim, self.hidden_dims[4], msg=err_msg)
self.assertEqual(gnn.layers[5].out_dim, self.out_dim, msg=err_msg)
f = gnn.layers[0].out_dim_factor
f2 = [
(
((ii // residual_skip_steps) + 1) * f
if ((ii % residual_skip_steps) == 0 and ii > 0)
else f
)
for ii in range(6)
]
self.assertEqual(gnn.layers[0].in_dim, self.in_dim, msg=err_msg)
self.assertEqual(gnn.layers[1].in_dim, f2[0] * self.hidden_dims[0], msg=err_msg)
self.assertEqual(gnn.layers[2].in_dim, f2[1] * self.hidden_dims[1], msg=err_msg)
self.assertEqual(gnn.layers[3].in_dim, f2[2] * self.hidden_dims[2], msg=err_msg)
self.assertEqual(gnn.layers[4].in_dim, f2[3] * self.hidden_dims[3], msg=err_msg)
self.assertEqual(gnn.layers[5].in_dim, f2[4] * self.hidden_dims[4], msg=err_msg)
out_g = gnn.forward(bg)
feat_out = out_g["feat"]
edge_feat_out = out_g["edge_feat"]
out_dim_edges = (
gnn.full_dims_edges[-1] if gnn.full_dims_edges is not None else self.in_dim_edges
)
self.assertListEqual(
list(feat_out.shape), [self.num_nodes, self.out_dim], msg=err_msg
)
self.assertListEqual(
list(edge_feat_out.shape), [self.num_edges, out_dim_edges], msg=err_msg
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_attention.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the attention layer
"""
from ast import Assert
import numpy as np
import torch
import unittest as ut
from torch_geometric.data import Data, Batch
from copy import deepcopy
from graphium.ipu.to_dense_batch import to_dense_batch
from graphium.nn.base_layers import MultiheadAttentionMup
def seed_everything(seed: int):
import random, os
import numpy as np
import torch
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
class test_MultiHeadAttention(ut.TestCase):
seed_everything(42)
in_dim = 12
out_dim = 12
in_dim_edges = 10
out_dim_edges = 10
edge_idx1 = torch.stack([torch.tensor([0, 1, 2, 3, 2]), torch.tensor([1, 2, 3, 0, 0])])
edge_idx2 = torch.stack([torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])])
x1 = torch.randn(edge_idx1.max() + 1, in_dim, dtype=torch.float32)
e1 = torch.randn(edge_idx1.shape[-1], in_dim_edges, dtype=torch.float32)
x2 = torch.randn(edge_idx2.max() + 1, in_dim, dtype=torch.float32)
e2 = torch.randn(edge_idx2.shape[-1], in_dim_edges, dtype=torch.float32)
g1 = Data(feat=x1, edge_index=edge_idx1, edge_feat=e1)
g2 = Data(feat=x2, edge_index=edge_idx2, edge_feat=e2)
bg = Batch.from_data_list([g1, g2])
attn_kwargs = {"embed_dim": in_dim, "num_heads": 2, "batch_first": True}
def test_attention_class(self):
bg = deepcopy(self.bg)
seed_everything(42)
attention_layer = MultiheadAttentionMup(biased_attention=False, **self.attn_kwargs)
attention_layer.eval()
seed_everything(42)
attention_layer_bias = MultiheadAttentionMup(biased_attention=True, **self.attn_kwargs)
attention_layer_bias.eval()
h_dense, mask, _ = to_dense_batch(
bg.feat,
batch=bg.batch,
batch_size=None,
max_num_nodes_per_graph=None,
drop_nodes_last_graph=False,
)
# attn_bias [batch, num_heads, nodes, nodes]
nodes = h_dense.size()[1]
attn_bias_3d = torch.zeros(2, 2, nodes, nodes)
h_dense.nodepair_gaussian_bias_3d = attn_bias_3d
# Apply attention layer and attention layer with bias.
h_attn_output = attention_layer(
h_dense,
h_dense,
h_dense,
attn_bias=None,
attn_mask=None,
key_padding_mask=~mask,
need_weights=False,
)[0]
h_attn_output_biased = attention_layer_bias(
h_dense,
h_dense,
h_dense,
attn_bias=attn_bias_3d,
attn_mask=None,
key_padding_mask=~mask,
need_weights=False,
)[0]
self.assertEqual(h_attn_output.size(), h_attn_output_biased.size())
np.testing.assert_array_almost_equal(
h_attn_output.detach().numpy(), h_attn_output_biased.detach().numpy(), decimal=0
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_base_layers.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different layers of graphium/nn/base_layers
"""
import torch
import unittest as ut
from torch_geometric.data import Data, Batch
from copy import deepcopy
from graphium.nn.base_layers import DropPath, TransformerEncoderLayerMup
from graphium.ipu.to_dense_batch import to_dense_batch, to_sparse_batch
class test_Base_Layers(ut.TestCase):
in_dim = 21
out_dim = 11
in_dim_edges = 13
out_dim_edges = 17
edge_idx1 = torch.stack([torch.tensor([0, 1, 2, 3, 2]), torch.tensor([1, 2, 3, 0, 0])])
edge_idx2 = torch.stack([torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])])
x1 = torch.randn(edge_idx1.max() + 1, in_dim, dtype=torch.float32)
e1 = torch.randn(edge_idx1.shape[-1], in_dim_edges, dtype=torch.float32)
x2 = torch.randn(edge_idx2.max() + 1, in_dim, dtype=torch.float32)
e2 = torch.randn(edge_idx2.shape[-1], in_dim_edges, dtype=torch.float32)
g1 = Data(feat=x1, edge_index=edge_idx1, edge_feat=e1)
g2 = Data(feat=x2, edge_index=edge_idx2, edge_feat=e2)
bg = Batch.from_data_list([g1, g2])
batch_size = 2
max_num_nodes_per_graph = max(x1.shape[0], x2.shape[0])
# for drop_rate=0.5, test if the output shape is correct
def test_droppath_layer_0p5(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
layer = DropPath(drop_rate=0.5)
h_out = layer.forward(feat_in, bg.batch)
self.assertEqual(h_out.shape, feat_in.shape)
# for drop_rate=1.0, test if the output are all zeros
def test_droppath_layer_1p0(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
zero_tesor = torch.zeros(feat_in.shape)
layer = DropPath(drop_rate=1.0)
h_out = layer.forward(feat_in, bg.batch)
self.assertTrue(torch.allclose(zero_tesor, h_out.detach()))
# for drop_rate=0.0, test if the output matches the original output
def test_droppath_layer_0p0(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
layer = DropPath(drop_rate=0.0)
h_out = layer.forward(feat_in, bg.batch)
self.assertTrue(torch.allclose(feat_in.detach(), h_out.detach()))
# test output shape is correct for TransformerEncoderLayerMup
def test_transformer_encoder_layer_mup(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
layer = TransformerEncoderLayerMup(
biased_attention=False, d_model=self.in_dim, nhead=1, dim_feedforward=4 * self.in_dim
)
feat_dense, key_padding_mask, idx = to_dense_batch(
feat_in,
batch=bg.batch,
batch_size=self.batch_size,
max_num_nodes_per_graph=self.max_num_nodes_per_graph,
drop_nodes_last_graph=False,
)
attn_mask = None
key_padding_mask = ~key_padding_mask
h_out_dense = layer.forward(feat_dense)
h_out = to_sparse_batch(h_out_dense, mask_idx=idx)
self.assertEqual(h_out.shape, feat_in.shape)
================================================
FILE: tests/test_collate.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the collate
"""
import unittest as ut
from copy import deepcopy
import torch
import numpy as np
from torch_geometric.data import Data
from graphium.data.collate import collate_labels, graphium_collate_fn
class test_Collate(ut.TestCase):
def test_collate_labels(self):
# Create fake labels
labels_size_dict = {
"graph_label1": [1],
"graph_label2": [3],
"node_label2": [5],
"edge_label3": [5, 2],
"node_label4": [5, 1],
}
labels_dtype_dict = {
"graph_label1": torch.float32,
"graph_label2": torch.float16,
"node_label2": torch.float32,
"edge_label3": torch.float32,
"node_label4": torch.float32,
}
fake_label = {
"graph_label1": torch.FloatTensor([1]),
"graph_label2": torch.HalfTensor([1, 2, 3]),
"node_label2": torch.FloatTensor([1, 2, 3, 4, 5]),
"edge_label3": torch.FloatTensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]),
"node_label4": torch.FloatTensor([[1], [2], [3], [4], [5]]),
}
fake_labels = []
num_labels = 10
for i in range(num_labels):
pyg_labels = Data(x=torch.empty(5, 0), edge_index=torch.empty(2, 5))
for key, val in fake_label.items():
pyg_labels[key] = val + 17 * 2
fake_labels.append(pyg_labels)
# Collate labels and check for the right shapes and dtypes
collated_labels = collate_labels(
deepcopy(fake_labels), deepcopy(labels_size_dict), deepcopy(labels_dtype_dict)
)
self.assertEqual(collated_labels["graph_label1"].shape, torch.Size([num_labels, 1])) # , 1
self.assertEqual(collated_labels["graph_label2"].shape, torch.Size([num_labels, 3])) # , 1
self.assertEqual(collated_labels["node_label2"].shape, torch.Size([num_labels * 5, 1])) # , 5
self.assertEqual(collated_labels["edge_label3"].shape, torch.Size([num_labels * 5, 2])) # , 5, 2
self.assertEqual(collated_labels["node_label4"].shape, torch.Size([num_labels * 5, 1])) # , 5, 1
self.assertEqual(collated_labels["graph_label1"].dtype, torch.float32)
self.assertEqual(collated_labels["graph_label2"].dtype, torch.float16)
# Check that the values are correct
graph_label1_true = deepcopy(torch.stack([this_label["graph_label1"] for this_label in fake_labels]))
graph_label2_true = deepcopy(torch.stack([this_label["graph_label2"] for this_label in fake_labels]))
label2_true = deepcopy(torch.stack([this_label["node_label2"] for this_label in fake_labels]))
label3_true = deepcopy(torch.stack([this_label["edge_label3"] for this_label in fake_labels]))
label4_true = deepcopy(torch.stack([this_label["node_label4"] for this_label in fake_labels]))
# NOTE: Flatten due to the way Data objects are collated (concat along first dim, instead of stacked)
np.testing.assert_array_equal(collated_labels["graph_label1"].numpy(), graph_label1_true.numpy())
np.testing.assert_array_equal(collated_labels["graph_label2"].numpy(), graph_label2_true.numpy())
np.testing.assert_array_equal(
collated_labels["node_label2"].numpy(), label2_true.flatten(0, 1).unsqueeze(1).numpy()
)
np.testing.assert_array_equal(
collated_labels["edge_label3"].numpy(), label3_true.flatten(0, 1).numpy()
)
np.testing.assert_array_equal(
collated_labels["node_label4"].numpy(), label4_true.flatten(0, 1).numpy()
)
# Remove some labels and check that the collation still works and puts `nan` in the right places
missing_labels = {
"graph_label1": [1, 3, 5, 7, 9],
"graph_label2": [0, 4, 3, 1, 7],
"node_label2": [0, 2, 4, 6, 8],
"edge_label3": [0, 1, 2, 3, 4],
"node_label4": [5, 1, 4, 9, 6],
}
for key, missing_idx in missing_labels.items():
for idx in missing_idx:
fake_labels[idx].pop(key)
graph_label1_true[missing_labels["graph_label1"]] = float("nan")
graph_label2_true[missing_labels["graph_label2"]] = float("nan")
label2_true[missing_labels["node_label2"]] = float("nan")
label3_true[missing_labels["edge_label3"]] = float("nan")
label4_true[missing_labels["node_label4"]] = float("nan")
# Collate labels and check for the right shapes
labels_size_dict = {
"graph_label1": [1],
"graph_label2": [3],
"node_label2": [5],
"edge_label3": [5, 2],
"node_label4": [5, 1],
}
collated_labels = collate_labels(
deepcopy(fake_labels), deepcopy(labels_size_dict), deepcopy(labels_dtype_dict)
)
self.assertEqual(collated_labels["graph_label1"].shape, torch.Size([num_labels, 1])) # , 1
self.assertEqual(collated_labels["graph_label2"].shape, torch.Size([num_labels, 3])) # , 1
self.assertEqual(collated_labels["node_label2"].shape, torch.Size([num_labels * 5, 1])) # , 5
self.assertEqual(collated_labels["edge_label3"].shape, torch.Size([num_labels * 5, 2])) # , 5, 2
self.assertEqual(collated_labels["node_label4"].shape, torch.Size([num_labels * 5, 1])) # , 5, 1
# Check that the values are correct when some labels are missing
# NOTE: Flatten due to the way Data objects are collated (concat along first dim, instead of stacked)
np.testing.assert_array_equal(collated_labels["graph_label1"].numpy(), graph_label1_true.numpy())
np.testing.assert_array_equal(collated_labels["graph_label2"].numpy(), graph_label2_true.numpy())
np.testing.assert_array_equal(
collated_labels["node_label2"].numpy(), label2_true.flatten(0, 1).unsqueeze(1).numpy()
)
np.testing.assert_array_equal(
collated_labels["edge_label3"].numpy(), label3_true.flatten(0, 1).numpy()
)
np.testing.assert_array_equal(
collated_labels["node_label4"].numpy(), label4_true.flatten(0, 1).numpy()
)
# Now test the `graphium_collate_fn` function when only labels are given
fake_labels2 = [{"labels": this_label} for this_label in fake_labels]
collated_labels = graphium_collate_fn(
deepcopy(fake_labels2), labels_size_dict=labels_size_dict, labels_dtype_dict=labels_dtype_dict
)["labels"]
self.assertEqual(collated_labels["graph_label1"].shape, torch.Size([num_labels, 1]))
self.assertEqual(collated_labels["graph_label2"].shape, torch.Size([num_labels, 3]))
self.assertEqual(collated_labels["node_label2"].shape, torch.Size([num_labels * 5, 1])) # , 5
self.assertEqual(collated_labels["edge_label3"].shape, torch.Size([num_labels * 5, 2])) # , 5, 2
self.assertEqual(collated_labels["node_label4"].shape, torch.Size([num_labels * 5, 1])) # , 5, 1
# Check that the values are correct when some labels are missing
# NOTE: Flatten due to the way Data objects are collated (concat along first dim, instead of stacked)
np.testing.assert_array_equal(collated_labels["graph_label1"].numpy(), graph_label1_true.numpy())
np.testing.assert_array_equal(collated_labels["graph_label2"].numpy(), graph_label2_true.numpy())
np.testing.assert_array_equal(
collated_labels["node_label2"].numpy(), label2_true.flatten(0, 1).unsqueeze(1).numpy()
)
np.testing.assert_array_equal(
collated_labels["edge_label3"].numpy(), label3_true.flatten(0, 1).numpy()
)
np.testing.assert_array_equal(
collated_labels["node_label4"].numpy(), label4_true.flatten(0, 1).numpy()
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_data_utils.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pandas as pd
import unittest as ut
import graphium
import tempfile
class TestDataUtils(ut.TestCase):
def test_list_datasets(
self,
):
datasets = graphium.data.utils.list_graphium_datasets()
assert isinstance(datasets, set)
assert len(datasets) > 0
def test_download_datasets(self):
dataset_dir = tempfile.TemporaryDirectory()
data_path = graphium.data.utils.download_graphium_dataset(
"graphium-zinc-micro", output_path=dataset_dir.name
)
fpath = graphium.utils.fs.join(data_path, "ZINC-micro.csv")
df = pd.read_csv(fpath)
assert df.shape == (1000, 4) # type: ignore
assert df.columns.tolist() == ["SMILES", "SA", "logp", "score"] # type: ignore
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_datamodule.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import unittest as ut
import numpy as np
import torch
import pandas as pd
import tempfile
import graphium
from graphium.utils.fs import rm, exists, get_size
from graphium.data import GraphOGBDataModule, MultitaskFromSmilesDataModule
TEMP_CACHE_DATA_PATH = "tests/temp_cache_0000"
class Test_DataModule(ut.TestCase):
def test_ogb_datamodule(self):
# other datasets are too large to be tested
dataset_names = ["ogbg-molhiv", "ogbg-molpcba", "ogbg-moltox21", "ogbg-molfreesolv"]
dataset_name = dataset_names[3]
# Setup the featurization
featurization_args = {}
featurization_args["atom_property_list_float"] = [] # ["weight", "valence"]
featurization_args["atom_property_list_onehot"] = ["atomic-number", "degree"]
# featurization_args["conformer_property_list"] = ["positions_3d"]
featurization_args["edge_property_list"] = ["bond-type-onehot"]
featurization_args["add_self_loop"] = False
featurization_args["use_bonds_weights"] = False
featurization_args["explicit_H"] = False
# Config for datamodule
task_specific_args = {}
task_specific_args["task_1"] = {"task_level": "graph", "dataset_name": dataset_name}
dm_args = {}
dm_args["processed_graph_data_path"] = None
dm_args["featurization"] = featurization_args
dm_args["batch_size_training"] = 16
dm_args["batch_size_inference"] = 16
dm_args["num_workers"] = 0
dm_args["pin_memory"] = True
dm_args["featurization_n_jobs"] = 0
dm_args["featurization_progress"] = True
dm_args["featurization_backend"] = "loky"
dm_args["featurization_batch_size"] = 50
ds = GraphOGBDataModule(task_specific_args, **dm_args)
ds.prepare_data(save_smiles_and_ids=False)
# Check the keys in the dataset
ds.setup(save_smiles_and_ids=False)
assert set(ds.train_ds[0].keys()) == {"features", "labels"}
# Delete the cache if already exist
if exists(TEMP_CACHE_DATA_PATH):
rm(TEMP_CACHE_DATA_PATH, recursive=True)
# Reset the datamodule
ds = GraphOGBDataModule(task_specific_args, **dm_args)
ds.prepare_data(save_smiles_and_ids=True)
# Check the keys in the dataset
ds.setup(save_smiles_and_ids=True)
assert set(ds.train_ds[0].keys()) == {"smiles", "mol_ids", "features", "labels"}
# test module
assert ds.num_edge_feats == 5
assert ds.num_node_feats == 50
assert len(ds) == 642
# test batch loader
batch = next(iter(ds.train_dataloader()))
assert len(batch["smiles"]) == 16
assert len(batch["labels"]["graph_task_1"]) == 16
assert len(batch["mol_ids"]) == 16
def test_none_filtering(self):
# Create the objects to filter
list_of_num = [ii for ii in range(100)]
list_of_str = [str(ii) for ii in list_of_num]
tuple_of_num = tuple(list_of_num)
array_of_num = np.asarray(list_of_num)
array_of_str = np.asarray(list_of_str)
tensor_of_num = torch.as_tensor(array_of_num)
arrays_of_num = np.stack([list_of_num, list_of_num, list_of_num], axis=1)
arrays_of_str = np.stack([list_of_str, list_of_str, list_of_str], axis=1)
tensors_of_num = torch.as_tensor(arrays_of_num)
dic = {"str": list_of_str, "num": list_of_num}
df = pd.DataFrame(dic)
df_shuffled = df.sample(frac=1)
series_num = df["num"]
series_num_shuffled = df_shuffled["num"]
# Create different indexes to use for filtering
all_idx_none = [[3, 17, 88], [22, 33, 44, 55, 66, 77, 88], [], np.arange(len(list_of_num))]
# Loop all the indexes and filter the objects.
for ii, idx_none in enumerate(all_idx_none):
msg = f"Failed for ii={ii}"
# Create the true filtered sequences
filtered_num = [ii for ii in range(100) if ii not in idx_none]
filtered_str = [str(ii) for ii in filtered_num]
assert len(filtered_num) == len(list_of_num) - len(idx_none)
assert len(filtered_str) == len(list_of_str) - len(idx_none)
# Filter the sequences from the Datamodule function
(
list_of_num_2,
list_of_str_2,
tuple_of_num_2,
array_of_num_2,
array_of_str_2,
tensor_of_num_2,
df_2,
df_shuffled_2,
dic_2,
arrays_of_num_2,
arrays_of_str_2,
tensors_of_num_2,
series_num_2,
series_num_shuffled_2,
) = graphium.data.MultitaskFromSmilesDataModule._filter_none_molecules(
idx_none,
list_of_num,
list_of_str,
tuple_of_num,
array_of_num,
array_of_str,
tensor_of_num,
df,
df_shuffled,
dic,
arrays_of_num,
arrays_of_str,
tensors_of_num,
series_num,
series_num_shuffled,
)
df_shuffled_2 = df_shuffled_2.sort_values(by="num", axis=0)
series_num_shuffled_2 = series_num_shuffled_2.sort_values(axis=0)
# Assert the filtering is done correctly
self.assertListEqual(list_of_num_2, filtered_num, msg=msg)
self.assertListEqual(list_of_str_2, filtered_str, msg=msg)
self.assertListEqual(list(tuple_of_num_2), filtered_num, msg=msg)
self.assertListEqual(array_of_num_2.tolist(), filtered_num, msg=msg)
self.assertListEqual(array_of_str_2.tolist(), filtered_str, msg=msg)
self.assertListEqual(tensor_of_num_2.tolist(), filtered_num, msg=msg)
for jj in range(arrays_of_num.shape[1]):
self.assertListEqual(arrays_of_num_2[:, jj].tolist(), filtered_num, msg=msg)
self.assertListEqual(arrays_of_str_2[:, jj].tolist(), filtered_str, msg=msg)
self.assertListEqual(tensors_of_num_2[:, jj].tolist(), filtered_num, msg=msg)
self.assertListEqual(dic_2["num"], filtered_num, msg=msg)
self.assertListEqual(dic_2["str"], filtered_str, msg=msg)
self.assertListEqual(df_2["num"].tolist(), filtered_num, msg=msg)
self.assertListEqual(df_2["str"].tolist(), filtered_str, msg=msg)
self.assertListEqual(series_num_2.tolist(), filtered_num, msg=msg)
# When the dataframe is shuffled, the lists are different because the filtering
# is done on the row indexes, not the dataframe indexes.
bool_to_check = (len(idx_none) == 0) or (len(idx_none) == len(df_shuffled))
self.assertIs(df_shuffled_2["num"].tolist() == filtered_num, bool_to_check, msg=msg)
self.assertIs(df_shuffled_2["str"].tolist() == filtered_str, bool_to_check, msg=msg)
self.assertIs(series_num_shuffled_2.tolist() == filtered_num, bool_to_check, msg=msg)
def test_caching(self):
# other datasets are too large to be tested
dataset_name = "ogbg-molfreesolv"
# Setup the featurization
featurization_args = {}
featurization_args["atom_property_list_float"] = [] # ["weight", "valence"]
featurization_args["atom_property_list_onehot"] = ["atomic-number", "degree"]
featurization_args["edge_property_list"] = ["bond-type-onehot"]
featurization_args["add_self_loop"] = False
featurization_args["use_bonds_weights"] = False
featurization_args["explicit_H"] = False
# Config for datamodule
task_specific_args = {}
task_specific_args["task_1"] = {"task_level": "graph", "dataset_name": dataset_name}
dm_args = {}
dm_args["featurization"] = featurization_args
dm_args["batch_size_training"] = 16
dm_args["batch_size_inference"] = 16
dm_args["num_workers"] = 0
dm_args["pin_memory"] = True
dm_args["featurization_n_jobs"] = 0
dm_args["featurization_progress"] = True
dm_args["featurization_backend"] = "loky"
dm_args["featurization_batch_size"] = 50
# Delete the cache if already exist
if exists(TEMP_CACHE_DATA_PATH):
rm(TEMP_CACHE_DATA_PATH, recursive=True)
# Prepare the data. It should create the cache there
assert not exists(TEMP_CACHE_DATA_PATH)
ds = GraphOGBDataModule(task_specific_args, processed_graph_data_path=TEMP_CACHE_DATA_PATH, **dm_args)
# assert not ds.load_data_from_cache(verbose=False)
ds.prepare_data(save_smiles_and_ids=False)
# Check the keys in the dataset
ds.setup(save_smiles_and_ids=False)
assert set(ds.train_ds[0].keys()) == {"features", "labels"}
# ds_batch = next(iter(ds.train_dataloader()))
train_loader = ds.get_dataloader(ds.train_ds, shuffle=False, stage="train")
batch = next(iter(train_loader))
# Test loading cached data
assert exists(TEMP_CACHE_DATA_PATH)
cached_ds_from_ram = GraphOGBDataModule(
task_specific_args,
processed_graph_data_path=TEMP_CACHE_DATA_PATH,
dataloading_from="ram",
**dm_args,
)
cached_ds_from_ram.prepare_data()
cached_ds_from_ram.setup()
cached_train_loader_from_ram = cached_ds_from_ram.get_dataloader(
cached_ds_from_ram.train_ds, shuffle=False, stage="train"
)
batch_from_ram = next(iter(cached_train_loader_from_ram))
cached_ds_from_disk = GraphOGBDataModule(
task_specific_args,
processed_graph_data_path=TEMP_CACHE_DATA_PATH,
dataloading_from="disk",
**dm_args,
)
cached_ds_from_disk.prepare_data()
cached_ds_from_disk.setup()
cached_train_loader_from_disk = cached_ds_from_disk.get_dataloader(
cached_ds_from_disk.train_ds, shuffle=False, stage="train"
)
batch_from_disk = next(iter(cached_train_loader_from_disk))
# Features are the same
np.testing.assert_array_almost_equal(
batch["features"].edge_index, batch_from_ram["features"].edge_index
)
np.testing.assert_array_almost_equal(
batch["features"].edge_index, batch_from_disk["features"].edge_index
)
assert batch["features"].num_nodes == batch_from_ram["features"].num_nodes
assert batch["features"].num_nodes == batch_from_disk["features"].num_nodes
np.testing.assert_array_almost_equal(
batch["features"].edge_weight, batch_from_ram["features"].edge_weight
)
np.testing.assert_array_almost_equal(
batch["features"].edge_weight, batch_from_disk["features"].edge_weight
)
np.testing.assert_array_almost_equal(batch["features"].feat, batch_from_ram["features"].feat)
np.testing.assert_array_almost_equal(batch["features"].feat, batch_from_disk["features"].feat)
np.testing.assert_array_almost_equal(
batch["features"].edge_feat, batch_from_ram["features"].edge_feat
)
np.testing.assert_array_almost_equal(
batch["features"].edge_feat, batch_from_disk["features"].edge_feat
)
np.testing.assert_array_almost_equal(batch["features"].batch, batch_from_ram["features"].batch)
np.testing.assert_array_almost_equal(batch["features"].batch, batch_from_disk["features"].batch)
np.testing.assert_array_almost_equal(batch["features"].ptr, batch_from_ram["features"].ptr)
np.testing.assert_array_almost_equal(batch["features"].ptr, batch_from_disk["features"].ptr)
# Labels are the same
np.testing.assert_array_almost_equal(
batch["labels"].graph_task_1, batch_from_ram["labels"].graph_task_1
)
np.testing.assert_array_almost_equal(
batch["labels"].graph_task_1, batch_from_disk["labels"].graph_task_1
)
np.testing.assert_array_almost_equal(batch["labels"].x, batch_from_ram["labels"].x)
np.testing.assert_array_almost_equal(batch["labels"].x, batch_from_disk["labels"].x)
np.testing.assert_array_almost_equal(batch["labels"].edge_index, batch_from_ram["labels"].edge_index)
np.testing.assert_array_almost_equal(batch["labels"].edge_index, batch_from_disk["labels"].edge_index)
np.testing.assert_array_almost_equal(batch["labels"].batch, batch_from_ram["labels"].batch)
np.testing.assert_array_almost_equal(batch["labels"].batch, batch_from_disk["labels"].batch)
np.testing.assert_array_almost_equal(batch["labels"].ptr, batch_from_ram["labels"].ptr)
np.testing.assert_array_almost_equal(batch["labels"].ptr, batch_from_disk["labels"].ptr)
# Delete the cache if already exist
if exists(TEMP_CACHE_DATA_PATH):
rm(TEMP_CACHE_DATA_PATH, recursive=True)
# Reset the datamodule
ds = GraphOGBDataModule(task_specific_args, processed_graph_data_path=TEMP_CACHE_DATA_PATH, **dm_args)
ds.prepare_data(save_smiles_and_ids=True)
ds.setup(save_smiles_and_ids=True)
assert set(ds.train_ds[0].keys()) == {"smiles", "mol_ids", "features", "labels"}
# test module
assert ds.num_edge_feats == 5
assert ds.num_node_feats == 50
assert len(ds) == 642
# test batch loader
batch = next(iter(ds.train_dataloader()))
assert len(batch["smiles"]) == 16
assert len(batch["labels"]["graph_task_1"]) == 16
assert len(batch["mol_ids"]) == 16
# Delete the cache if already exist
if exists(TEMP_CACHE_DATA_PATH):
rm(TEMP_CACHE_DATA_PATH, recursive=True)
def test_datamodule_with_none_molecules(self):
# Setup the featurization
featurization_args = {}
featurization_args["atom_property_list_float"] = [] # ["weight", "valence"]
featurization_args["atom_property_list_onehot"] = ["atomic-number", "degree"]
featurization_args["edge_property_list"] = ["bond-type-onehot"]
# Config for datamodule
bad_csv = "tests/data/micro_ZINC_corrupt.csv"
task_specific_args = {}
task_kwargs = {"df_path": bad_csv, "split_val": 0.0, "split_test": 0.0}
task_specific_args["task_1"] = {
"task_level": "graph",
"label_cols": "SA",
"smiles_col": "SMILES1",
**task_kwargs,
}
task_specific_args["task_2"] = {
"task_level": "graph",
"label_cols": "logp",
"smiles_col": "SMILES2",
**task_kwargs,
}
task_specific_args["task_3"] = {
"task_level": "graph",
"label_cols": "score",
"smiles_col": "SMILES3",
**task_kwargs,
}
# Read the corrupted dataset and get stats
df = pd.read_csv(bad_csv)
bad_smiles = (df["SMILES1"] == "XXX") & (df["SMILES2"] == "XXX") & (df["SMILES3"] == "XXX")
num_bad_smiles = sum(bad_smiles)
# Test the datamodule
datamodule = MultitaskFromSmilesDataModule(
task_specific_args=task_specific_args,
featurization_args=featurization_args,
featurization_n_jobs=0,
featurization_batch_size=1,
)
datamodule.prepare_data()
datamodule.setup(save_smiles_and_ids=True)
# Check that the number of molecules is correct
smiles = df["SMILES1"].tolist() + df["SMILES2"].tolist() + df["SMILES3"].tolist()
num_unique_smiles = len(set(smiles)) - 1 # -1 because of the XXX
# self.assertEqual(len(datamodule.train_ds), num_unique_smiles - num_bad_smiles)
# Change the index of the dataframe
index_smiles = []
for ii in range(len(df)):
if df["SMILES1"][ii] != "XXX":
smiles = df["SMILES1"][ii]
elif df["SMILES2"][ii] != "XXX":
smiles = df["SMILES2"][ii]
elif df["SMILES3"][ii] != "XXX":
smiles = df["SMILES3"][ii]
else:
smiles = "XXX"
index_smiles.append(smiles)
df["idx_smiles"] = index_smiles
df = df.set_index("idx_smiles")
# Convert the smilies from the train_ds to a list, and check the content
train_smiles = [d["smiles"] for d in datamodule.train_ds]
# Check that the set of smiles are the same
train_smiles_flat = list(set([item for sublist in train_smiles for item in sublist]))
train_smiles_flat.sort()
index_smiles_filt = list(set([smiles for smiles in index_smiles if smiles != "XXX"]))
index_smiles_filt.sort()
self.assertListEqual(train_smiles_flat, index_smiles_filt)
# Check that the smiles are correct for each datapoint in the dataset
for smiles in train_smiles:
self.assertEqual(len(set(smiles)), 1) # Check that all smiles are the same
this_smiles = smiles[0]
true_smiles = df.loc[this_smiles][["SMILES1", "SMILES2", "SMILES3"]]
num_true_smiles = sum(true_smiles != "XXX")
self.assertEqual(len(smiles), num_true_smiles) # Check that the number of smiles is correct
self.assertEqual(
this_smiles, true_smiles[true_smiles != "XXX"].values[0]
) # Check that the smiles are correct
# Convert the labels from the train_ds to a dataframe
train_labels = [{task: val[0] for task, val in d["labels"].items()} for d in datamodule.train_ds]
train_labels_df = pd.DataFrame(train_labels)
train_labels_df = train_labels_df.rename(
columns={"graph_task_1": "graph_SA", "graph_task_2": "graph_logp", "graph_task_3": "graph_score"}
)
train_labels_df["smiles"] = [s[0] for s in datamodule.train_ds.smiles]
train_labels_df = train_labels_df.set_index("smiles")
train_labels_df = train_labels_df.sort_index()
# Check that the labels are correct
df2 = df.reset_index()[~bad_smiles].set_index("idx_smiles").sort_index()
labels = train_labels_df[["graph_SA", "graph_logp", "graph_score"]].values
nans = np.isnan(labels)
true_nans = df2[["SMILES1", "SMILES2", "SMILES3"]].values == "XXX"
true_labels = df2[["SA", "logp", "score"]].values
true_labels[true_nans] = np.nan
np.testing.assert_array_equal(nans, true_nans) # Check that the nans are correct
np.testing.assert_array_almost_equal(
labels, true_labels, decimal=5
) # Check that the label values are correct
def test_datamodule_multiple_data_files(self):
# Test single CSV files
csv_file = "tests/data/micro_ZINC_shard_1.csv"
task_kwargs = {"df_path": csv_file, "split_val": 0.0, "split_test": 0.0}
task_specific_args = {
"task": {"task_level": "graph", "label_cols": ["score"], "smiles_col": "SMILES", **task_kwargs}
}
ds = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds.prepare_data()
ds.setup()
self.assertEqual(len(ds.train_ds), 10)
# Test multi CSV files
csv_file = "tests/data/micro_ZINC_shard_*.csv"
task_kwargs = {"df_path": csv_file, "split_val": 0.0, "split_test": 0.0}
task_specific_args = {
"task": {"task_level": "graph", "label_cols": ["score"], "smiles_col": "SMILES", **task_kwargs}
}
ds = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds.prepare_data()
ds.setup()
self.assertEqual(len(ds.train_ds), 20)
# Test single Parquet files
parquet_file = "tests/data/micro_ZINC_shard_1.parquet"
task_kwargs = {"df_path": parquet_file, "split_val": 0.0, "split_test": 0.0}
task_specific_args = {
"task": {"task_level": "graph", "label_cols": ["score"], "smiles_col": "SMILES", **task_kwargs}
}
ds = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds.prepare_data()
ds.setup()
self.assertEqual(len(ds.train_ds), 10)
# Test multi Parquet files
parquet_file = "tests/data/micro_ZINC_shard_*.parquet"
task_kwargs = {"df_path": parquet_file, "split_val": 0.0, "split_test": 0.0}
task_specific_args = {
"task": {"task_level": "graph", "label_cols": ["score"], "smiles_col": "SMILES", **task_kwargs}
}
ds = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds.prepare_data()
ds.setup()
self.assertEqual(len(ds.train_ds), 20)
def test_splits_file(self):
# Test single CSV files
csv_file = "tests/data/micro_ZINC_shard_1.csv"
df = pd.read_csv(csv_file)
# Split the CSV file with 80/10/10
train = 0.8
val = 0.1
indices = np.arange(len(df))
split_train = indices[: int(len(df) * train)]
split_val = indices[int(len(df) * train) : int(len(df) * (train + val))]
split_test = indices[int(len(df) * (train + val)) :]
splits = {"train": split_train, "val": split_val, "test": split_test}
# Test the splitting using `splits` directly as `splits_path`
task_kwargs = {
"df_path": csv_file,
"splits_path": splits,
"split_val": 0.0,
"split_test": 0.0,
}
task_specific_args = {
"task": {
"task_level": "graph",
"label_cols": ["score"],
"smiles_col": "SMILES",
**task_kwargs,
}
}
ds = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds.prepare_data(save_smiles_and_ids=True)
ds.setup(save_smiles_and_ids=True)
self.assertEqual(len(ds.train_ds), len(split_train))
self.assertEqual(len(ds.val_ds), len(split_val))
self.assertEqual(len(ds.test_ds), len(split_test))
# Create a TemporaryFile to save the splits, and test the datamodule
with tempfile.NamedTemporaryFile(suffix=".pt") as temp:
# Save the splits
torch.save(splits, temp)
# Test the datamodule
task_kwargs = {
"df_path": csv_file,
"splits_path": temp.name,
"split_val": 0.0,
"split_test": 0.0,
}
task_specific_args = {
"task": {
"task_level": "graph",
"label_cols": ["score"],
"smiles_col": "SMILES",
**task_kwargs,
}
}
ds2 = MultitaskFromSmilesDataModule(task_specific_args, featurization_n_jobs=0)
ds2.prepare_data(save_smiles_and_ids=True)
ds2.setup(save_smiles_and_ids=True)
self.assertEqual(len(ds2.train_ds), len(split_train))
self.assertEqual(len(ds2.val_ds), len(split_val))
self.assertEqual(len(ds2.test_ds), len(split_test))
# Check that the splits are the same
self.assertEqual(len(ds.train_ds.smiles), len(split_train))
np.testing.assert_array_equal(ds.train_ds.smiles, ds2.train_ds.smiles)
np.testing.assert_array_equal(ds.val_ds.smiles, ds2.val_ds.smiles)
np.testing.assert_array_equal(ds.test_ds.smiles, ds2.test_ds.smiles)
if __name__ == "__main__":
ut.main()
# Delete the cache
if exists(TEMP_CACHE_DATA_PATH):
rm(TEMP_CACHE_DATA_PATH, recursive=True)
================================================
FILE: tests/test_dataset.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import unittest as ut
from graphium.data import load_micro_zinc
from graphium.data.dataset import SingleTaskDataset, MultitaskDataset
from graphium.data.smiles_transform import smiles_to_unique_mol_ids
from graphium.data.utils import get_keys
class Test_Multitask_Dataset(ut.TestCase):
# Then we can choose different rows and columns for the tests as we see fit.
# Remember tests are supposed to be FAST, and reading from the file system multiple times slows things down.
# Make sure that the inputs to single task datasets are always lists!
# Do not pass a data frame itself, but turn it into a list to satisfy the type required.
def test_multitask_dataset_case_1(self):
"""Case: different tasks, all with the same smiles set.
- Check that for each task, all smiles are received from the initial DF.
- Check that for each task, you have the same label values as the initial DF.
"""
df_micro_zinc = load_micro_zinc() # Has about 1000 molecules
df = df_micro_zinc.iloc[0:4]
num_unique_mols = 4
# Here we take the microzinc dataset and split the labels up into 'SA', 'logp' and 'score' in order to simulate having multiple single-task datasets
df_micro_zinc_SA = df[["SMILES", "SA"]]
df_micro_zinc_logp = df[["SMILES", "logp"]]
df_micro_zinc_score = df[["SMILES", "score"]]
# We need to turn these dataframes into single-task datasets.
# We don't need to do featurization yet.
ds_micro_zinc_SA = SingleTaskDataset(
smiles=df_micro_zinc_SA.loc[:, "SMILES"].tolist(), labels=df_micro_zinc_SA.loc[:, "SA"].tolist()
)
ds_micro_zinc_logp = SingleTaskDataset(
smiles=df_micro_zinc_logp.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_logp.loc[:, "logp"].tolist(),
)
ds_micro_zinc_score = SingleTaskDataset(
smiles=df_micro_zinc_score.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_score.loc[:, "score"].tolist(),
)
# Create the multitask dataset
datasets_dict = {"SA": ds_micro_zinc_SA, "logp": ds_micro_zinc_logp, "score": ds_micro_zinc_score}
multitask_microzinc = MultitaskDataset(
datasets_dict, save_smiles_and_ids=True
) # Can optionally have features
# Check: The number of unique molecules equals the number of datapoints in the multitask dataset.
self.assertEqual(num_unique_mols, multitask_microzinc.__len__())
# Check that for each task, you have the same label values as the initial DF.
for idx in range(multitask_microzinc.__len__()):
smiles = df[["SMILES"]].iloc[idx].values[0]
# label = df[['SA']].iloc[idx]
label_SA = ds_micro_zinc_SA.labels[idx]
label_logp = ds_micro_zinc_logp.labels[idx]
label_score = ds_micro_zinc_score.labels[idx]
# Search for the mol id in the multitask dataset
mol_ids = smiles_to_unique_mol_ids([smiles])
mol_id = mol_ids[0]
found_idx = -1
for i, id in enumerate(multitask_microzinc.mol_ids):
if mol_id == id:
found_idx = i
# Compare labels
self.assertEqual(label_SA, multitask_microzinc.labels[found_idx]["SA"])
self.assertEqual(label_logp, multitask_microzinc.labels[found_idx]["logp"])
self.assertEqual(label_score, multitask_microzinc.labels[found_idx]["score"])
def test_multitask_dataset_case_2(self):
"""Case: Different tasks, but with no intersection in the smiles (each task has a unique set of smiles)
- Check that the total dataset has as much smiles as all tasks together
- Check that, for each task, only the smiles related to that task have values, and ensure the value is what's expected from the initial DF
"""
df = load_micro_zinc() # Has about 1000 molecules
# Choose non-overlapping smiles by choosing specific rows from the original dataframe.
df_rows_SA = df.iloc[0:200] # 200 data points
df_rows_logp = df.iloc[200:400] # 200 data points
df_rows_score = df.iloc[400:750] # 350 data points
total_data_points = 750
# Here we split the data according to the task we care about.
df_micro_zinc_SA = df_rows_SA[["SMILES", "SA"]]
df_micro_zinc_logp = df_rows_logp[["SMILES", "logp"]]
df_micro_zinc_score = df_rows_score[["SMILES", "score"]]
# We need to turn these dataframes into single-task datasets.
# We don't need to do featurization yet.
ds_micro_zinc_SA = SingleTaskDataset(
smiles=df_micro_zinc_SA.loc[:, "SMILES"].tolist(), labels=df_micro_zinc_SA.loc[:, "SA"].tolist()
)
ds_micro_zinc_logp = SingleTaskDataset(
smiles=df_micro_zinc_logp.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_logp.loc[:, "logp"].tolist(),
)
ds_micro_zinc_score = SingleTaskDataset(
smiles=df_micro_zinc_score.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_score.loc[:, "score"].tolist(),
)
# Create the multitask dataset
datasets_dict = {"SA": ds_micro_zinc_SA, "logp": ds_micro_zinc_logp, "score": ds_micro_zinc_score}
multitask_microzinc = MultitaskDataset(
datasets_dict, save_smiles_and_ids=True
) # Can optionally have features
# The total dataset has as many molecules as there are smiles in all tasks put together
self.assertEqual(total_data_points, multitask_microzinc.__len__())
# For each task, only the smiles related to that task have values, and the value is what's expected from the initial DF.
for idx in range(len(ds_micro_zinc_SA)):
smiles = df[["SMILES"]].iloc[idx].values[0]
task = "task"
if idx in range(0, 200):
task = "SA"
elif idx in range(200, 400):
task = "logp"
elif idx in range(400, 750):
task = "score"
# Labels of that molecule
label_SA = df[["SA"]].iloc[idx].values[0]
label_logp = df[["logp"]].iloc[idx].values[0]
label_score = df[["score"]].iloc[idx].values[0]
# Search for that molecule in the multitask dataset
mol_ids = smiles_to_unique_mol_ids([smiles])
mol_id = mol_ids[0]
found_idx = -1
for i, id in enumerate(multitask_microzinc.mol_ids):
if mol_id == id:
found_idx = i
multitask_microzinc_labels = get_keys(multitask_microzinc.labels[found_idx])
if task == "SA":
self.assertEqual(label_SA, multitask_microzinc.labels[found_idx]["SA"])
self.assertFalse("score" in multitask_microzinc_labels)
self.assertFalse("logp" in multitask_microzinc_labels)
elif task == "logp":
self.assertEqual(label_logp, multitask_microzinc.labels[found_idx]["logp"])
self.assertFalse("score" in multitask_microzinc_labels)
self.assertFalse("SA" in multitask_microzinc_labels)
elif task == "score":
self.assertEqual(label_score, multitask_microzinc.labels[found_idx]["score"])
self.assertFalse("SA" in multitask_microzinc_labels)
self.assertFalse("logp" in multitask_microzinc_labels)
def test_multitask_dataset_case_3(self):
"""Case: Different tasks, but with semi-intersection (some smiles unique per task, some intersect)
- Check that the total dataset has as much smiles as the unique number of smiles.
- Check that for each task, you retrieve the same smiles as expected from the initial DF
"""
df_micro_zinc = load_micro_zinc() # Has about 1000 molecules
df = df_micro_zinc.iloc[0:5]
# Choose OVERLAPPING smiles by choosing specific rows from the original dataframe. The tasks will not necessarily have unique smiles.
df_rows_SA = df.iloc[0:3]
df_rows_logp = df.iloc[1:4]
df_rows_score = df.iloc[3:5]
total_data_points = 5
# Here we split the data according to the task we care about.
df_micro_zinc_SA = df_rows_SA[["SMILES", "SA"]]
df_micro_zinc_logp = df_rows_logp[["SMILES", "logp"]]
df_micro_zinc_score = df_rows_score[["SMILES", "score"]]
# We need to turn these dataframes into single-task datasets.
# We don't need to do featurization yet.
ds_micro_zinc_SA = SingleTaskDataset(
smiles=df_micro_zinc_SA.loc[:, "SMILES"].tolist(), labels=df_micro_zinc_SA.loc[:, "SA"].tolist()
)
ds_micro_zinc_logp = SingleTaskDataset(
smiles=df_micro_zinc_logp.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_logp.loc[:, "logp"].tolist(),
)
ds_micro_zinc_score = SingleTaskDataset(
smiles=df_micro_zinc_score.loc[:, "SMILES"].tolist(),
labels=df_micro_zinc_score.loc[:, "score"].tolist(),
)
# Create the multitask dataset
datasets_dict = {"SA": ds_micro_zinc_SA, "logp": ds_micro_zinc_logp, "score": ds_micro_zinc_score}
multitask_microzinc = MultitaskDataset(
datasets_dict, save_smiles_and_ids=True
) # Can optionally have features
# The multitask dataset has as many molecules as there are unique smiles across the single task datasets.
self.assertEqual(total_data_points, multitask_microzinc.__len__())
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_ensemble_layers.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different layers of graphium/nn/ensemble_layers
"""
import numpy as np
import torch
from torch.nn import Linear
import unittest as ut
from graphium.nn.base_layers import FCLayer, MLP, MuReadoutGraphium
from graphium.nn.ensemble_layers import (
EnsembleLinear,
EnsembleFCLayer,
EnsembleMLP,
EnsembleMuReadoutGraphium,
)
from graphium.nn.architectures import FeedForwardNN, EnsembleFeedForwardNN
class test_Ensemble_Layers(ut.TestCase):
def check_ensemble_linear(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
use_mureadout=False,
):
msg = f"Testing EnsembleLinear with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
if use_mureadout:
# Create EnsembleMuReadoutGraphium instance
ensemble_linear = EnsembleMuReadoutGraphium(in_dim, out_dim, num_ensemble)
# Create equivalent separate Linear layers with synchronized weights and biases
linear_layers = [MuReadoutGraphium(in_dim, out_dim) for _ in range(num_ensemble)]
else:
# Create EnsembleLinear instance
ensemble_linear = EnsembleLinear(in_dim, out_dim, num_ensemble)
# Create equivalent separate Linear layers with synchronized weights and biases
linear_layers = [Linear(in_dim, out_dim) for _ in range(num_ensemble)]
for i, linear_layer in enumerate(linear_layers):
linear_layer.weight.data = ensemble_linear.weight.data[i]
if ensemble_linear.bias is not None:
linear_layer.bias.data = ensemble_linear.bias.data[i].squeeze()
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_linear(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (num_ensemble, batch_size, out_dim), msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, linear_layer in enumerate(linear_layers):
individual_output = linear_layer(input_tensor)
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output[i].detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
# Test with a sample input with the extra `num_ensemble` and `more_batch_dim` dimension
if more_batch_dim:
out_shape = (more_batch_dim, num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(more_batch_dim, num_ensemble, batch_size, in_dim)
else:
out_shape = (num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(num_ensemble, batch_size, in_dim)
ensemble_output = ensemble_linear(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, out_shape, msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, linear_layer in enumerate(linear_layers):
if more_batch_dim:
individual_output = linear_layer(input_tensor[:, i])
ensemble_output_i = ensemble_output[:, i]
else:
individual_output = linear_layer(input_tensor[i])
ensemble_output_i = ensemble_output[i]
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output_i.detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
def test_ensemble_linear(self):
# more_batch_dim=0
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=0)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=0)
# more_batch_dim=1
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=1)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=1)
# more_batch_dim=7
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=7)
self.check_ensemble_linear(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=7)
def test_ensemble_mureadout_graphium(self):
# Test `use_mureadout`
# more_batch_dim=0
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=0, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=0, use_mureadout=True
)
# more_batch_dim=1
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=1, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=1, use_mureadout=True
)
# more_batch_dim=7
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=7, use_mureadout=True
)
self.check_ensemble_linear(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=7, use_mureadout=True
)
def check_ensemble_fclayer(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
is_readout_layer=False,
):
msg = f"Testing EnsembleFCLayer with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
# Create EnsembleFCLayer instance
ensemble_fclayer = EnsembleFCLayer(in_dim, out_dim, num_ensemble, is_readout_layer=is_readout_layer)
# Create equivalent separate FCLayer layers with synchronized weights and biases
fc_layers = [FCLayer(in_dim, out_dim, is_readout_layer=is_readout_layer) for _ in range(num_ensemble)]
for i, fc_layer in enumerate(fc_layers):
fc_layer.linear.weight.data = ensemble_fclayer.linear.weight.data[i]
if ensemble_fclayer.bias is not None:
fc_layer.linear.bias.data = ensemble_fclayer.linear.bias.data[i].squeeze()
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_fclayer(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (num_ensemble, batch_size, out_dim), msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, fc_layer in enumerate(fc_layers):
individual_output = fc_layer(input_tensor)
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output[i].detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
# Test with a sample input with the extra `num_ensemble` and `more_batch_dim` dimension
if more_batch_dim:
out_shape = (more_batch_dim, num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(more_batch_dim, num_ensemble, batch_size, in_dim)
else:
out_shape = (num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(num_ensemble, batch_size, in_dim)
ensemble_output = ensemble_fclayer(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, out_shape, msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, fc_layer in enumerate(fc_layers):
if more_batch_dim:
individual_output = fc_layer(input_tensor[:, i])
ensemble_output_i = ensemble_output[:, i]
else:
individual_output = fc_layer(input_tensor[i])
ensemble_output_i = ensemble_output[i]
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output_i.detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
def test_ensemble_fclayer(self):
# more_batch_dim=0
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=0)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=0)
# more_batch_dim=1
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=1)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=1)
# more_batch_dim=7
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=7)
self.check_ensemble_fclayer(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=7)
# Test `is_readout_layer`
self.check_ensemble_fclayer(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, is_readout_layer=True
)
self.check_ensemble_fclayer(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, is_readout_layer=True
)
self.check_ensemble_fclayer(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, is_readout_layer=True
)
def check_ensemble_mlp(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
last_layer_is_readout=False,
):
msg = f"Testing EnsembleMLP with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
# Create EnsembleMLP instance
hidden_dims = [17, 17, 17]
ensemble_mlp = EnsembleMLP(
in_dim, hidden_dims, out_dim, num_ensemble, last_layer_is_readout=last_layer_is_readout
)
# Create equivalent separate MLP layers with synchronized weights and biases
mlps = [
MLP(in_dim, hidden_dims, out_dim, last_layer_is_readout=last_layer_is_readout)
for _ in range(num_ensemble)
]
for i, mlp in enumerate(mlps):
for j, layer in enumerate(mlp.fully_connected):
layer.linear.weight.data = ensemble_mlp.fully_connected[j].linear.weight.data[i]
if layer.bias is not None:
layer.linear.bias.data = ensemble_mlp.fully_connected[j].linear.bias.data[i].squeeze()
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (num_ensemble, batch_size, out_dim), msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, mlp in enumerate(mlps):
individual_output = mlp(input_tensor)
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output[i].detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
# Test with a sample input with the extra `num_ensemble` and `more_batch_dim` dimension
if more_batch_dim:
out_shape = (more_batch_dim, num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(more_batch_dim, num_ensemble, batch_size, in_dim)
else:
out_shape = (num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(num_ensemble, batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, out_shape, msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, mlp in enumerate(mlps):
if more_batch_dim:
individual_output = mlp(input_tensor[:, i])
ensemble_output_i = ensemble_output[:, i]
else:
individual_output = mlp(input_tensor[i])
ensemble_output_i = ensemble_output[i]
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output_i.detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
def test_ensemble_mlp(self):
# more_batch_dim=0
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=0)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=0)
# more_batch_dim=1
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=1)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=1)
# more_batch_dim=7
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=7)
self.check_ensemble_mlp(in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=7)
# Test `last_layer_is_readout`
self.check_ensemble_mlp(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, last_layer_is_readout=True
)
self.check_ensemble_mlp(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, last_layer_is_readout=True
)
self.check_ensemble_mlp(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, last_layer_is_readout=True
)
def check_ensemble_feedforwardnn(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
last_layer_is_readout=False,
):
msg = f"Testing EnsembleFeedForwardNN with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
# Create EnsembleFeedForwardNN instance
hidden_dims = [17, 17, 17]
ensemble_mlp = EnsembleFeedForwardNN(
in_dim,
out_dim,
hidden_dims,
num_ensemble,
reduction=None,
last_layer_is_readout=last_layer_is_readout,
)
# Create equivalent separate MLP layers with synchronized weights and biases
mlps = [
FeedForwardNN(in_dim, out_dim, hidden_dims, last_layer_is_readout=last_layer_is_readout)
for _ in range(num_ensemble)
]
for i, mlp in enumerate(mlps):
for j, layer in enumerate(mlp.layers):
layer.linear.weight.data = ensemble_mlp.layers[j].linear.weight.data[i]
if layer.bias is not None:
layer.linear.bias.data = ensemble_mlp.layers[j].linear.bias.data[i].squeeze()
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (num_ensemble, batch_size, out_dim), msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
individual_outputs = []
for i, mlp in enumerate(mlps):
individual_outputs.append(mlp(input_tensor))
individual_outputs = torch.stack(individual_outputs).detach().numpy()
for i, mlp in enumerate(mlps):
ensemble_output_i = ensemble_output[i].detach().numpy()
np.testing.assert_allclose(
ensemble_output_i, individual_outputs[..., i, :, :], atol=1e-5, err_msg=msg
)
# Test with a sample input with the extra `num_ensemble` and `more_batch_dim` dimension
if more_batch_dim:
out_shape = (more_batch_dim, num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(more_batch_dim, num_ensemble, batch_size, in_dim)
else:
out_shape = (num_ensemble, batch_size, out_dim)
input_tensor = torch.randn(num_ensemble, batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, out_shape, msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
for i, mlp in enumerate(mlps):
if more_batch_dim:
individual_output = mlp(input_tensor[:, i])
ensemble_output_i = ensemble_output[:, i]
else:
individual_output = mlp(input_tensor[i])
ensemble_output_i = ensemble_output[i]
individual_output = individual_output.detach().numpy()
ensemble_output_i = ensemble_output_i.detach().numpy()
np.testing.assert_allclose(ensemble_output_i, individual_output, atol=1e-5, err_msg=msg)
def check_ensemble_feedforwardnn_mean(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
last_layer_is_readout=False,
):
msg = f"Testing EnsembleFeedForwardNN with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
# Create EnsembleFeedForwardNN instance
hidden_dims = [17, 17, 17]
ensemble_mlp = EnsembleFeedForwardNN(
in_dim,
out_dim,
hidden_dims,
num_ensemble,
reduction="mean",
last_layer_is_readout=last_layer_is_readout,
)
# Create equivalent separate MLP layers with synchronized weights and biases
mlps = [
FeedForwardNN(in_dim, out_dim, hidden_dims, last_layer_is_readout=last_layer_is_readout)
for _ in range(num_ensemble)
]
for i, mlp in enumerate(mlps):
for j, layer in enumerate(mlp.layers):
layer.linear.weight.data = ensemble_mlp.layers[j].linear.weight.data[i]
if layer.bias is not None:
layer.linear.bias.data = ensemble_mlp.layers[j].linear.bias.data[i].squeeze()
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (batch_size, out_dim), msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
individual_outputs = []
for i, mlp in enumerate(mlps):
individual_outputs.append(mlp(input_tensor))
individual_outputs = torch.stack(individual_outputs, dim=-3)
individual_outputs = individual_outputs.mean(dim=-3).detach().numpy()
np.testing.assert_allclose(
ensemble_output.detach().numpy(), individual_outputs, atol=1e-5, err_msg=msg
)
# Test with a sample input with the extra `num_ensemble` and `more_batch_dim` dimension
if more_batch_dim:
out_shape = (more_batch_dim, batch_size, out_dim)
input_tensor = torch.randn(more_batch_dim, num_ensemble, batch_size, in_dim)
else:
out_shape = (batch_size, out_dim)
input_tensor = torch.randn(num_ensemble, batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor).detach()
# Check for the output shape
self.assertEqual(ensemble_output.shape, out_shape, msg=msg)
# Make sure that the outputs of the individual layers are the same as the ensemble output
individual_outputs = []
for i, mlp in enumerate(mlps):
if more_batch_dim:
individual_outputs.append(mlp(input_tensor[:, i]))
else:
individual_outputs.append(mlp(input_tensor[i]))
individual_output = torch.stack(individual_outputs, dim=-3).mean(dim=-3).detach().numpy()
np.testing.assert_allclose(ensemble_output, individual_output, atol=1e-5, err_msg=msg)
def check_ensemble_feedforwardnn_simple(
self,
in_dim: int,
out_dim: int,
num_ensemble: int,
batch_size: int,
more_batch_dim: int,
last_layer_is_readout=False,
**kwargs,
):
msg = f"Testing EnsembleFeedForwardNN with in_dim={in_dim}, out_dim={out_dim}, num_ensemble={num_ensemble}, batch_size={batch_size}, more_batch_dim={more_batch_dim}"
# Create EnsembleFeedForwardNN instance
hidden_dims = [17, 17, 17]
ensemble_mlp = EnsembleFeedForwardNN(
in_dim,
out_dim,
hidden_dims,
num_ensemble,
reduction=None,
last_layer_is_readout=last_layer_is_readout,
**kwargs,
)
# Test with a sample input
input_tensor = torch.randn(batch_size, in_dim)
ensemble_output = ensemble_mlp(input_tensor)
# Check for the output shape
self.assertEqual(ensemble_output.shape, (num_ensemble, batch_size, out_dim), msg=msg)
def test_ensemble_feedforwardnn(self):
# more_batch_dim=0
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=0
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=0
)
# more_batch_dim=1
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=1
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=1
)
# more_batch_dim=7
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=1, more_batch_dim=7
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=1, batch_size=13, more_batch_dim=7
)
# Test `last_layer_is_readout`
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, last_layer_is_readout=True
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, last_layer_is_readout=True
)
self.check_ensemble_feedforwardnn(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, last_layer_is_readout=True
)
# Test `reduction`
self.check_ensemble_feedforwardnn_mean(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, last_layer_is_readout=True
)
self.check_ensemble_feedforwardnn_mean(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, last_layer_is_readout=True
)
self.check_ensemble_feedforwardnn_mean(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, last_layer_is_readout=True
)
# Test `subset_in_dim`
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, subset_in_dim=0.5
)
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, subset_in_dim=0.5
)
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, subset_in_dim=0.5
)
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, subset_in_dim=7
)
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, subset_in_dim=7
)
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, subset_in_dim=7
)
with self.assertRaises(AssertionError):
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=0, subset_in_dim=1.5
)
with self.assertRaises(AssertionError):
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=1, subset_in_dim=39
)
with self.assertRaises(AssertionError):
self.check_ensemble_feedforwardnn_simple(
in_dim=11, out_dim=5, num_ensemble=3, batch_size=13, more_batch_dim=7, subset_in_dim=39
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_featurizer.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different datasets of graphium/features/featurizer.py
"""
import numpy as np
import unittest as ut
from copy import deepcopy
from rdkit import Chem
import datamol as dm
from graphium.features.featurizer import (
get_mol_atomic_features_onehot,
get_mol_atomic_features_float,
get_mol_edge_features,
mol_to_adj_and_features,
mol_to_pyggraph,
)
class test_featurizer(ut.TestCase):
smiles = [
"C",
"CC",
"C1(C[N]CCC1)=O",
"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",
"OCCc1c(C)[n+](cs1)Cc2cnc(C)nc2N",
"O1C=C[C@H]([C@H]1O2)c3c2cc(OC)c4c3OC(=O)C5=C4CCC(=O)5",
"CC1(C2C1C(N(C2)C(=O)C(C(C)=B)NC(=O)C(F)(F)F)C(=O)NC(C(C3CCNC3=O)[Cl])C#N)C",
]
smiles_noble = ["[He].[He]", "[He][He]", "[Kr][Kr]"]
atomic_onehot_props = [
"atomic-number",
"valence",
"degree",
"implicit-valence",
"hybridization",
"chirality",
"phase",
"type",
"group",
"period",
]
atomic_float_props = [
"atomic-number",
"mass",
"valence",
"implicit-valence",
"hybridization",
"chirality",
"aromatic",
"in-ring",
"min-ring",
"max-ring",
"num-ring",
"degree",
"radical-electron",
"formal-charge",
"vdw-radius",
"covalent-radius",
"electronegativity",
"ionization",
"melting-point",
"metal",
"single-bond",
"aromatic-bond",
"double-bond",
"triple-bond",
"is-carbon",
"group",
"period",
]
edge_props = [
"bond-type-onehot",
"bond-type-float",
"stereo",
"in-ring",
"conjugated",
"estimated-bond-length",
"conformer-bond-length",
]
def test_get_mol_atomic_features_onehot(self):
props = deepcopy(self.atomic_onehot_props)
bad_props = ["bob"]
all_smiles = self.smiles + self.smiles_noble
for s in all_smiles:
err_msg = f"\n\tError for params:\n\t\tSMILES: {s}"
mol = dm.to_mol(s)
for ii in range(len(props)):
this_props = props[:ii]
err_msg2 = err_msg + f"\n\t\tprops: {this_props}"
prop_dict = get_mol_atomic_features_onehot(mol, property_list=this_props)
self.assertListEqual(list(prop_dict.keys()), this_props, msg=err_msg)
for key, val in prop_dict.items():
err_msg3 = err_msg2 + f"\n\t\tkey: {key}"
self.assertEqual(val.shape[0], mol.GetNumAtoms(), msg=err_msg3)
self.assertGreater(val.shape[1], 1, msg=err_msg3)
self.assertTrue(np.all((val == 0) | (val == 1)), msg=err_msg3)
with self.assertRaises(ValueError, msg=err_msg):
get_mol_atomic_features_onehot(mol, property_list=bad_props)
def test_get_mol_atomic_features_float(self):
props = deepcopy(self.atomic_float_props)
bad_props = ["bob"]
all_smiles = self.smiles + self.smiles_noble
for s in all_smiles:
err_msg = f"\n\tError for params:\n\t\tSMILES: {s}"
mol = dm.to_mol(s)
for ii in range(len(props)):
this_props = props[:ii]
err_msg2 = err_msg + f"\n\t\tprops: {this_props}"
prop_dict = get_mol_atomic_features_float(mol, property_list=this_props, mask_nan=None)
self.assertListEqual(list(prop_dict.keys()), this_props, msg=err_msg)
for key, val in prop_dict.items():
err_msg3 = err_msg2 + f"\n\t\tkey: {key}"
self.assertListEqual(list(val.shape), [mol.GetNumAtoms()], msg=err_msg3)
with self.assertRaises(ValueError, msg=err_msg):
get_mol_atomic_features_float(mol, property_list=bad_props)
def test_get_mol_atomic_features_float_nan_mask(self):
for s in self.smiles_noble:
mol = dm.to_mol(s)
# Nothing happens when `mask_nan = None`, nans are still in the property array
prop_dict = get_mol_atomic_features_float(
mol, property_list=self.atomic_float_props, mask_nan=None
)
prop_array = np.concatenate(list(prop_dict.values()), axis=0)
nans = np.isnan(prop_array)
# Capture a raised error when `mask_nan = "raise"`
with self.assertRaises(ValueError):
prop_dict = get_mol_atomic_features_float(
mol, property_list=self.atomic_float_props, mask_nan="raise"
)
# Not sure how to Capture a logged warning when `mask_nan = "warn"`
# Here, I'm testing a behaviour similar to `mask_nan = None`
prop_dict = get_mol_atomic_features_float(
mol, property_list=self.atomic_float_props, mask_nan="warn"
)
prop_array = np.concatenate(list(prop_dict.values()), axis=0)
self.assertEqual(len(self.atomic_float_props), len(prop_dict))
self.assertTrue(any(np.isnan(prop_array)))
# NaNs are replaced by `42` when `mask_nan=42`
prop_dict = get_mol_atomic_features_float(mol, property_list=self.atomic_float_props, mask_nan=42)
prop_array = np.concatenate(list(prop_dict.values()), axis=0)
self.assertEqual(len(self.atomic_float_props), len(prop_dict))
self.assertFalse(any(np.isnan(prop_array)))
self.assertTrue(all(prop_array[nans] == 42))
def test_get_mol_edge_features(self):
props = deepcopy(self.edge_props)
bad_props = ["bob"]
all_smiles = self.smiles + self.smiles_noble
for s in all_smiles:
err_msg = f"\n\tError for params:\n\t\tSMILES: {s}"
mol = dm.to_mol(s)
for ii in range(len(props)):
this_props = props[: ii + 1]
err_msg2 = err_msg + f"\n\t\tprops: {this_props}"
prop_dict = get_mol_edge_features(mol, property_list=this_props)
self.assertListEqual(list(prop_dict.keys()), this_props, msg=err_msg)
for key, val in prop_dict.items():
err_msg3 = err_msg2 + f"\n\t\tkey: {key}"
self.assertEqual(val.shape[0], mol.GetNumBonds(), msg=err_msg3)
if mol.GetNumBonds() > 0:
with self.assertRaises(ValueError, msg=err_msg):
get_mol_edge_features(mol, property_list=bad_props)
def test_mol_to_adj_and_features(self):
np.random.seed(42)
for s in self.smiles:
err_msg = f"\n\tError for params:\n\t\tSMILES: {s}"
mol = dm.to_mol(s)
mol_Hs = Chem.AddHs(mol) # type: ignore
mol_No_Hs = Chem.RemoveHs(mol) # type: ignore
for explicit_H in [True, False]:
this_mol = mol_Hs if explicit_H else mol_No_Hs
for ii in np.arange(0, 5, 0.2):
num_props = int(round(ii))
err_msg2 = err_msg + f"\n\t\texplicit_H: {explicit_H}\n\t\tii: {ii}"
adj, ndata, edata, _, _ = mol_to_adj_and_features(
mol=mol,
atom_property_list_onehot=np.random.choice(
self.atomic_onehot_props, size=num_props, replace=False
),
atom_property_list_float=np.random.choice(
self.atomic_float_props, size=num_props, replace=False
),
edge_property_list=np.random.choice(self.edge_props, size=num_props, replace=False),
add_self_loop=False,
explicit_H=explicit_H,
use_bonds_weights=False,
)
self.assertEqual(adj.shape[0], this_mol.GetNumAtoms(), msg=err_msg2)
if num_props > 0:
self.assertEqual(ndata.shape[0], this_mol.GetNumAtoms(), msg=err_msg2)
if this_mol.GetNumBonds() > 0:
self.assertEqual(edata.shape[0], this_mol.GetNumBonds(), msg=err_msg2)
self.assertGreaterEqual(edata.shape[1], num_props, msg=err_msg2)
self.assertGreaterEqual(ndata.shape[1], num_props, msg=err_msg2)
def test_mol_to_pyggraph(self):
np.random.seed(42)
for s in self.smiles:
err_msg = f"\n\tError for params:\n\t\tSMILES: {s}"
mol = dm.to_mol(s)
mol_Hs = Chem.AddHs(mol) # type: ignore
mol_No_Hs = Chem.RemoveHs(mol) # type: ignore
graph = mol_to_pyggraph(
mol=mol,
atom_property_list_onehot=[],
atom_property_list_float=["atomic-number"],
edge_property_list=["bond-type-float"],
add_self_loop=False,
explicit_H=False,
use_bonds_weights=False,
on_error="raise",
)
# Check the number of nodes and edges
self.assertListEqual(list(graph["feat"].shape), [mol.GetNumAtoms(), 1], msg=err_msg)
self.assertListEqual(list(graph["edge_feat"].shape), [2 * mol.GetNumBonds(), 1], msg=err_msg)
# Check the node features
feat = graph["feat"].to_dense().numpy() * 5 + 6 # Undo the scaling
atom_nums = np.asarray([atom.GetAtomicNum() for atom in mol.GetAtoms()])
np.testing.assert_array_almost_equal(feat[:, 0], atom_nums, decimal=5, err_msg=err_msg)
# Check the edge features
edge_feat = graph["edge_feat"].to_dense().numpy()
bond_types = np.asarray([bond.GetBondTypeAsDouble() for bond in mol.GetBonds()]).repeat(2)
np.testing.assert_array_almost_equal(edge_feat[:, 0], bond_types, decimal=5, err_msg=err_msg)
# Check the edge indices
if mol.GetNumBonds() > 0:
edge_index = graph["edge_index"].to_dense().numpy()
true_edge_index = []
for bond in mol.GetBonds():
true_edge_index.append([bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()])
true_edge_index.append([bond.GetEndAtomIdx(), bond.GetBeginAtomIdx()])
true_edge_index = np.asarray(true_edge_index).T
np.testing.assert_array_equal(edge_index, true_edge_index, err_msg=err_msg)
# Loop over many possible combinations of properties
for explicit_H in [True, False]:
this_mol = mol_Hs if explicit_H else mol_No_Hs
for ii in np.arange(0, 5, 0.2):
num_props = int(round(ii))
err_msg2 = err_msg + f"\n\t\texplicit_H: {explicit_H}\n\t\tii: {ii}"
graph = mol_to_pyggraph(
mol=mol,
atom_property_list_onehot=np.random.choice(
self.atomic_onehot_props, size=num_props, replace=False
),
atom_property_list_float=np.random.choice(
self.atomic_float_props, size=num_props, replace=False
),
edge_property_list=np.random.choice(self.edge_props, size=num_props, replace=False),
add_self_loop=False,
explicit_H=explicit_H,
use_bonds_weights=False,
on_error="raise",
)
self.assertEqual(graph.num_nodes, this_mol.GetNumAtoms(), msg=err_msg2)
self.assertEqual(graph.num_edges, 2 * this_mol.GetNumBonds(), msg=err_msg2)
if num_props > 0:
ndata = graph["feat"]
edata = graph["edge_feat"]
self.assertEqual(ndata.shape[0], this_mol.GetNumAtoms(), msg=err_msg2)
self.assertEqual(edata.shape[0], 2 * this_mol.GetNumBonds(), msg=err_msg2)
self.assertGreaterEqual(ndata.shape[1], num_props, msg=err_msg2)
self.assertGreaterEqual(edata.shape[1], num_props, msg=err_msg2)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_finetuning.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import os
import unittest as ut
from copy import deepcopy
from os.path import abspath, dirname
import torch
from lightning.pytorch.callbacks import Callback
from omegaconf import OmegaConf
import graphium
from graphium.config._loader import (
load_accelerator,
load_architecture,
load_datamodule,
load_metrics,
load_predictor,
load_trainer,
save_params_to_wandb,
)
from graphium.finetuning import GraphFinetuning, modify_cfg_for_finetuning
from graphium.trainer import PredictorModule
MAIN_DIR = dirname(dirname(abspath(graphium.__file__)))
CONFIG_FILE = "graphium/config/dummy_finetuning.yaml"
os.chdir(MAIN_DIR)
class Test_Finetuning(ut.TestCase):
def test_finetuning_from_task_head(self):
# Skip test if PyTDC package not installed
try:
import tdc
except ImportError:
self.skipTest("PyTDC needs to be installed to run this test. Use `pip install PyTDC`.")
##################################################
### Test modification of config for finetuning ###
##################################################
cfg = graphium.load_config(name="dummy_finetuning_from_task_head")
cfg = OmegaConf.to_container(cfg, resolve=True)
cfg = modify_cfg_for_finetuning(cfg)
# Initialize the accelerator
cfg, accelerator_type = load_accelerator(cfg)
# Load and initialize the dataset
datamodule = load_datamodule(cfg, accelerator_type)
datamodule.task_specific_args["lipophilicity_astrazeneca"].sample_size = 100
# Initialize the network
model_class, model_kwargs = load_architecture(
cfg,
in_dims=datamodule.in_dims,
)
datamodule.prepare_data()
metrics = load_metrics(cfg)
predictor = load_predictor(
cfg,
model_class,
model_kwargs,
metrics,
datamodule.get_task_levels(),
accelerator_type,
datamodule.featurization,
datamodule.task_norms,
)
# Create module map
module_map = deepcopy(predictor.model.pretrained_model.net._module_map)
cfg_finetune = cfg["finetuning"]
finetuning_module = "".join([cfg_finetune["finetuning_module"], "-", cfg_finetune["task"]])
finetuning_module_from_pretrained = "".join(
[cfg_finetune["finetuning_module"], "-", cfg_finetune["sub_module_from_pretrained"]]
)
# Test for correctly modified shapes and number of layers in finetuning module
self.assertEqual(
len(module_map[finetuning_module]),
3,
)
self.assertEqual(module_map[finetuning_module][-1].linear.weight.size(0), 8)
self.assertEqual(predictor.model.finetuning_head.net.in_dim, 8)
self.assertEqual(len(predictor.model.finetuning_head.net.layers), 2)
self.assertEqual(predictor.model.finetuning_head.net.out_dim, 1)
################################################
### Test overwriting with pretrained weights ###
################################################
# Load pretrained & replace in predictor
pretrained_model = PredictorModule.load_pretrained_model(
cfg["finetuning"]["pretrained_model"], device="cpu"
).model
pretrained_model.create_module_map()
module_map_from_pretrained = deepcopy(pretrained_model._module_map)
# Finetuning module has only been partially overwritten
loaded_layers = module_map_from_pretrained[finetuning_module_from_pretrained]
overwritten_layers = module_map[finetuning_module]
for idx, (loaded, overwritten) in enumerate(zip(loaded_layers, overwritten_layers)):
if idx < 1:
assert torch.equal(loaded.linear.weight, overwritten.linear.weight)
assert torch.equal(loaded.linear.bias, overwritten.linear.bias)
else:
assert not torch.equal(loaded.linear.weight, overwritten.linear.weight)
assert not torch.equal(loaded.linear.bias, overwritten.linear.bias)
if idx + 1 == min(len(loaded_layers), len(overwritten_layers)):
break
for module_name in module_map.keys():
if module_name == finetuning_module:
break
loaded_module, overwritten_module = (
module_map_from_pretrained[module_name],
module_map[module_name],
)
for loaded_params, overwritten_params in zip(
loaded_module.parameters(), overwritten_module.parameters()
):
assert torch.equal(loaded_params.data, overwritten_params.data)
#################################################
### Test correct (un)freezing during training ###
#################################################
# Define test callback that checks for correct (un)freezing
class TestCallback(Callback):
def __init__(self, cfg):
super().__init__()
self.cfg_finetune = cfg["finetuning"]
def on_train_epoch_start(self, trainer, pl_module):
module_map = pl_module.model.pretrained_model.net._module_map
finetuning_module = "".join(
[self.cfg_finetune["finetuning_module"], "-", self.cfg_finetune["task"]]
)
training_depth = self.cfg_finetune["added_depth"] + self.cfg_finetune.pop(
"unfreeze_pretrained_depth", 0
)
frozen_parameters, unfrozen_parameters = [], []
if trainer.current_epoch == 0:
frozen = True
for module_name, module in module_map.items():
if module_name == finetuning_module:
# After the finetuning module, all parameters are unfrozen
frozen = False
frozen_parameters.extend(
[
parameter.requires_grad
for parameter in module[:-training_depth].parameters()
]
)
unfrozen_parameters.extend(
[
parameter.requires_grad
for parameter in module[-training_depth:].parameters()
]
)
continue
if frozen:
frozen_parameters.extend(
[parameter.requires_grad for parameter in module.parameters()]
)
else:
unfrozen_parameters.extend(
[parameter.requires_grad for parameter in module.parameters()]
)
# Finetuning head is always unfrozen
unfrozen_parameters.extend(
[
parameter.requires_grad
for parameter in pl_module.model.finetuning_head.parameters()
]
)
assert not True in frozen_parameters
assert not False in unfrozen_parameters
if trainer.current_epoch == 2:
# All parameter are unfrozen starting from epoch_unfreeze_all
unfrozen_parameters = [
parameter.requires_grad for parameter in pl_module.model.parameters()
]
assert not False in unfrozen_parameters
trainer = load_trainer(cfg, accelerator_type)
finetuning_training_kwargs = cfg["finetuning"]["training_kwargs"]
trainer.callbacks.append(GraphFinetuning(**finetuning_training_kwargs))
# Add test callback to trainer
trainer.callbacks.append(TestCallback(cfg))
predictor.set_max_nodes_edges_per_graph(datamodule, stages=["train", "val"])
# Run the model training
trainer.fit(model=predictor, datamodule=datamodule)
def test_finetuning_from_gnn(self):
# Skip test if PyTDC package not installed
try:
import tdc
except ImportError:
self.skipTest("PyTDC needs to be installed to run this test. Use `pip install PyTDC`.")
##################################################
### Test modification of config for finetuning ###
##################################################
cfg = graphium.load_config(name="dummy_finetuning_from_gnn")
cfg = OmegaConf.to_container(cfg, resolve=True)
cfg = modify_cfg_for_finetuning(cfg)
# Initialize the accelerator
cfg, accelerator_type = load_accelerator(cfg)
# Load and initialize the dataset
datamodule = load_datamodule(cfg, accelerator_type)
datamodule.task_specific_args["lipophilicity_astrazeneca"].sample_size = 100
# Initialize the network
model_class, model_kwargs = load_architecture(
cfg,
in_dims=datamodule.in_dims,
)
datamodule.prepare_data()
metrics = load_metrics(cfg)
predictor = load_predictor(
cfg,
model_class,
model_kwargs,
metrics,
datamodule.get_task_levels(),
accelerator_type,
datamodule.featurization,
datamodule.task_norms,
)
# Create module map
module_map = deepcopy(predictor.model.pretrained_model.net._module_map)
cfg_finetune = cfg["finetuning"]
finetuning_module = cfg_finetune["finetuning_module"]
# Test for correctly modified shapes and number of layers in finetuning module
self.assertEqual(
len(module_map[finetuning_module]),
5,
)
self.assertEqual(module_map[finetuning_module][-1].model.lin.weight.size(0), 96)
self.assertEqual(len(module_map["graph_output_nn-graph"]), 2)
assert predictor.model.pretrained_model.net.task_heads.graph_output_nn[
"graph"
].graph_output_nn_kwargs["graph"]["pooling"] == ["mean"]
################################################
### Test overwriting with pretrained weights ###
################################################
# Load pretrained & replace in predictor
pretrained_model = PredictorModule.load_pretrained_model(
cfg["finetuning"]["pretrained_model"], device="cpu"
).model
pretrained_model.create_module_map()
module_map_from_pretrained = deepcopy(pretrained_model._module_map)
# Finetuning module has only been partially overwritten
loaded_layers = module_map_from_pretrained[finetuning_module]
overwritten_layers = module_map[finetuning_module]
for idx, (loaded, overwritten) in enumerate(zip(loaded_layers, overwritten_layers)):
if idx < 2:
assert torch.equal(loaded.model.lin.weight, overwritten.model.lin.weight)
else:
assert not torch.equal(loaded.model.lin.weight, overwritten.model.lin.weight)
if idx + 1 == min(len(loaded_layers), len(overwritten_layers)):
break
for module_name in module_map.keys():
if module_name == finetuning_module:
break
loaded_module, overwritten_module = (
module_map_from_pretrained[module_name],
module_map[module_name],
)
for loaded_params, overwritten_params in zip(
loaded_module.parameters(), overwritten_module.parameters()
):
assert torch.equal(loaded_params.data, overwritten_params.data)
#################################################
### Test correct (un)freezing during training ###
#################################################
# Define test callback that checks for correct (un)freezing
class TestCallback(Callback):
def __init__(self, cfg):
super().__init__()
self.cfg_finetune = cfg["finetuning"]
def on_train_epoch_start(self, trainer, pl_module):
module_map = pl_module.model.pretrained_model.net._module_map
training_depth = self.cfg_finetune["added_depth"] + self.cfg_finetune.pop(
"unfreeze_pretrained_depth", 0
)
frozen_parameters, unfrozen_parameters = [], []
if trainer.current_epoch == 0:
frozen = True
for module_name, module in module_map.items():
if module_name == finetuning_module:
# After the finetuning module, all parameters are unfrozen
frozen = False
frozen_parameters.extend(
[
parameter.requires_grad
for parameter in module[:-training_depth].parameters()
]
)
unfrozen_parameters.extend(
[
parameter.requires_grad
for parameter in module[-training_depth:].parameters()
]
)
continue
if frozen:
frozen_parameters.extend(
[parameter.requires_grad for parameter in module.parameters()]
)
else:
unfrozen_parameters.extend(
[parameter.requires_grad for parameter in module.parameters()]
)
assert not True in frozen_parameters
assert not False in unfrozen_parameters
if trainer.current_epoch == 1:
# All parameter are unfrozen starting from epoch_unfreeze_all
unfrozen_parameters = [
parameter.requires_grad for parameter in pl_module.model.parameters()
]
assert not False in unfrozen_parameters
trainer = load_trainer(cfg, accelerator_type)
finetuning_training_kwargs = cfg["finetuning"]["training_kwargs"]
trainer.callbacks.append(GraphFinetuning(**finetuning_training_kwargs))
# Add test callback to trainer
trainer.callbacks.append(TestCallback(cfg))
predictor.set_max_nodes_edges_per_graph(datamodule, stages=["train", "val"])
# Run the model training
trainer.fit(model=predictor, datamodule=datamodule)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_ipu_dataloader.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
# General imports
import yaml
import unittest as ut
import numpy as np
from copy import deepcopy
from warnings import warn
from unittest.mock import patch
from lightning import Trainer, LightningModule
from functools import partial
import pytest
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import torch
from torch.utils.data.dataloader import default_collate
from lightning_graphcore import IPUStrategy
def random_packing(num_nodes, batch_size):
ipu_batch_size = int(len(num_nodes) / batch_size)
indices = np.arange(len(num_nodes))
np.random.shuffle(indices)
indices = np.reshape(indices, (ipu_batch_size, batch_size)).tolist()
return indices
def global_batch_collator(batch_size, batches):
packs = []
for pack_idx in range(0, len(batches), batch_size):
packs.append(default_collate(batches[pack_idx : pack_idx + batch_size]))
global_batch = default_collate(packs)
global_batch = (global_batch[0], tuple(global_batch[1]))
return global_batch
@pytest.mark.ipu
class test_DataLoading(ut.TestCase):
class TestSimpleLightning(LightningModule):
# Create a basic Ligthning for testing the batch sizes
def __init__(self, batch_size, node_feat_size, edge_feat_size, num_batch) -> None:
super().__init__()
self.batch_size = batch_size
self.node_feat_size = node_feat_size
self.edge_feat_size = edge_feat_size
self.layer = torch.nn.Linear(node_feat_size, 1)
self.loss_fn = torch.nn.L1Loss()
self.num_batch = num_batch
def validation_step(self, batch, batch_idx):
self.assert_shapes(batch, batch_idx, "val")
loss = self.forward(batch)
return loss
def training_step(self, batch, batch_idx):
self.assert_shapes(batch, batch_idx, "train")
loss = self.forward(batch)
return loss
def forward(self, batch):
out = self.layer(batch[1][0]).squeeze(-1)
loss = self.loss_fn(out, batch[0])
return loss
def assert_shapes(self, batch, batch_idx, step):
# Test the shape of the labels
this_shape = list(batch[0].shape)
true_shape = [1, self.batch_size]
assert (
this_shape == true_shape
), f"Shape of the labels is `{this_shape}` but should be {true_shape}"
# Test the shape of the first feature
this_shape = list(batch[1][0].shape)
true_shape = [1, self.batch_size, self.node_feat_size]
assert (
this_shape == true_shape
), f"Shape of the feature 0 is `{this_shape}` but should be {true_shape}"
# Test the shape of the second feature
this_shape = list(batch[1][1].shape)
true_shape = [1, self.batch_size, self.edge_feat_size]
assert (
this_shape == true_shape
), f"Shape of the feature 0 is `{this_shape}` but should be {true_shape}"
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-3)
class TestDataset(torch.utils.data.Dataset):
# Create a simple dataset for testing the Lightning integration
def __init__(self, labels, node_features, edge_features):
self.labels = labels
self.node_features = node_features
self.edge_features = edge_features
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
# [label, [feat1, feat2]]
return [self.labels[idx], [self.node_features[idx], self.edge_features[idx]]]
# @pytest.mark.skip
def test_poptorch_simple_deviceiterations_gradient_accumulation(self):
"""
Test a simple version of the device-iterations and gradient accumulation
to make sure that the dataloader and models handle them correcly.
"""
with patch("poptorch.ipuHardwareIsAvailable", return_value=True):
with patch("lightning_graphcore.accelerator._IPU_AVAILABLE", new=True):
import poptorch
assert poptorch.ipuHardwareIsAvailable()
from lightning_graphcore.accelerator import _IPU_AVAILABLE
assert _IPU_AVAILABLE is True
# Initialize constants
gradient_accumulation = 2
device_iterations = 3
batch_size = 5
num_replicate = 7
node_feat_size = 11
edge_feat_size = 13
# Initialize the batch info and poptorch options
opts = poptorch.Options()
opts.useIpuModel(True)
opts.deviceIterations(device_iterations)
training_opts = deepcopy(opts)
training_opts.Training.gradientAccumulation(gradient_accumulation)
inference_opts = deepcopy(opts)
# Initialize the dataset
num_batch = device_iterations * gradient_accumulation * num_replicate
data_size = num_batch * batch_size
dataset = self.TestDataset(
labels=np.random.rand(data_size).astype(np.float32),
node_features=[
np.random.rand(node_feat_size).astype(np.float32) for ii in range(data_size)
],
edge_features=[
np.random.rand(edge_feat_size).astype(np.float32) for ii in range(data_size)
],
)
# Initialize the dataloader
train_dataloader = poptorch.DataLoader(
options=training_opts,
dataset=deepcopy(dataset),
batch_size=batch_size,
collate_fn=partial(global_batch_collator, batch_size),
)
val_dataloader = poptorch.DataLoader(
options=inference_opts,
dataset=deepcopy(dataset),
batch_size=batch_size,
collate_fn=partial(global_batch_collator, batch_size),
)
# Build the model, and run it on "IPU"
model = self.TestSimpleLightning(batch_size, node_feat_size, edge_feat_size, num_batch)
strategy = IPUStrategy(
training_opts=training_opts, inference_opts=inference_opts, autoreport=True
)
trainer = Trainer(
logger=True,
enable_checkpointing=False,
max_epochs=2,
strategy=strategy,
num_sanity_val_steps=0,
accelerator="ipu",
devices=1,
)
trainer.fit(model=model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)
@pytest.mark.skip
def test_poptorch_graphium_deviceiterations_gradient_accumulation_full(self):
"""
Test the device-iterations and gradient accumulation in a way
that is very similar to the Graphium code
to make sure that the dataloader and models handle them correcly.
"""
with patch("poptorch.ipuHardwareIsAvailable", return_value=True):
with patch("lightning_graphcore.accelerator._IPU_AVAILABLE", new=True):
try:
import poptorch
except Exception as e:
warn(f"Skipping this test because poptorch is not available.\n{e}")
return
from lightning_graphcore import IPUStrategy
import lightning_graphcore
# Current library imports
from graphium.config._loader import (
load_datamodule,
load_metrics,
load_architecture,
load_accelerator,
load_predictor,
load_trainer,
)
from graphium.utils.safe_run import SafeRun
# Simplified testing config - reflecting the toymix requirements
CONFIG_FILE = "tests/config_test_ipu_dataloader_multitask.yaml"
with open(CONFIG_FILE, "r") as f:
cfg = yaml.safe_load(f)
cfg, accelerator = load_accelerator(cfg)
# Load the datamodule, and prepare the data
datamodule = load_datamodule(cfg, accelerator_type=accelerator)
datamodule.prepare_data()
metrics = load_metrics(cfg)
model_class, model_kwargs = load_architecture(cfg, in_dims=datamodule.in_dims)
# datamodule.setup()
predictor = load_predictor(
cfg,
model_class,
model_kwargs,
metrics,
datamodule.get_task_levels(),
accelerator,
datamodule.featurization,
datamodule.task_norms,
)
assert poptorch.ipuHardwareIsAvailable()
trainer = load_trainer(cfg, "test", accelerator, "date_time_suffix")
# Run the model training
with SafeRun(
name="TRAINING", raise_error=cfg["constants"]["raise_train_error"], verbose=True
):
trainer.fit(model=predictor, datamodule=datamodule)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_ipu_losses.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import unittest as ut
import torch
from torch.nn import BCELoss, MSELoss, L1Loss, BCEWithLogitsLoss
from copy import deepcopy
import pytest
from graphium.ipu.ipu_losses import BCELossIPU, MSELossIPU, L1LossIPU, BCEWithLogitsLossIPU, HybridCELossIPU
from graphium.trainer.losses import HybridCELoss
@pytest.mark.ipu
class test_Losses(ut.TestCase):
torch.manual_seed(42)
preds = torch.rand((100, 10), dtype=torch.float32)
target = torch.rand((100, 10), dtype=torch.float32)
th = 0.7
nan_th = 0.2
preds_greater = preds > th
target_greater = (target > th).to(torch.float32)
target_greater_nan = deepcopy(target_greater)
is_nan = target < nan_th
target_greater_nan[target < nan_th] = torch.nan
target_nan = deepcopy(target)
target_nan[target < nan_th] = torch.nan
def test_bce(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target_greater)
target_nan = deepcopy(self.target_greater_nan)
# Regular loss
loss_true = BCELoss()(preds, target)
loss_ipu = BCELossIPU()(preds, target)
self.assertFalse(loss_true.isnan(), "Regular BCELoss is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular BCELoss is different"
)
# Weighted loss
weight = torch.rand(preds.shape[1], dtype=torch.float32)
loss_true = BCELoss(weight=weight)(preds, target)
loss_ipu = BCELossIPU(weight=weight)(preds, target)
self.assertFalse(loss_true.isnan(), "Regular BCELoss is NaN")
self.assertAlmostEqual(loss_true.item(), loss_ipu.item(), msg="Weighted BCELoss is different")
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = BCELoss()(preds[not_nan], target[not_nan])
loss_ipu = BCELossIPU()(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Regular BCELoss with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular BCELossIPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular BCELoss with NaN is different"
)
# Weighted loss with NaNs in target
not_nan = ~target_nan.isnan()
weight = torch.rand(preds.shape, dtype=torch.float32)
loss_true = BCELoss(weight=weight[not_nan])(preds[not_nan], target_nan[not_nan])
loss_ipu = BCELossIPU(weight=weight)(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Weighted BCELoss with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Weighted BCELossIPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Weighted BCELoss with NaN is different"
)
def test_mse(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target)
target_nan = deepcopy(self.target_nan)
# Regular loss
loss_true = MSELoss()(preds, target)
loss_ipu = MSELossIPU()(preds, target)
self.assertFalse(loss_true.isnan(), "Regular MSELoss is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular MSELoss is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = MSELoss()(preds[not_nan], target[not_nan])
loss_ipu = MSELossIPU()(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Regular MSELoss with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular MSELossIPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular MSELoss with NaN is different"
)
def test_l1(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target)
target_nan = deepcopy(self.target_nan)
# Regular loss
loss_true = L1Loss()(preds, target)
loss_ipu = L1LossIPU()(preds, target)
self.assertFalse(loss_true.isnan(), "Regular MAELoss is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular MAELoss is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = L1Loss()(preds[not_nan], target[not_nan])
loss_ipu = L1LossIPU()(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Regular MAELoss with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular MAELossIPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular MAELoss with NaN is different"
)
def test_bce_logits(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target_greater)
target_nan = deepcopy(self.target_greater_nan)
# Regular loss
loss_true = BCEWithLogitsLoss()(preds, target)
loss_ipu = BCEWithLogitsLossIPU()(preds, target)
self.assertFalse(loss_true.isnan(), "Regular BCEWithLogitsLoss is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular BCEWithLogitsLoss is different"
)
# Weighted loss
weight = torch.rand(preds.shape[1], dtype=torch.float32)
loss_true = BCEWithLogitsLoss(weight=weight)(preds, target)
loss_ipu = BCEWithLogitsLossIPU(weight=weight)(preds, target)
self.assertFalse(loss_true.isnan(), "Regular BCEWithLogitsLoss is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), msg="Weighted BCEWithLogitsLoss is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = BCEWithLogitsLoss()(preds[not_nan], target[not_nan])
loss_ipu = BCEWithLogitsLossIPU()(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Regular test_bce_logits with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular test_bce_logits with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular BCELoss with NaN is different"
)
# Weighted loss with NaNs in target
not_nan = ~target_nan.isnan()
weight = torch.rand(preds.shape, dtype=torch.float32)
loss_true = BCEWithLogitsLoss(weight=weight[not_nan])(preds[not_nan], target_nan[not_nan])
loss_ipu = BCEWithLogitsLossIPU(weight=weight)(preds, target_nan)
self.assertFalse(loss_true.isnan(), "Weighted test_bce_logits with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Weighted test_bce_logits with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(),
loss_ipu.item(),
places=6,
msg="Weighted BCEWithLogitsLoss with NaN is different",
)
================================================
FILE: tests/test_ipu_metrics.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import unittest as ut
import torch
from torchmetrics.functional import (
auroc,
average_precision,
precision,
accuracy,
recall,
pearson_corrcoef,
spearman_corrcoef,
r2_score,
f1_score,
fbeta_score,
mean_squared_error,
mean_absolute_error,
)
from copy import deepcopy
import pytest
from graphium.ipu.ipu_metrics import (
auroc_ipu,
average_precision_ipu,
precision_ipu,
accuracy_ipu,
recall_ipu,
pearson_ipu,
spearman_ipu,
r2_score_ipu,
f1_score_ipu,
fbeta_score_ipu,
mean_squared_error_ipu,
mean_absolute_error_ipu,
)
@pytest.mark.ipu
class test_Metrics(ut.TestCase):
torch.manual_seed(42)
preds = torch.rand((100, 10), dtype=torch.float32)
target = torch.rand((100, 10), dtype=torch.float32)
th = 0.7
nan_th = 0.2
preds_greater = preds > th
target_greater = (target > th).to(torch.float32)
target_greater_nan = deepcopy(target_greater)
is_nan = target < nan_th
target_greater_nan[target < nan_th] = torch.nan
target_nan = deepcopy(target)
target_nan[target < nan_th] = torch.nan
def test_auroc(self):
preds = deepcopy(self.preds)[:, 0]
target = deepcopy(self.target)[:, 0]
target_nan = deepcopy(self.target_nan)[:, 0]
target[target < 0.5] = 0
target[target >= 0.5] = 1
target_nan[target_nan < 0.5] = 0
target_nan[target_nan >= 0.5] = 1
# Regular loss
score_true = auroc(preds, target.to(int))
score_ipu = auroc_ipu(preds, target)
self.assertFalse(score_true.isnan(), "Regular AUROC score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Regular AUROC score is different"
)
# Weighted loss (As in BCE)
sample_weights = torch.rand(preds.shape[0], dtype=torch.float32)
score_true = auroc(preds, target.to(int), sample_weights=sample_weights)
score_ipu = auroc_ipu(preds, target, sample_weights=sample_weights)
self.assertFalse(score_true.isnan(), "Regular AUROC score is NaN")
self.assertAlmostEqual(score_true.item(), score_ipu.item(), msg="Weighted AUROC score is different")
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
score_true = auroc(preds[not_nan], target[not_nan].to(int))
score_ipu = auroc_ipu(preds, target_nan)
self.assertFalse(score_true.isnan(), "Regular AUROC score with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Regular AUROCIPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Regular AUROC score with NaN is different"
)
# Weighted loss with NaNs in target (As in BCE)
not_nan = ~target_nan.isnan()
sample_weights = torch.rand(preds.shape, dtype=torch.float32)
loss_true = auroc(preds[not_nan], target_nan[not_nan].to(int), sample_weights=sample_weights[not_nan])
loss_ipu = auroc_ipu(preds, target_nan, sample_weights=sample_weights)
self.assertFalse(loss_true.isnan(), "Weighted AUROC score with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Weighted AUROC IPU score with target_nan is NaN")
self.assertAlmostEqual(
# AssertionError: 0.6603766679763794 != 0.6234951615333557 within 2 places
loss_true.item(),
loss_ipu.item(),
places=6,
msg="Weighted AUROC with NaN is different",
)
def test_average_precision(self): # TODO: Make work with multi-class
preds = deepcopy(self.preds)[:, 0]
target = deepcopy(self.target)[:, 0]
target_nan = deepcopy(self.target_nan)[:, 0]
target[target < 0.5] = 0
target[target >= 0.5] = 1
target_nan[target_nan < 0.5] = 0
target_nan[target_nan >= 0.5] = 1
# Regular loss
score_true = average_precision(preds, target.to(int), task="binary")
score_ipu = average_precision_ipu(preds, target.to(int), task="binary")
self.assertFalse(score_true.isnan(), "Regular Average Precision is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Regular Average Precision is different"
)
# Regular average precision with NaNs in target
not_nan = ~target_nan.isnan()
score_true = average_precision(preds[not_nan], target[not_nan].to(int), task="binary")
score_ipu = average_precision_ipu(preds, target_nan, task="binary")
self.assertFalse(score_true.isnan(), "Regular Average Precision with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Regular Average Precision IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Regular Average Precision with NaN is different",
)
def test_precision(self):
preds = deepcopy(self.preds)[:, :4]
target = deepcopy(self.target)[:, 0]
t = deepcopy(target)
target[t < 0.4] = 0
target[(t >= 0.4) & (t < 0.6)] = 1
target[(t >= 0.6) & (t < 0.8)] = 2
target[(t >= 0.8)] = 3
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
target_nan_bin = deepcopy(target_nan)
target_nan_bin[target_nan > 0] = 1
# Micro precision binary
score_true = precision(preds[:, 0], target.to(int) > 0, average="micro")
score_ipu = precision_ipu(preds[:, 0], target > 0, average="micro")
self.assertFalse(score_true.isnan(), "Micro Precision binary is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Precision binary is different"
)
# Micro precision binary with NaNs in target
not_nan = ~target_nan.isnan()
score_true = precision(preds[:, 0][not_nan], target_nan_bin[not_nan].to(int), average="micro")
score_ipu = precision_ipu(preds[:, 0], target_nan_bin, average="micro")
self.assertFalse(score_true.isnan(), "Micro Precision binary with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Precision binary IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Precision with NaN is different"
)
# Micro precision
score_true = precision(preds, target.to(int), average="micro")
score_ipu = precision_ipu(preds, target, average="micro")
self.assertFalse(score_true.isnan(), "Micro Precision is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Precision is different"
)
# Micro precision with NaNs in target
not_nan = ~target_nan.isnan()
score_true = precision(preds[not_nan], target[not_nan].to(int), average="micro")
score_ipu = precision_ipu(preds, target_nan, average="micro")
self.assertFalse(score_true.isnan(), "Micro Precision with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Precision IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Precision with NaN is different"
)
# Macro precision
score_true = precision(preds, target.to(int), average="macro", num_classes=4)
score_ipu = precision_ipu(preds, target, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Precision is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro Precision is different"
)
# Macro precision multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = precision(preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4)
score_ipu = precision_ipu(preds, target_nan, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Precision multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro Precision multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Macro Precision multiclass with NaN is different",
)
# Macro precision multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = precision(preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4)
score_ipu = precision_ipu(preds, target_nan, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Precision multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro Precision multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Macro Precision multiclass with NaN is different",
)
# Weighted precision multiclass
score_true = precision(preds, target.to(int), average="weighted", num_classes=4)
score_ipu = precision_ipu(preds, target, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Precision multiclass is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Weighted Precision multiclass is different"
)
# Weighted precision multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = precision(preds[not_nan], target[not_nan].to(int), average="weighted", num_classes=4)
score_ipu = precision_ipu(preds, target_nan, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Precision multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Weighted Precision multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Regular Average Precision multiclass with NaN is different",
)
def test_accuracy(self):
preds = deepcopy(self.preds)[:, :4]
target = deepcopy(self.target)[:, 0]
t = deepcopy(target)
target[t < 0.4] = 0
target[(t >= 0.4) & (t < 0.6)] = 1
target[(t >= 0.6) & (t < 0.8)] = 2
target[(t >= 0.8)] = 3
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
target_nan_bin = deepcopy(target_nan)
target_nan_bin[target_nan > 0] = 1
# Micro accuracy binary
score_true = accuracy(preds[:, 0], target.to(int) > 0, average="micro")
score_ipu = accuracy_ipu(preds[:, 0], target > 0, average="micro")
self.assertFalse(score_true.isnan(), "Micro Accuracy binary is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Accuracy binary is different"
)
# Micro accuracy binary with NaNs in target
not_nan = ~target_nan.isnan()
score_true = accuracy(preds[:, 0][not_nan], target_nan_bin[not_nan].to(int), average="micro")
score_ipu = accuracy_ipu(preds[:, 0], target_nan_bin, average="micro")
self.assertFalse(score_true.isnan(), "Micro Accuracy binary with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Accuracy binary IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Accuracy with NaN is different"
)
# Micro accuracy
score_true = accuracy(preds, target.to(int), average="micro")
score_ipu = accuracy_ipu(preds, target, average="micro")
self.assertFalse(score_true.isnan(), "Micro Accuracy is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Accuracy is different"
)
# Micro accuracy with NaNs in target
not_nan = ~target_nan.isnan()
score_true = accuracy(preds[not_nan], target[not_nan].to(int), average="micro")
score_ipu = accuracy_ipu(preds, target_nan, average="micro")
self.assertFalse(score_true.isnan(), "Micro Accuracy with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Accuracy IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Accuracy with NaN is different"
)
# Macro accuracy
score_true = accuracy(preds, target.to(int), average="macro", num_classes=4)
score_ipu = accuracy_ipu(preds, target, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Accuracy is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro Accuracy is different"
)
# Macro accuracy with NaNs in target
not_nan = ~target_nan.isnan()
score_true = accuracy(preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4)
score_ipu = accuracy_ipu(preds, target_nan, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Accuracy with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro Accuracy IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro Accuracy with NaN is different"
)
# Weighted accuracy
score_true = accuracy(preds, target.to(int), average="weighted", num_classes=4)
score_ipu = accuracy_ipu(preds, target, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Accuracy is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Weighted Accuracy is different"
)
# Weighted accuracy with NaNs in target
not_nan = ~target_nan.isnan()
score_true = accuracy(preds[not_nan], target[not_nan].to(int), average="weighted", num_classes=4)
score_ipu = accuracy_ipu(preds, target_nan, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Accuracy with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Weighted Accuracy IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Regular Accuracy with NaN is different"
)
def test_recall(self):
preds = deepcopy(self.preds)[:, :4]
target = deepcopy(self.target)[:, 0]
t = deepcopy(target)
target[t < 0.4] = 0
target[(t >= 0.4) & (t < 0.6)] = 1
target[(t >= 0.6) & (t < 0.8)] = 2
target[(t >= 0.8)] = 3
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
target_nan_bin = deepcopy(target_nan)
target_nan_bin[target_nan > 0] = 1
# Micro recall binary
score_true = recall(preds[:, 0], target.to(int) > 0, average="micro")
score_ipu = recall_ipu(preds[:, 0], target > 0, average="micro")
self.assertFalse(score_true.isnan(), "Micro Recall binary is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Recall binary is different"
)
# Micro recall binary with NaNs in target
not_nan = ~target_nan.isnan()
score_true = recall(preds[:, 0][not_nan], target_nan_bin[not_nan].to(int), average="micro")
score_ipu = recall_ipu(preds[:, 0], target_nan_bin, average="micro")
self.assertFalse(score_true.isnan(), "Micro Recall binary with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Recall binary IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Recall binary with NaN is different"
)
# Micro recall
score_true = recall(preds, target.to(int), average="micro")
score_ipu = recall_ipu(preds, target, average="micro")
self.assertFalse(score_true.isnan(), "Micro Recall is NaN")
self.assertAlmostEqual(score_true.item(), score_ipu.item(), places=6, msg="Micro Recall is different")
# Micro recall with NaNs in target
not_nan = ~target_nan.isnan()
score_true = recall(preds[not_nan], target[not_nan].to(int), average="micro")
score_ipu = recall_ipu(preds, target_nan, average="micro")
self.assertFalse(score_true.isnan(), "Micro Recall with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro Recall IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro Recall with NaN is different"
)
# Macro recall multiclass
score_true = recall(preds, target.to(int), average="macro", num_classes=4)
score_ipu = recall_ipu(preds, target, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Recall is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro Recall multiclass is different"
)
# Macro recall multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = recall(preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4)
score_ipu = recall_ipu(preds, target_nan, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro Recall multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro Recall multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro Recall multiclass with NaN is different"
)
# Weighted recallmulticlass
score_true = recall(preds, target.to(int), average="weighted", num_classes=4)
score_ipu = recall_ipu(preds, target, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Recall is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Weighted Recall is different"
)
# Weighted recall multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = recall(preds[not_nan], target[not_nan].to(int), average="weighted", num_classes=4)
score_ipu = recall_ipu(preds, target_nan, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted Recall multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Weighted Recall multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Regular Recall multiclass with NaN is different",
)
def test_pearsonr(self):
preds = deepcopy(self.preds)[:, 0]
target = deepcopy(self.target)[:, 0] + preds
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
# Regular loss
score_true = pearson_corrcoef(preds, target)
score_ipu = pearson_ipu(preds, target)
self.assertFalse(score_true.isnan(), "Pearson is NaN")
self.assertAlmostEqual(score_true.item(), score_ipu.item(), places=4, msg="Pearson is different")
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
score_true = pearson_corrcoef(preds[not_nan], target[not_nan])
score_ipu = pearson_ipu(preds, target_nan)
self.assertFalse(score_true.isnan(), "Regular PearsonR with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "IPU PearsonR score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=4, msg="Pearson with NaN is different"
)
def test_spearmanr(self):
preds = deepcopy(self.preds)[:, 0]
target = deepcopy(self.target)[:, 0] + preds
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
# Regular loss
score_true = spearman_corrcoef(preds, target)
score_ipu = spearman_ipu(preds, target)
self.assertFalse(score_true.isnan(), "Spearman is NaN")
self.assertAlmostEqual(score_true.item(), score_ipu.item(), places=4, msg="Spearman is different")
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
score_true = spearman_corrcoef(preds[not_nan], target[not_nan])
score_ipu = spearman_ipu(preds, target_nan)
self.assertFalse(score_true.isnan(), "Regular Spearman with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "IPU Spearman score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=4, msg="Spearman with NaN is different"
)
def test_r2_score(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target) + preds
target_nan = deepcopy(target)
target_nan[self.is_nan] = float("nan")
# Regular loss
score_true = r2_score(preds, target)
score_ipu = r2_score_ipu(preds, target)
self.assertFalse(score_true.isnan(), "r2_score is NaN")
self.assertAlmostEqual(score_true.item(), score_ipu.item(), places=4, msg="r2_score is different")
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
score_ipu = r2_score_ipu(preds, target_nan, multioutput="raw_values")
for ii in range(preds.shape[1]):
score_true = r2_score(
preds[:, ii][not_nan[:, ii]], target_nan[:, ii][not_nan[:, ii]], multioutput="raw_values"
)
self.assertFalse(score_true.isnan().any(), f"{ii}: r2_score with target_nan is NaN")
self.assertFalse(score_ipu[ii].isnan().any(), f"{ii}: IPU r2_score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu[ii].item(), places=4, msg=f"{ii}: r2_score with NaN is different"
)
def test_fbeta_score(self):
preds = deepcopy(self.preds)[:, :4]
target = deepcopy(self.target)[:, 0]
t = deepcopy(target)
target[t < 0.4] = 0
target[(t >= 0.4) & (t < 0.6)] = 1
target[(t >= 0.6) & (t < 0.8)] = 2
target[(t >= 0.8)] = 3
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
target_nan_bin = deepcopy(target_nan)
target_nan_bin[target_nan > 0] = 1
# Micro fbeta_score binary
score_true = fbeta_score(preds[:, 0], target.to(int) > 0, average="micro", beta=0.5)
score_ipu = fbeta_score_ipu(preds[:, 0], target > 0, average="micro", beta=0.5)
self.assertFalse(score_true.isnan(), "Micro FBETA_score binary is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro FBETA_score binary is different"
)
# Micro fbeta_score binary with NaNs in target
not_nan = ~target_nan.isnan()
score_true = fbeta_score(
preds[:, 0][not_nan], target_nan_bin[not_nan].to(int), average="micro", beta=0.5
)
score_ipu = fbeta_score_ipu(preds[:, 0], target_nan_bin, average="micro", beta=0.5)
self.assertFalse(score_true.isnan(), "Micro FBETA_score binary with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro FBETA_score binary IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Micro FBETA_score binary with NaN is different",
)
# Micro fbeta_score
score_true = fbeta_score(preds, target.to(int), average="micro", beta=0.5)
score_ipu = fbeta_score_ipu(preds, target, average="micro", beta=0.5)
self.assertFalse(score_true.isnan(), "Micro FBETA_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro FBETA_score is different"
)
# Micro fbeta_score with NaNs in target
not_nan = ~target_nan.isnan()
score_true = fbeta_score(preds[not_nan], target[not_nan].to(int), average="micro", beta=0.5)
score_ipu = fbeta_score_ipu(preds, target_nan, average="micro", beta=0.5)
self.assertFalse(score_true.isnan(), "Micro FBETA_score with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro FBETA_score IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro FBETA_score with NaN is different"
)
# Macro fbeta_score multiclass
score_true = fbeta_score(preds, target.to(int), average="macro", num_classes=4, beta=0.5)
score_ipu = fbeta_score_ipu(preds, target, average="macro", num_classes=4, beta=0.5)
self.assertFalse(score_true.isnan(), "Macro FBETA_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro FBETA_score multiclass is different"
)
# Macro fbeta_score multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = fbeta_score(
preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4, beta=0.5
)
score_ipu = fbeta_score_ipu(preds, target_nan, average="macro", num_classes=4, beta=0.5)
self.assertFalse(score_true.isnan(), "Macro FBETA_score multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro FBETA_score multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Macro FBETA_score multiclass with NaN is different",
)
# Weighted fbeta_scoremulticlass
score_true = fbeta_score(preds, target.to(int), average="weighted", num_classes=4, beta=0.5)
score_ipu = fbeta_score_ipu(preds, target, average="weighted", num_classes=4, beta=0.5)
self.assertFalse(score_true.isnan(), "Weighted FBETA_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Weighted FBETA_score is different"
)
# Weighted fbeta_score multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = fbeta_score(
preds[not_nan], target[not_nan].to(int), average="weighted", num_classes=4, beta=0.5
)
score_ipu = fbeta_score_ipu(preds, target_nan, average="weighted", num_classes=4, beta=0.5)
self.assertFalse(score_true.isnan(), "Weighted FBETA_score multiclass with target_nan is NaN")
self.assertFalse(
score_ipu.isnan(), "Weighted FBETA_score multiclass IPU score with target_nan is NaN"
)
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Regular FBETA_score multiclass with NaN is different",
)
def test_f1_score(self):
preds = deepcopy(self.preds)[:, :4]
target = deepcopy(self.target)[:, 0]
t = deepcopy(target)
target[t < 0.4] = 0
target[(t >= 0.4) & (t < 0.6)] = 1
target[(t >= 0.6) & (t < 0.8)] = 2
target[(t >= 0.8)] = 3
target_nan = deepcopy(target)
target_nan[self.is_nan[:, 0]] = float("nan")
target_nan_bin = deepcopy(target_nan)
target_nan_bin[target_nan > 0] = 1
# Micro f1_score binary
score_true = f1_score(preds[:, 0], target.to(int) > 0, average="micro")
score_ipu = f1_score_ipu(preds[:, 0], target > 0, average="micro")
self.assertFalse(score_true.isnan(), "Micro F1_score binary is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro F1_score binary is different"
)
# Micro f1_score binary with NaNs in target
not_nan = ~target_nan.isnan()
score_true = f1_score(preds[:, 0][not_nan], target_nan_bin[not_nan].to(int), average="micro")
score_ipu = f1_score_ipu(preds[:, 0], target_nan_bin, average="micro")
self.assertFalse(score_true.isnan(), "Micro F1_score binary with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro F1_score binary IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro F1_score binary with NaN is different"
)
# Micro f1_score
score_true = f1_score(preds, target.to(int), average="micro")
score_ipu = f1_score_ipu(preds, target, average="micro")
self.assertFalse(score_true.isnan(), "Micro F1_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro F1_score is different"
)
# Micro f1_score with NaNs in target
not_nan = ~target_nan.isnan()
score_true = f1_score(preds[not_nan], target[not_nan].to(int), average="micro")
score_ipu = f1_score_ipu(preds, target_nan, average="micro")
self.assertFalse(score_true.isnan(), "Micro F1_score with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Micro F1_score IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Micro F1_score with NaN is different"
)
# Macro f1_score multiclass
score_true = f1_score(preds, target.to(int), average="macro", num_classes=4)
score_ipu = f1_score_ipu(preds, target, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro F1_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Macro F1_score multiclass is different"
)
# Macro f1_score multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = f1_score(preds[not_nan], target[not_nan].to(int), average="macro", num_classes=4)
score_ipu = f1_score_ipu(preds, target_nan, average="macro", num_classes=4)
self.assertFalse(score_true.isnan(), "Macro F1_score multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Macro F1_score multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Macro F1_score multiclass with NaN is different",
)
# Weighted f1_scoremulticlass
score_true = f1_score(preds, target.to(int), average="weighted", num_classes=4)
score_ipu = f1_score_ipu(preds, target, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted F1_score is NaN")
self.assertAlmostEqual(
score_true.item(), score_ipu.item(), places=6, msg="Weighted F1_score is different"
)
# Weighted f1_score multiclass with NaNs in target
not_nan = ~target_nan.isnan()
score_true = f1_score(preds[not_nan], target[not_nan].to(int), average="weighted", num_classes=4)
score_ipu = f1_score_ipu(preds, target_nan, average="weighted", num_classes=4)
self.assertFalse(score_true.isnan(), "Weighted F1_score multiclass with target_nan is NaN")
self.assertFalse(score_ipu.isnan(), "Weighted F1_score multiclass IPU score with target_nan is NaN")
self.assertAlmostEqual(
score_true.item(),
score_ipu.item(),
places=6,
msg="Regular F1_score multiclass with NaN is different",
)
def test_mse(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target)
target_nan = deepcopy(self.target_nan)
squared = True
# Regular loss
loss_true = mean_squared_error(preds, target, squared)
loss_ipu = mean_squared_error_ipu(preds=preds, target=target, squared=squared)
self.assertFalse(loss_true.isnan(), "Regular Mean Squared Error is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular Mean Squared Error is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = mean_squared_error(preds[not_nan], target[not_nan], squared)
loss_ipu = mean_squared_error_ipu(preds=preds, target=target_nan, squared=squared)
self.assertFalse(loss_true.isnan(), "Regular Mean Squared Error with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular Mean Squared Error IPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(),
loss_ipu.item(),
places=6,
msg="Regular Mean Squared Error with NaN is different",
)
squared = False
# Regular loss
loss_true = mean_squared_error(preds, target, squared)
loss_ipu = mean_squared_error_ipu(preds=preds, target=target, squared=squared)
self.assertFalse(loss_true.isnan(), "Regular Mean Squared Error is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular Mean Squared Error is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = mean_squared_error(preds[not_nan], target[not_nan], squared)
loss_ipu = mean_squared_error_ipu(preds=preds, target=target_nan, squared=squared)
self.assertFalse(loss_true.isnan(), "Regular Mean Squared Error with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular Mean Squared Error IPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(),
loss_ipu.item(),
places=6,
msg="Regular Mean Squared Error with NaN is different",
)
def test_mae(self):
preds = deepcopy(self.preds)
target = deepcopy(self.target)
target_nan = deepcopy(self.target_nan)
# Regular loss
loss_true = mean_absolute_error(preds, target)
loss_ipu = mean_absolute_error_ipu(preds=preds, target=target)
self.assertFalse(loss_true.isnan(), "Regular Mean Absolute Error is NaN")
self.assertAlmostEqual(
loss_true.item(), loss_ipu.item(), places=6, msg="Regular Mean Absolute Error is different"
)
# Regular loss with NaNs in target
not_nan = ~target_nan.isnan()
loss_true = mean_absolute_error(preds[not_nan], target[not_nan])
loss_ipu = mean_absolute_error_ipu(preds=preds, target=target_nan)
self.assertFalse(loss_true.isnan(), "Regular Mean Absolute Error with target_nan is NaN")
self.assertFalse(loss_ipu.isnan(), "Regular Mean Absolute Error IPU with target_nan is NaN")
self.assertAlmostEqual(
loss_true.item(),
loss_ipu.item(),
places=6,
msg="Regular Mean Absolute Error with NaN is different",
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_ipu_options.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pytest
from graphium.config._loader import _get_ipu_opts, load_ipu_options
from graphium.ipu.ipu_utils import ipu_options_list_to_file
import tempfile
from typing import Optional, List
import os
CONFIG_EXTRACT = {
"trainer": {"trainer": {"accumulate_grad_batches": 10}},
"accelerator": {
"type": "ipu",
"config_override": {
"datamodule": {
"args": {
"ipu_dataloader_training_opts": {
"mode": "async",
"max_num_nodes_per_graph": 44,
"max_num_edges_per_graph": 80,
},
"ipu_dataloader_inference_opts": {
"mode": "async",
"max_num_nodes_per_graph": 44,
"max_num_edges_per_graph": 80,
},
"batch_size_training": 50,
"batch_size_inference": 50,
}
},
"predictor": {"optim_kwargs": {"loss_scaling": 1024}},
"trainer": {"trainer": {"precision": 16, "accumulate_grad_batches": 4}},
},
"ipu_config": [
"deviceIterations(5)",
"replicationFactor(16)",
"TensorLocations.numIOTiles(128)",
'_Popart.set("defaultBufferingDepth", 128)',
"Precision.enableStochasticRounding(True)",
],
"ipu_inference_config": [
"deviceIterations(1)",
"replicationFactor(4)",
"TensorLocations.numIOTiles(32)",
'_Popart.set("defaultBufferingDepth", 16)',
"Precision.enableStochasticRounding(True)",
],
},
}
@pytest.mark.ipu
def test_ipu_options():
try:
import poptorch
ipu_opts, ipu_inference_opts = _get_ipu_opts(CONFIG_EXTRACT)
# Define the expected IPU options for comparison
expected_ipu_opts = [
"deviceIterations(5)",
"replicationFactor(16)",
"TensorLocations.numIOTiles(128)",
'_Popart.set("defaultBufferingDepth", 128)',
"Precision.enableStochasticRounding(True)",
]
expected_ipu_inference_opts = [
"deviceIterations(1)",
"replicationFactor(4)",
"TensorLocations.numIOTiles(32)",
'_Popart.set("defaultBufferingDepth", 16)',
"Precision.enableStochasticRounding(True)",
]
# Test the _get_ipu_opts method
ipu_opts, ipu_inference_opts = _get_ipu_opts(CONFIG_EXTRACT)
assert ipu_opts == expected_ipu_opts, f"Expected {expected_ipu_opts}, but got {ipu_opts}"
assert (
ipu_inference_opts == expected_ipu_inference_opts
), f"Expected {expected_ipu_inference_opts}, but got {ipu_inference_opts}"
# Test the load_ipu_options method
ipu_training_opts, ipu_inference_opts = load_ipu_options(
ipu_opts=ipu_opts,
seed=42,
model_name="test_model",
gradient_accumulation=CONFIG_EXTRACT["trainer"]["trainer"].get("accumulate_grad_batches", None),
ipu_inference_opts=ipu_inference_opts,
)
# Ensure that the options objects are not None
assert ipu_training_opts is not None, "Expected ipu_training_opts not to be None"
assert ipu_inference_opts is not None, "Expected ipu_inference_opts not to be None"
# Test the properties of the options objects
assert (
ipu_training_opts.replication_factor == 16
), "Expected replication_factor of ipu_training_opts to be 16"
assert (
ipu_inference_opts.replication_factor == 4
), "Expected replication_factor of ipu_inference_opts to be 4"
assert ipu_training_opts._popart, "Expected _popart of ipu_training_opts to be True"
assert ipu_inference_opts._popart, "Expected _popart of ipu_inference_opts to be True"
except ImportError:
pytest.skip("Skipping this test because poptorch is not available")
@pytest.mark.ipu
def test_ipu_options_list_to_file():
# Define a list of IPU options
ipu_options = [
"deviceIterations(5)",
"replicationFactor(16)",
"TensorLocations.numIOTiles(128)",
'_Popart.set("defaultBufferingDepth", 128)',
"Precision.enableStochasticRounding(True)",
]
# Call the function with the list of IPU options
tmp_file = ipu_options_list_to_file(ipu_options)
# Check that the function returns a temporary file object
assert isinstance(tmp_file, tempfile._TemporaryFileWrapper)
# Check that the temporary file exists
assert os.path.exists(tmp_file.name)
# Check the contents of the temporary file
with open(tmp_file.name, "r") as f:
contents = f.read().splitlines()
assert contents == ipu_options
# Check the behavior when the input is None
tmp_file = ipu_options_list_to_file(None)
assert tmp_file is None
================================================
FILE: tests/test_ipu_poptorch.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pytest
@pytest.mark.ipu
def test_poptorch():
# Run this test only if poptorch is available
# Primarily to test the install and SDK is correctly activated
try:
import poptorch
opts = poptorch.Options()
except ImportError:
raise ImportError
assert True
================================================
FILE: tests/test_ipu_to_dense_batch.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pytest
import torch
from torch_geometric.data import Data, Batch
from graphium.ipu.to_dense_batch import to_dense_batch
from warnings import warn
# General imports
import yaml
import unittest as ut
import numpy as np
from copy import deepcopy
from warnings import warn
from lightning import Trainer, LightningModule
from lightning_graphcore import IPUStrategy
from functools import partial
import torch
from torch.utils.data.dataloader import default_collate
# Current library imports
from graphium.config._loader import load_datamodule, load_metrics, load_architecture, load_accelerator
@pytest.mark.ipu
class TestIPUBatch:
@pytest.fixture(autouse=True)
def setup_class(self):
self.in_dim = 12
self.out_dim = 12
self.in_dim_edges = 10
self.out_dim_edges = 10
self.edge_idx1 = torch.stack(
[torch.tensor([0, 1, 2, 3, 2], dtype=torch.int), torch.tensor([1, 2, 3, 0, 0], dtype=torch.int)]
)
self.edge_idx2 = torch.stack(
[torch.tensor([0, 0, 0, 1], dtype=torch.int), torch.tensor([0, 1, 2, 0], dtype=torch.int)]
)
self.x1 = torch.randn(self.edge_idx1.max().item() + 1, self.in_dim, dtype=torch.float32)
self.e1 = torch.randn(self.edge_idx1.shape[-1], self.in_dim_edges, dtype=torch.float32)
self.x2 = torch.randn(self.edge_idx2.max().item() + 1, self.in_dim, dtype=torch.float32)
self.e2 = torch.randn(self.edge_idx2.shape[-1], self.in_dim_edges, dtype=torch.float32)
self.g1 = Data(feat=self.x1, edge_index=self.edge_idx1, edge_feat=self.e1)
self.g2 = Data(feat=self.x2, edge_index=self.edge_idx2, edge_feat=self.e2)
self.bg = Batch.from_data_list([self.g1, self.g2])
self.attn_kwargs = {"embed_dim": self.in_dim, "num_heads": 2, "batch_first": True}
# @pytest.mark.skip
@pytest.mark.parametrize("max_num_nodes_per_graph, batch_size", [(10, 5), (20, 10), (30, 15)])
def test_ipu_to_dense_batch(self, max_num_nodes_per_graph, batch_size):
# Run this test only if poptorch is available
try:
import poptorch
opts = poptorch.Options()
opts.useIpuModel(True)
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x, batch):
return to_dense_batch(
x,
batch=batch,
batch_size=batch_size,
max_num_nodes_per_graph=max_num_nodes_per_graph,
drop_nodes_last_graph=False,
)
model = MyModel()
model = model.eval()
poptorch_model_inf = poptorch.inferenceModel(model, options=opts)
# for data in train_dataloader:
out, mask, idx = poptorch_model_inf(self.bg.feat, self.bg.batch)
# Check the output sizes
assert out.size() == torch.Size([batch_size, max_num_nodes_per_graph, 12])
# Check the mask for true / false values
assert mask.size() == torch.Size([batch_size, max_num_nodes_per_graph])
assert torch.sum(mask) == 7
assert (mask[0][:4] == True).all()
assert (mask[0][4:] == False).all()
assert (mask[1][:3] == True).all()
assert (mask[1][3:] == False).all()
assert (mask[2:] == False).all()
# Check the idx are all the true values in the mask
assert (mask.flatten()[idx] == True).all()
poptorch_model_inf.detachFromDevice()
except ImportError:
pytest.skip("Skipping this test because poptorch is not available")
def test_ipu_to_dense_batch_no_batch_no_max_nodes(self):
h_dense, mask = to_dense_batch(
self.bg.feat,
batch=None,
batch_size=None,
max_num_nodes_per_graph=None,
drop_nodes_last_graph=False,
)
# Add assertions to check the output as needed
assert torch.allclose(h_dense, self.bg.feat.unsqueeze(0), atol=1e-5), "Tensors are not equal"
assert mask.size(1) == h_dense.size(1)
assert mask.all().item(), "Not all values in the tensor are True"
def test_ipu_to_dense_batch_no_batch(self):
max_nodes_per_graph = 10
h_dense, mask, id = to_dense_batch(
self.bg.feat,
batch=None,
batch_size=None,
max_num_nodes_per_graph=max_nodes_per_graph,
drop_nodes_last_graph=False,
)
assert mask.size() == (1, max_nodes_per_graph)
assert torch.sum(mask) == 7
assert torch.equal(id, torch.arange(7))
assert h_dense.size() == (1, max_nodes_per_graph, self.bg.feat.size(-1))
def test_ipu_to_dense_batch_drop_last(self):
out, mask, idx = to_dense_batch(
self.bg.feat,
batch=None,
batch_size=None,
max_num_nodes_per_graph=3,
drop_nodes_last_graph=True,
)
# Add assertions to check the output as needed
assert mask.size(1) == out.size(1)
# Check the mask and output have been clipped
assert mask.size() == torch.Size([1, 3])
assert mask.all().item(), "Not all values in the tensor are True"
================================================
FILE: tests/test_loaders.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
from graphium.config._loader import merge_dicts
from copy import deepcopy
import unittest as ut
class TestLoader(ut.TestCase):
def test_merge_dicts(self):
dict_a = {"a": {"b": {"c": 1, "d": 2}, "e": 3}, "f": 4}
dict_b = {"a": {"b": {"g": 6}}, "h": 7}
dict_c = {
"a": {
"b": {
"c": 1,
},
}
}
# Check that the new keys are added correctly
merge_dicts(dict_a, dict_b)
self.assertEqual(dict_a["a"]["b"]["g"], dict_b["a"]["b"]["g"])
self.assertEqual(dict_a["h"], dict_b["h"])
# Check that no error is thrown if a key exists, but the value is identical
merge_dicts(dict_a, dict_c)
self.assertEqual(dict_a["a"]["b"]["c"], dict_c["a"]["b"]["c"])
# Check that an error is thrown if a key exists, but the value is different
dict_d = deepcopy(dict_c)
dict_d["a"]["b"]["c"] = 2
with self.assertRaises(ValueError):
merge_dicts(dict_a, dict_d)
# Main
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_losses.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the metrics and wrappers of graphium/trainer/metrics/...
"""
import torch
import unittest as ut
from torch.nn import functional as F
from graphium.trainer.losses import HybridCELoss
from graphium.trainer.predictor_options import EvalOptions
def _parse(loss_fun):
eval_options = EvalOptions(loss_fun=loss_fun, metrics_on_progress_bar=None)
return eval_options.parse_loss_fun(loss_fun)
class test_HybridCELoss(ut.TestCase):
input = torch.Tensor([[0.1, 0.1, 0.3, 0.5, 0.0], [0.1, 0.0, 0.7, 0.2, 0.0]])
target = torch.Tensor([3, 0]).long()
brackets = torch.Tensor([0, 1, 2, 3, 4])
regression_input = torch.Tensor([2.0537, 2.0017]) # inner product of input and brackets
regression_target = torch.Tensor([3, 0]).float()
def test_pure_ce_loss(self):
loss = HybridCELoss(n_brackets=len(self.brackets), alpha=1.0, reduction="none")
assert torch.allclose(
loss(self.input, self.target),
F.cross_entropy(self.input, self.target, reduction="none"),
)
assert loss(self.input, self.target).shape == (2,)
def test_pure_mae_loss(self):
loss = HybridCELoss(
n_brackets=len(self.brackets),
alpha=0.0,
regression_loss="mae",
reduction="none",
)
assert torch.allclose(
loss(self.input, self.target),
F.l1_loss(self.regression_input, self.regression_target, reduction="none"),
rtol=1e-04,
atol=1e-07,
)
assert loss(self.input, self.target).shape == (2,)
def test_pure_mse_loss(self):
loss = HybridCELoss(
n_brackets=len(self.brackets),
alpha=0.0,
regression_loss="mse",
reduction="none",
)
assert torch.allclose(
loss(self.input, self.target),
F.mse_loss(self.regression_input, self.regression_target, reduction="none"),
rtol=1e-04,
atol=1e-07,
)
assert loss(self.input, self.target).shape == (2,)
def test_hybrid_loss(self):
loss = HybridCELoss(n_brackets=len(self.brackets), alpha=0.5, regression_loss="mse")
ce_loss = F.cross_entropy(self.input, self.target)
mse_loss = F.mse_loss(self.regression_input, self.regression_target)
assert torch.allclose(
loss(self.input, self.target), 0.5 * ce_loss + 0.5 * mse_loss, rtol=1e-04, atol=1e-07
)
assert loss(self.input, self.target).shape == torch.Size([])
def test_loss_parser(self):
# HybridCE cannot be parsed from a string because it requires specifying n_brackets
loss_fun = "hybrid_ce"
self.assertRaises(TypeError, _parse, loss_fun=loss_fun)
# HybridCE requires n_brackets to be specified
loss_fun = {"name": "hybrid_ce"}
self.assertRaises(TypeError, _parse, loss_fun=loss_fun)
loss_fun = {"name": "hybrid_ce", "n_brackets": 3}
parsed = _parse(loss_fun)
assert isinstance(parsed, HybridCELoss)
assert len(parsed.brackets) == 3
assert parsed.regression_loss == F.mse_loss
loss_fun = {"name": "hybrid_ce", "n_brackets": 5, "regression_loss": "mae"}
parsed = _parse(loss_fun)
assert isinstance(parsed, HybridCELoss)
assert len(parsed.brackets) == 5
assert parsed.regression_loss == F.l1_loss
class test_BCELoss(ut.TestCase):
def test_loss_parser(self):
loss_fun = "bce"
parsed = _parse(loss_fun)
assert isinstance(parsed, torch.nn.BCELoss)
loss_fun = {"name": "bce"}
parsed = _parse(loss_fun)
assert isinstance(parsed, torch.nn.BCELoss)
assert parsed.reduction != "sum"
loss_fun = {"name": "bce", "reduction": "sum"}
parsed = _parse(loss_fun)
assert isinstance(parsed, torch.nn.BCELoss)
assert parsed.reduction == "sum"
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_metrics.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the metrics and wrappers of graphium/trainer/metrics/...
"""
import torch
import unittest as ut
import tempfile
import os
import operator as op
from graphium.trainer.metrics import (
MetricWrapper,
Thresholder,
)
from torchmetrics.functional import mean_squared_error
class test_Metrics(ut.TestCase):
def test_thresholder(self):
torch.manual_seed(42)
preds = torch.rand(100, dtype=torch.float32)
target = torch.rand(100, dtype=torch.float32)
th = 0.7
preds_greater = preds > th
target_greater = target > th
# Test thresholder greater
for th_on_preds in [True, False]:
for th_on_target in [True, False]:
thresholder = Thresholder(
threshold=th, operator="greater", th_on_target=th_on_target, th_on_preds=th_on_preds
)
preds2, target2 = thresholder(preds, target)
if th_on_preds:
self.assertListEqual(preds2.tolist(), preds_greater.tolist())
else:
self.assertListEqual(preds2.tolist(), preds.tolist())
if th_on_target:
self.assertListEqual(target2.tolist(), target_greater.tolist())
else:
self.assertListEqual(target2.tolist(), target.tolist())
# Test thresholder lower
for th_on_preds in [True, False]:
for th_on_target in [True, False]:
thresholder = Thresholder(
threshold=th, operator="lower", th_on_target=th_on_target, th_on_preds=th_on_preds
)
preds2, target2 = thresholder(preds, target)
if th_on_preds:
self.assertListEqual(preds2.tolist(), (~preds_greater).tolist())
else:
self.assertListEqual(preds2.tolist(), preds.tolist())
if th_on_target:
self.assertListEqual(target2.tolist(), (~target_greater).tolist())
else:
self.assertListEqual(target2.tolist(), target.tolist())
class test_MetricWrapper(ut.TestCase):
def test_target_nan_mask(self):
torch.random.manual_seed(42)
for sz in [(100,), (100, 1), (100, 10)]:
err_msg = f"Error for `sz = {sz}`"
# Generate prediction and target matrices
target = torch.rand(sz, dtype=torch.float32)
preds = (0.5 * target) + (0.5 * torch.rand(sz, dtype=torch.float32))
is_nan = torch.rand(sz) > 0.3
target = (target > 0.5).to(torch.float32)
target[is_nan] = float("nan")
# Compute score with different ways of ignoring NaNs
metric = MetricWrapper(metric="mse", target_nan_mask=None)
score1 = metric(preds, target)
self.assertTrue(torch.isnan(score1), msg=err_msg)
# Replace NaNs by 0
metric = MetricWrapper(metric="mse", target_nan_mask=0)
score2 = metric(preds, target)
this_target = target.clone()
this_target[is_nan] = 0
this_preds = preds.clone()
self.assertAlmostEqual(score2, mean_squared_error(this_preds, this_target), msg=err_msg)
# Replace NaNs by 1.5
metric = MetricWrapper(metric="mse", target_nan_mask=1.5)
score3 = metric(preds, target)
this_target = target.clone()
this_target[is_nan] = 1.5
this_preds = preds.clone()
self.assertAlmostEqual(score3, mean_squared_error(this_preds, this_target), msg=err_msg)
# Flatten matrix and ignore NaNs
metric = MetricWrapper(metric="mse", target_nan_mask="ignore", multitask_handling="flatten")
score4 = metric(preds, target)
this_target = target.clone()[~is_nan]
this_preds = preds.clone()[~is_nan]
self.assertAlmostEqual(score4, mean_squared_error(this_preds, this_target), msg=err_msg)
# Ignore NaNs in each column and average the score
metric = MetricWrapper(
metric="mse", target_nan_mask="ignore", multitask_handling="mean-per-label"
)
score5 = metric(preds, target)
this_target = target.clone()
this_preds = preds.clone()
this_is_nan = is_nan.clone()
if len(sz) == 1:
this_target = target.unsqueeze(-1)
this_preds = preds.unsqueeze(-1)
this_is_nan = is_nan.unsqueeze(-1)
this_target = [this_target[:, ii][~this_is_nan[:, ii]] for ii in range(this_target.shape[1])]
this_preds = [this_preds[:, ii][~this_is_nan[:, ii]] for ii in range(this_preds.shape[1])]
mse = []
for ii in range(len(this_preds)):
mse.append(mean_squared_error(this_preds[ii], this_target[ii]))
mse = torch.mean(torch.stack(mse))
self.assertAlmostEqual(score5.tolist(), mse.tolist(), msg=err_msg)
def test_pickling(self):
pickle_file = os.path.join(tempfile.gettempdir(), "test_metric_pickled.pkl")
metrics = ["mae", "mse", mean_squared_error]
target_nan_masks = [None, 2, "ignore"]
multitask_handlings = [None, "flatten", "mean-per-label"]
squeeze_targets = [True, False]
target_to_ints = [True, False]
other_kwargs = [{}, {"squared": False}]
thresholds = [
None,
{"threshold": 0.2, "operator": "greater"},
{"threshold": 0.3, "operator": op.lt},
{"threshold": 0.4, "operator": "lower"},
{"threshold": 0.5, "operator": "lower", "th_on_preds": False, "th_on_target": True},
{"threshold": 0.6, "operator": "lower"},
]
for metric in metrics:
for target_nan_mask in target_nan_masks:
for kwargs in other_kwargs:
for threshold_kwargs in thresholds:
for multitask_handling in multitask_handlings:
for squeeze_target in squeeze_targets:
for target_to_int in target_to_ints:
err_msg = f"{metric} - {target_nan_mask} - {kwargs} - {threshold_kwargs}"
if (multitask_handling is None) and (target_nan_mask == "ignore"):
# Raise with incompatible options
with self.assertRaises(ValueError):
MetricWrapper(
metric=metric,
threshold_kwargs=threshold_kwargs,
target_nan_mask=target_nan_mask,
multitask_handling=multitask_handling,
squeeze_target=squeeze_target,
target_to_int=target_to_int,
**kwargs,
)
else:
metric_wrapper = MetricWrapper(
metric=metric,
threshold_kwargs=threshold_kwargs,
target_nan_mask=target_nan_mask,
multitask_handling=multitask_handling,
squeeze_target=squeeze_target,
target_to_int=target_to_int,
**kwargs,
)
# Check that the metric can be saved and re-loaded without error
torch.save(metric_wrapper, pickle_file)
metric_wrapper2 = torch.load(pickle_file)
self.assertTrue(metric_wrapper == metric_wrapper2, msg=err_msg)
# Check that the metric only contains primitive types
state = metric_wrapper.__getstate__()
if state["threshold_kwargs"] is not None:
self.assertIsInstance(
state["threshold_kwargs"], dict, msg=err_msg
)
if isinstance(metric, str):
self.assertIsInstance(state["metric"], str, msg=err_msg)
def test_classifigression_target_squeezing(self):
preds = torch.Tensor([[0.1, 0.1, 0.3, 0.5, 0.0, 0.1, 0.0, 0.7, 0.2, 0.0]])
target = torch.Tensor([3, 0])
expected_scores = [0.5, 0.75]
n_brackets = 5
metrics = ["accuracy", "averageprecision"]
other_kwargs = [
{"task": "multiclass", "num_classes": n_brackets, "top_k": 1},
{"task": "multiclass", "num_classes": n_brackets},
]
for metric, kwargs, expected_score in zip(metrics, other_kwargs, expected_scores):
metric_wrapper = MetricWrapper(
metric=metric,
multitask_handling="mean-per-label",
squeeze_targets=True,
target_to_int=True,
**kwargs,
)
score = metric_wrapper(preds, target)
assert score == expected_score
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_mtl_architecture.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different architectures of graphium/nn/architectures...
The layers are not thoroughly tested due to the difficulty of testing them
"""
import torch
import unittest as ut
from torch_geometric.data import Data, Batch
from copy import deepcopy
from itertools import product
import sys
import traceback
import graphium
from graphium.config._loader import load_architecture
from graphium.nn.architectures import TaskHeads, FullGraphMultiTaskNetwork, GraphOutputNN
from graphium.nn.base_layers import FCLayer
from graphium.utils.spaces import LAYERS_DICT
kwargs = {
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
"residual_type": "none",
"residual_skip_steps": 1,
}
# task kwargs
task_1_kwargs = {
"out_dim": 5,
"task_level": "node",
"hidden_dims": [5, 6, 7],
}
task_2_kwargs = {
"out_dim": 3,
"task_level": "edge",
"hidden_dims": [8, 9, 10],
}
task_3_kwargs = {
"out_dim": 4,
"task_level": "graph",
"hidden_dims": [2, 2, 2],
}
task_4_kwargs = {
"out_dim": 4,
"task_level": "nodepair",
"hidden_dims": [2, 2, 2],
}
# level-wise kwargs
node_level_kwargs = {
"out_dim": 8,
"hidden_dims": [8, 9, 10],
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
"residual_type": "none",
"residual_skip_steps": 1,
}
graph_level_kwargs = {
"pooling": ["sum", "max"],
"out_dim": 8,
"hidden_dims": [8, 9, 10],
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
"residual_type": "none",
"residual_skip_steps": 1,
}
edge_level_kwargs = {
"out_dim": 8,
"hidden_dims": [8, 9, 10],
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
"residual_type": "none",
"residual_skip_steps": 1,
}
nodepair_level_kwargs = {
"out_dim": 8,
"hidden_dims": [8, 9, 10],
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
"layer_type": FCLayer,
"residual_type": "none",
"residual_skip_steps": 1,
}
task_1_params = {}
task_1_params.update(task_1_kwargs)
task_1_params.update(kwargs)
task_2_params = {}
task_2_params.update(task_2_kwargs)
task_2_params.update(kwargs)
task_3_params = {}
task_3_params.update(task_3_kwargs)
task_3_params.update(kwargs)
task_4_params = {}
task_4_params.update(task_4_kwargs)
task_4_params.update(kwargs)
def toy_test_data(in_dim=7, in_dim_edges=3):
edge_idx1 = torch.stack([torch.tensor([0, 1, 2, 3, 2]), torch.tensor([1, 2, 3, 0, 0])])
edge_idx2 = torch.stack([torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])])
x1 = torch.randn(edge_idx1.max() + 1, in_dim, dtype=torch.float32)
e1 = torch.randn(edge_idx1.shape[-1], in_dim_edges, dtype=torch.float32)
x2 = torch.randn(edge_idx2.max() + 1, in_dim, dtype=torch.float32)
e2 = torch.randn(edge_idx2.shape[-1], in_dim_edges, dtype=torch.float32)
# edge_idx1, e1 = add_self_loops(edge_idx1, e1)
# edge_idx2, e2 = add_self_loops(edge_idx2, e2)
g1 = Data(feat=x1, edge_index=edge_idx1, edge_feat=e1)
g2 = Data(feat=x2, edge_index=edge_idx2, edge_feat=e2)
bg = Batch.from_data_list([g1, g2])
num_nodes_per_graph = bg.batch.bincount()
batch_node = bg["feat"].size()[0]
batch_edge = bg["edge_feat"].size()[0]
batch_graph = 2
batch_nodepair = ((num_nodes_per_graph.max().item() ** 2) - num_nodes_per_graph.max().item()) // 2
return bg, batch_node, batch_edge, batch_graph, batch_nodepair
class test_GraphOutputNN(ut.TestCase):
def generate_test_data(self):
g_1 = torch.tensor([[2, 3, 4], [0, 1, 0], [0, 1, 1], [1, 0, 0]])
g_2 = torch.tensor([[3, 4, 0], [0, 1, 1], [1, 0, 0]])
g_3 = torch.tensor([[3, 4, 0], [0, 1, 1], [1, 0, 0], [2, 2, 2], [4, 4, 4]])
batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2])
x = torch.concat([g_1, g_2, g_3]).float()
nan = -1
expected_result = [
# graph 1
[
[2.0, 4.0, 4.0, 2.0, 2.0, 4.0],
[2.0, 4.0, 5.0, 2.0, 2.0, 3.0],
[3.0, 3.0, 4.0, 1.0, 3.0, 4.0],
[nan, nan, nan, nan, nan, nan],
[0.0, 2.0, 1.0, 0.0, 0.0, 1.0],
[1.0, 1.0, 0.0, 1.0, 1.0, 0.0],
[nan, nan, nan, nan, nan, nan],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
],
# graph 2
[
[3.0, 5.0, 1.0, 3.0, 3.0, 1.0],
[4.0, 4.0, 0.0, 2.0, 4.0, 0.0],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan],
],
# graph 3
[
[3.0, 5.0, 1.0, 3.0, 3.0, 1.0],
[4.0, 4.0, 0.0, 2.0, 4.0, 0.0],
[5.0, 6.0, 2.0, 1.0, 2.0, 2.0],
[7.0, 8.0, 4.0, 1.0, 0.0, 4.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[2.0, 3.0, 3.0, 2.0, 1.0, 1.0],
[4.0, 5.0, 5.0, 4.0, 3.0, 3.0],
[3.0, 2.0, 2.0, 1.0, 2.0, 2.0],
[5.0, 4.0, 4.0, 3.0, 4.0, 4.0],
[6.0, 6.0, 6.0, 2.0, 2.0, 2.0],
],
]
return x, batch, expected_result
def test_nodepair_max_num_nodes_not_set(self):
in_dim = 3
in_dim_edges = 8
graph_output_nn_kwargs = {
"node": node_level_kwargs,
"edge": edge_level_kwargs,
"graph": graph_level_kwargs,
"nodepair": nodepair_level_kwargs,
}
graph_output_nn = GraphOutputNN(
in_dim=in_dim,
in_dim_edges=in_dim_edges,
task_level="nodepair",
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
x, batch, expected_result = self.generate_test_data()
out = graph_output_nn.compute_nodepairs(node_feats=x, batch=batch)
out = torch.nan_to_num(out, nan=-1)
self.assertListEqual(expected_result, out.tolist())
def test_nodepair_with_max_num_nodes(self):
in_dim = 3
in_dim_edges = 8
graph_output_nn_kwargs = {
"node": node_level_kwargs,
"edge": edge_level_kwargs,
"graph": graph_level_kwargs,
"nodepair": nodepair_level_kwargs,
}
graph_output_nn = GraphOutputNN(
in_dim=in_dim,
in_dim_edges=in_dim_edges,
task_level="nodepair",
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
max_num_nodes = 5 # if we change this value, we also have to change the expected result.
x, batch, expected_result = self.generate_test_data()
out = graph_output_nn.compute_nodepairs(node_feats=x, batch=batch, max_num_nodes=max_num_nodes)
out = torch.nan_to_num(out, nan=-1)
self.assertListEqual(expected_result, out.tolist())
class test_TaskHeads(ut.TestCase):
def test_task_heads_forward(self):
in_dim = 8 # Dimension of the incoming data
in_dim_edges = 8
task_heads_params = {
"task_1": task_1_params,
"task_2": task_2_params,
"task_3": task_3_params,
"task_4": task_4_params,
}
graph_output_nn_kwargs = {
"node": node_level_kwargs,
"edge": edge_level_kwargs,
"graph": graph_level_kwargs,
"nodepair": nodepair_level_kwargs,
}
# Create the "multitask" network. Really it's just an input going to various FFNNs since there's nothing shared.
multi_head_nn = TaskHeads(
in_dim=in_dim,
in_dim_edges=in_dim_edges,
task_heads_kwargs=task_heads_params,
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
# Test the sizes of the MLPs for each head
# Head for task_1
task_1_head = multi_head_nn.task_heads["task_1"]
# Check the dimensions
self.assertEqual(len(task_1_head.layers), len(task_1_kwargs["hidden_dims"]) + 1)
self.assertEqual(task_1_head.layers[0].in_dim, in_dim)
self.assertEqual(task_1_head.layers[1].in_dim, task_1_kwargs["hidden_dims"][0])
self.assertEqual(task_1_head.layers[2].in_dim, task_1_kwargs["hidden_dims"][1])
self.assertEqual(task_1_head.layers[3].in_dim, task_1_kwargs["hidden_dims"][2])
# Head for task_2
task_2_head = multi_head_nn.task_heads["task_2"]
# Check the dimensions
self.assertEqual(len(task_2_head.layers), len(task_2_kwargs["hidden_dims"]) + 1)
self.assertEqual(task_2_head.layers[0].in_dim, in_dim)
self.assertEqual(task_2_head.layers[1].in_dim, task_2_kwargs["hidden_dims"][0])
self.assertEqual(task_2_head.layers[2].in_dim, task_2_kwargs["hidden_dims"][1])
self.assertEqual(task_2_head.layers[3].in_dim, task_2_kwargs["hidden_dims"][2])
# Head for task_3
task_3_head = multi_head_nn.task_heads["task_3"]
# Check the dimensions
self.assertEqual(len(task_3_head.layers), len(task_3_kwargs["hidden_dims"]) + 1)
self.assertEqual(task_3_head.layers[0].in_dim, in_dim)
self.assertEqual(task_3_head.layers[1].in_dim, task_3_kwargs["hidden_dims"][0])
self.assertEqual(task_3_head.layers[2].in_dim, task_3_kwargs["hidden_dims"][1])
self.assertEqual(task_3_head.layers[3].in_dim, task_3_kwargs["hidden_dims"][2])
# Head for task_4
task_4_head = multi_head_nn.task_heads["task_3"]
# Check the dimensions
self.assertEqual(len(task_4_head.layers), len(task_4_kwargs["hidden_dims"]) + 1)
self.assertEqual(task_4_head.layers[0].in_dim, in_dim)
self.assertEqual(task_4_head.layers[1].in_dim, task_4_kwargs["hidden_dims"][0])
self.assertEqual(task_4_head.layers[2].in_dim, task_4_kwargs["hidden_dims"][1])
self.assertEqual(task_4_head.layers[3].in_dim, task_4_kwargs["hidden_dims"][2])
# Check the output: It's a per-task prediction!
bg, batch_node, batch_edge, batch_graph, batch_nodepair = toy_test_data(
in_dim=in_dim, in_dim_edges=in_dim_edges
)
feat_out = multi_head_nn.forward(bg)
self.assertListEqual(
list(feat_out["task_1"].shape), [batch_node, task_1_kwargs["out_dim"]]
) # node level task
self.assertListEqual(
list(feat_out["task_2"].shape), [batch_edge, task_2_kwargs["out_dim"]]
) # edge level task
self.assertListEqual(
list(feat_out["task_3"].shape), [batch_graph, task_3_kwargs["out_dim"]]
) # graph level task
self.assertListEqual(
list(feat_out["task_4"].shape), [batch_graph, batch_nodepair, task_4_kwargs["out_dim"]]
) # nodepair level task
def test_task_heads_non_supported_level(self):
in_dim = 8 # Dimension of the incoming data
in_dim_edges = 8
fake_task = deepcopy(task_1_params)
fake_task["task_level"] = "certainly_not_supported_level"
task_heads_params = {
"task_1": fake_task,
"task_2": task_2_params,
"task_3": task_3_params,
"task_4": task_4_params,
}
graph_output_nn_kwargs = {
"certainly_not_supported_level": node_level_kwargs,
"edge": edge_level_kwargs,
"graph": graph_level_kwargs,
"nodepair": nodepair_level_kwargs,
}
with self.assertRaises(ValueError):
TaskHeads(
in_dim=in_dim,
in_dim_edges=in_dim_edges,
task_heads_kwargs=task_heads_params,
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
class test_Multitask_NN(ut.TestCase):
pyg_kwargs = {
"activation": "relu",
"last_activation": "none",
"normalization": "none",
"dropout": 0.2,
"name": "LNN",
}
in_dim = 7
fullgraph_out_dim = 11
in_dim_edges = 3
hidden_dims = [6, 6, 6, 6, 6]
virtual_nodes = ["none", "mean", "sum"]
norms = ["none", None, "batch_norm", "layer_norm"]
pna_kwargs = {"aggregators": ["mean", "max", "sum"], "scalers": ["identity", "amplification"]}
gnn_layers_kwargs = {
"pyg:gin": {},
"pyg:gine": {"in_dim_edges": in_dim_edges},
"pyg:gated-gcn": {"in_dim_edges": in_dim_edges, "hidden_dims_edges": hidden_dims},
"pyg:pna-msgpass#1": {"layer_kwargs": pna_kwargs, "in_dim_edges": 0},
"pyg:pna-msgpass#2": {"layer_kwargs": pna_kwargs, "in_dim_edges": in_dim_edges},
}
def test_full_graph_multitask_forward(self):
# Params for the network
temp_dim_1 = 5
temp_dim_2 = 7
temp_dim_edges = 21
default_graph_output_nn_kwargs = {
"node": dict(out_dim=10, hidden_dims=[3, 3, 3, 3]),
"edge": dict(out_dim=11, hidden_dims=[4, 4, 4, 4]),
"graph": dict(out_dim=12, hidden_dims=[5, 5, 5, 5]),
"nodepair": dict(out_dim=13, hidden_dims=[6, 6, 6, 6]),
}
# TODO: Test all combinations?
all_task_heads_kwargs = dict(
task_node=task_1_params,
task_edge=task_2_params,
task_graph=task_3_params,
task_nodepair=task_4_params,
)
only_node_and_graph_task_heads_kwargs = dict(task_node=task_1_params, task_graph=task_3_params)
# TODO: Configure properly in pytest best-practice
options = product(
[["sum"], ["mean", "max"]],
[1, 2],
self.virtual_nodes,
self.norms,
self.gnn_layers_kwargs.items(),
[None, all_task_heads_kwargs, only_node_and_graph_task_heads_kwargs],
[None, dict(in_dim=self.in_dim, out_dim=temp_dim_1, hidden_dims=[4, 4, 4, 4, 4])],
[None, dict(in_dim=self.in_dim_edges, out_dim=temp_dim_edges, hidden_dims=[4, 4])],
)
for (
pooling,
residual_skip_steps,
virtual_node,
normalization,
(layer_name, this_kwargs),
task_heads_kwargs,
pre_nn_kwargs,
pre_nn_edges_kwargs,
) in options:
err_msg = f"pooling={pooling}, virtual_node={virtual_node}, layer_name={layer_name}, residual_skip_steps={residual_skip_steps}, normalization={normalization}, task_heads={task_heads_kwargs}, pre_nn_kwargs={pre_nn_kwargs}, graph_output_nn_kwargs={default_graph_output_nn_kwargs}, pre_nn_edges_kwargs={pre_nn_edges_kwargs}"
layer_type = layer_name.split("#")[0]
# TODO: graph_output_nn is currently non-optional, should it be optional?
if task_heads_kwargs is not None:
continue
# TODO: Allow to pass single graph_output_nn_kwargs to apply to all levels?
bg, num_nodes, num_edges, num_graphs, num_nodepairs = toy_test_data(
in_dim=self.in_dim, in_dim_edges=self.in_dim_edges
)
expected_dim_edges = self.in_dim_edges
this_kwargs2 = deepcopy(this_kwargs)
if pre_nn_edges_kwargs is not None:
this_kwargs2["in_dim_edges"] = expected_dim_edges = temp_dim_edges
gnn_kwargs = dict(
in_dim=self.in_dim if pre_nn_kwargs is None else temp_dim_1,
out_dim=temp_dim_2,
hidden_dims=self.hidden_dims,
residual_type="densenet",
residual_skip_steps=residual_skip_steps,
layer_type=layer_type,
**this_kwargs2,
**self.pyg_kwargs,
)
graph_output_nn_kwargs = deepcopy(default_graph_output_nn_kwargs)
graph_output_nn_kwargs["graph"]["pooling"] = pooling
expectFailure = False
if (not LAYERS_DICT[layer_type].layer_supports_edges) and (pre_nn_edges_kwargs is not None):
expectFailure = True
if (not LAYERS_DICT[layer_type].layer_supports_edges) and (
task_heads_kwargs is not None and "task_edge" in task_heads_kwargs
):
expectFailure = True
if (
"in_dim_edges" in this_kwargs2
and this_kwargs2["in_dim_edges"] < 1
and (task_heads_kwargs is not None and "task_edge" in task_heads_kwargs)
):
expectFailure = True
if task_heads_kwargs is not None and "task_graph" in task_heads_kwargs:
expectFailure = True
if expectFailure:
with self.assertRaises(ValueError):
multitask_graph_nn = FullGraphMultiTaskNetwork(
gnn_kwargs=gnn_kwargs,
pre_nn_kwargs=pre_nn_kwargs,
pre_nn_edges_kwargs=pre_nn_edges_kwargs,
task_heads_kwargs=task_heads_kwargs,
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
continue
try:
multitask_graph_nn = FullGraphMultiTaskNetwork(
gnn_kwargs=gnn_kwargs,
pre_nn_kwargs=pre_nn_kwargs,
pre_nn_edges_kwargs=pre_nn_edges_kwargs,
task_heads_kwargs=task_heads_kwargs,
graph_output_nn_kwargs=graph_output_nn_kwargs,
)
batch_out = multitask_graph_nn.forward(bg)
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
msg = err_msg + "\n" + str(traceback.format_exception(exc_type, exc_value, exc_traceback))
self.fail(msg)
if task_heads_kwargs is not None:
if "task_node" in task_heads_kwargs:
self.assertListEqual(
list(batch_out["task_node"].shape),
[num_nodes, task_1_kwargs["out_dim"]],
msg=err_msg,
)
if "task_edge" in task_heads_kwargs:
self.assertListEqual(
list(batch_out["task_edge"].shape),
[num_edges, task_2_kwargs["out_dim"]],
msg=err_msg,
)
if "task_graph" in task_heads_kwargs:
self.assertListEqual(
list(batch_out["task_graph"].shape),
[num_graphs, task_3_kwargs["out_dim"]],
msg=err_msg,
)
if "task_nodepair" in task_heads_kwargs:
self.assertListEqual(
list(batch_out["task_nodepair"].shape),
[num_nodepairs, task_4_kwargs["out_dim"]],
msg=err_msg,
)
else:
feat_out = batch_out["feat"]
edge_feat_out = batch_out["edge_feat"]
out_dim_edges = (
multitask_graph_nn.gnn.full_dims_edges[-1]
if multitask_graph_nn.gnn.full_dims_edges is not None
else expected_dim_edges
)
if not LAYERS_DICT[layer_type].layer_supports_edges or this_kwargs2["in_dim_edges"] == 0:
self.assertEqual(multitask_graph_nn.out_dim_edges, 0)
else:
self.assertEqual(multitask_graph_nn.out_dim_edges, out_dim_edges)
self.assertListEqual(list(feat_out.shape), [num_nodes, temp_dim_2], msg=err_msg)
self.assertListEqual(list(edge_feat_out.shape), [num_edges, out_dim_edges], msg=err_msg)
def test_full_graph_multi_task_from_config(self):
cfg = graphium.load_config(name="zinc_default_multitask_pyg")
# Initialize the network
in_dims = {"feat": self.in_dim, "edge_feat": self.in_dim_edges}
model_class, model_kwargs = load_architecture(cfg, in_dims=in_dims)
multitask_full_graph_nn = model_class(**model_kwargs)
# Test
bg, num_nodes, num_edges, num_graphs, num_nodepairs = toy_test_data(
in_dim=self.in_dim, in_dim_edges=self.in_dim_edges
)
batch_out = multitask_full_graph_nn.forward(bg)
self.assertListEqual(
list(batch_out["task_1"].shape),
[num_nodes, task_1_kwargs["out_dim"]],
)
self.assertListEqual(
list(batch_out["task_2"].shape),
[num_edges, task_2_kwargs["out_dim"]],
)
self.assertListEqual(
list(batch_out["task_3"].shape),
[num_graphs, task_3_kwargs["out_dim"]],
)
self.assertListEqual(
list(batch_out["task_4"].shape),
[num_graphs, num_nodepairs, task_4_kwargs["out_dim"]],
)
def test_full_graph_multi_task_set_max_num_nodes(self):
cfg = graphium.load_config(name="zinc_default_multitask_pyg")
# Initialize the network
in_dims = {"feat": self.in_dim, "edge_feat": self.in_dim_edges}
model_class, model_kwargs = load_architecture(cfg, in_dims=in_dims)
multitask_full_graph_nn: FullGraphMultiTaskNetwork = model_class(**model_kwargs)
multitask_full_graph_nn.set_max_num_nodes_edges_per_graph(10, 4)
# Probing
self.assertEqual(
multitask_full_graph_nn.task_heads.graph_output_nn["nodepair"].max_num_nodes_per_graph, 10
)
self.assertEqual(multitask_full_graph_nn.gnn.layers[2].max_num_edges_per_graph, 4)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_multitask_datamodule.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import shutil
import tempfile
import unittest as ut
import pytest
from omegaconf import OmegaConf
import pandas as pd
import numpy as np
import graphium
class Test_Multitask_DataModule(ut.TestCase):
def setUp(self):
# Create a temporary directory
self.tmp_test_dir = tempfile.mkdtemp()
def tearDown(self):
# Remove the directory after the test
shutil.rmtree(self.tmp_test_dir)
def test_multitask_fromsmiles_dm(
self,
): # TODO: I think we can remove this as it tests tiny_zinc which only contain graph level labels
"""Cover similar testing as for the original data module."""
df = graphium.data.load_tiny_zinc() # 100 molecules
# Here we take the microzinc dataset and split the labels up into 'SA', 'logp' and 'score' in order to simulate having multiple single-task datasets
df_micro_zinc_SA = df[["SMILES", "SA"]]
df_micro_zinc_logp = df[["SMILES", "logp"]]
df_micro_zinc_score = df[["SMILES", "score"]]
# Setup the featurization. This will be the same across all tasks.
featurization_args = {}
featurization_args["atom_property_list_float"] = [] # ["weight", "valence"]
featurization_args["atom_property_list_onehot"] = ["atomic-number", "degree"]
featurization_args["edge_property_list"] = ["in-ring", "bond-type-onehot"]
featurization_args["add_self_loop"] = False
featurization_args["use_bonds_weights"] = False
featurization_args["explicit_H"] = False
# Config for multitask datamodule.
# Per-task arguments.
dm_task_args_SA = {}
dm_task_args_SA["df"] = df_micro_zinc_SA
dm_task_args_SA["task_level"] = "graph"
dm_task_args_SA["smiles_col"] = "SMILES"
dm_task_args_SA["label_cols"] = ["SA"]
dm_task_args_SA["split_val"] = 0.2
dm_task_args_SA["split_test"] = 0.2
dm_task_args_SA["seed"] = 19
dm_task_args_SA["splits_path"] = None # This may not always be provided
dm_task_args_SA["sample_size"] = None # This may not always be provided
dm_task_args_SA["idx_col"] = None # This may not always be provided
dm_task_args_SA["weights_col"] = None # This may not always be provided
dm_task_args_SA["weights_type"] = None # This may not always be provided
dm_task_args_logp = {}
dm_task_args_logp["df"] = df_micro_zinc_logp
dm_task_args_logp["task_level"] = "graph"
dm_task_args_logp["smiles_col"] = "SMILES"
dm_task_args_logp["label_cols"] = ["logp"]
dm_task_args_logp["split_val"] = 0.2
dm_task_args_logp["split_test"] = 0.2
dm_task_args_logp["seed"] = 19
dm_task_args_logp["splits_path"] = None # This may not always be provided
dm_task_args_logp["sample_size"] = None # This may not always be provided
dm_task_args_logp["idx_col"] = None # This may not always be provided
dm_task_args_logp["weights_col"] = None # This may not always be provided
dm_task_args_logp["weights_type"] = None # This may not always be provided
dm_task_args_score = {}
dm_task_args_score["df"] = df_micro_zinc_score
dm_task_args_score["task_level"] = "graph"
dm_task_args_score["smiles_col"] = "SMILES"
dm_task_args_score["label_cols"] = ["score"]
dm_task_args_score["split_val"] = 0.2
dm_task_args_score["split_test"] = 0.2
dm_task_args_score["seed"] = 19
dm_task_args_score["splits_path"] = None # This may not always be provided
dm_task_args_score["sample_size"] = None # This may not always be provided
dm_task_args_score["idx_col"] = None # This may not always be provided
dm_task_args_score["weights_col"] = None # This may not always be provided
dm_task_args_score["weights_type"] = None # This may not always be provided
dm_task_kwargs = {}
dm_task_kwargs["SA"] = dm_task_args_SA
dm_task_kwargs["logp"] = dm_task_args_logp
dm_task_kwargs["score"] = dm_task_args_score
dm_args = {}
# Task-specific arguments for the datamodule
dm_args["task_specific_args"] = dm_task_kwargs
# Task-independent arguments
dm_args["featurization"] = featurization_args
dm_args["featurization_n_jobs"] = 16
dm_args["featurization_progress"] = True
dm_args["featurization_backend"] = "loky"
dm_args["num_workers"] = 0
dm_args["pin_memory"] = True
dm_args["processed_graph_data_path"] = None
dm_args["batch_size_training"] = 16
dm_args["batch_size_inference"] = 16
# Create the data module
dm = graphium.data.MultitaskFromSmilesDataModule(**dm_args)
# self.assertEqual(50, dm.num_node_feats) # Not implemeneted error
# self.assertEqual(6, dm.num_edge_feats)
dm.prepare_data()
dm.setup()
# self.assertEqual(len(dm), 100) # Should this have a fixed value for when it's initialized? MTL dataset only gets created after.
self.assertEqual(len(dm.train_ds), 60) # type: ignore
self.assertEqual(len(dm.val_ds), 20) # type: ignore
self.assertEqual(len(dm.test_ds), 20) # type: ignore
# assert dm.num_node_feats == 50
# assert dm.num_edge_feats == 6
for dl in [dm.train_dataloader(), dm.val_dataloader(), dm.test_dataloader()]:
it = iter(dl)
batch = next(it)
assert set(batch.keys()) == {"labels", "features"}
# assert batch["labels"].shape == (16, 1) # Single-task case
assert batch["labels"]["graph_SA"].shape == (16, 1)
assert batch["labels"]["graph_logp"].shape == (16, 1)
assert batch["labels"]["graph_score"].shape == (16, 1)
# @pytest.mark.skip
def test_multitask_fromsmiles_from_config(self):
config = graphium.load_config(name="zinc_default_multitask_pyg")
df = graphium.data.load_tiny_zinc() # 100 molecules
# Here we take the microzinc dataset and split the labels up into 'SA', 'logp' and 'score' in order to simulate having multiple single-task datasets
df_micro_zinc_SA = df[["SMILES", "SA"]]
df_micro_zinc_logp = df[["SMILES", "logp"]]
df_micro_zinc_score = df[["SMILES", "score"]]
# dm_args = dict(config.datamodule.args)
dm_args = OmegaConf.to_container(config.datamodule.args, resolve=True)
# dm_args["task_specific_args"]["SA"]["df"] = df
dm_args["task_specific_args"]["SA"]["df"] = df_micro_zinc_SA
dm_args["task_specific_args"]["logp"]["df"] = df_micro_zinc_logp
dm_args["task_specific_args"]["score"]["df"] = df_micro_zinc_score
dm_args["task_specific_args"]["SA"]["smiles_col"] = "SMILES"
dm_args["task_specific_args"]["logp"]["smiles_col"] = "SMILES"
dm_args["task_specific_args"]["score"]["smiles_col"] = "SMILES"
dm_args["task_specific_args"]["SA"]["label_cols"] = ["SA"]
dm_args["task_specific_args"]["logp"]["label_cols"] = ["logp"]
dm_args["task_specific_args"]["score"]["label_cols"] = ["score"]
dm_args["task_specific_args"]["SA"]["df_path"] = None
dm_args["task_specific_args"]["logp"]["df_path"] = None
dm_args["task_specific_args"]["score"]["df_path"] = None
dm = graphium.data.MultitaskFromSmilesDataModule(**dm_args)
# assert dm.num_node_feats == 50
# assert dm.num_edge_feats == 6
dm.prepare_data()
dm.setup()
# self.assertEqual(len(dm), 100) # Should this have a fixed value for when it's initialized? MTL dataset only gets created after.
self.assertEqual(len(dm.train_ds), 60) # type: ignore
self.assertEqual(len(dm.val_ds), 20) # type: ignore
self.assertEqual(len(dm.test_ds), 20) # type: ignore
# assert dm.num_node_feats == 50
# assert dm.num_edge_feats == 6
for dl in [dm.train_dataloader(), dm.val_dataloader(), dm.test_dataloader()]:
it = iter(dl)
batch = next(it)
assert set(batch.keys()) == {"labels", "features"}
# assert batch["labels"].shape == (16, 1) # Single-task case
assert batch["labels"]["graph_SA"].shape == (16, 1)
assert batch["labels"]["graph_logp"].shape == (16, 1)
assert batch["labels"]["graph_score"].shape == (16, 1)
def test_multitask_fromsmiles_from_config_csv(self):
config = graphium.load_config(name="zinc_default_multitask_pyg")
dm_args = OmegaConf.to_container(config.datamodule.args, resolve=True)
dm = graphium.data.MultitaskFromSmilesDataModule(**dm_args)
dm.prepare_data()
dm.setup()
# self.assertEqual(len(dm), 100) # Should this have a fixed value for when it's initialized? MTL dataset only gets created after.
self.assertEqual(len(dm.train_ds), 60) # type: ignore
self.assertEqual(len(dm.val_ds), 20) # type: ignore
self.assertEqual(len(dm.test_ds), 20) # type: ignore
# assert dm.num_node_feats == 50
# assert dm.num_edge_feats == 6
for dl in [dm.train_dataloader(), dm.val_dataloader(), dm.test_dataloader()]:
it = iter(dl)
batch = next(it)
assert set(batch.keys()) == {"labels", "features"}
# assert batch["labels"].shape == (16, 1) # Single-task case
assert batch["labels"]["graph_SA"].shape == (16, 1)
assert batch["labels"]["graph_logp"].shape == (16, 1)
assert batch["labels"]["graph_score"].shape == (16, 1)
def test_multitask_fromsmiles_from_config_parquet(self):
config = graphium.load_config(name="fake_multilevel_multitask_pyg")
dm_args = OmegaConf.to_container(config.datamodule.args, resolve=True)
dm = graphium.data.MultitaskFromSmilesDataModule(**dm_args)
dm.prepare_data()
dm.setup()
self.assertEqual(len(dm.train_ds), 1004) # type: ignore
dl = dm.train_dataloader()
it = iter(dl)
batch = next(it)
assert set(batch.keys()) == {"labels", "features"}
# assert batch["labels"].shape == (16, 1) # Single-task case
assert batch["labels"]["graph_SA"].shape == (16, 1)
assert batch["labels"]["node_logp"].shape == (
batch["features"].feat.size(0),
2,
) # test node level
assert batch["labels"]["edge_score"].shape == (
batch["features"].edge_feat.size(0),
2,
) # test edge level
def test_multitask_with_missing_fromsmiles_from_config_parquet(self):
config = graphium.load_config(name="fake_and_missing_multilevel_multitask_pyg")
dm_args = OmegaConf.to_container(config.datamodule.args, resolve=True)
dm = graphium.data.MultitaskFromSmilesDataModule(**dm_args)
dm.prepare_data()
dm.setup()
self.assertEqual(len(dm.train_ds), 1004) # type: ignore
dl = dm.train_dataloader()
it = iter(dl)
batch = next(it)
assert set(batch.keys()) == {"labels", "features"}
# assert batch["labels"].shape == (16, 1) # Single-task case
assert batch["labels"]["graph_SA"].shape == (16, 1)
assert batch["labels"]["node_logp"].shape == (
batch["features"].feat.size(0),
2,
) # test node level
assert batch["labels"]["edge_score"].shape == (
batch["features"].edge_feat.size(0),
2,
) # test edge level
def test_extract_graph_level_singletask(self):
df = pd.read_parquet(f"tests/converted_fake_multilevel_data.parquet")
num_graphs = len(df)
label_cols = ["graph_label"]
output = graphium.data.datamodule.extract_labels(df, "graph", label_cols)
assert isinstance(output, np.ndarray)
assert len(output.shape) == 2
assert output.shape[0] == num_graphs
assert output.shape[1] == 1
def test_extract_graph_level_multitask(self):
df = pd.read_parquet(f"tests/converted_fake_multilevel_data.parquet")
num_graphs = len(df)
label_cols = ["graph_label", "graph_label"]
output = graphium.data.datamodule.extract_labels(df, "graph", label_cols)
assert isinstance(output, np.ndarray)
assert len(output.shape) == 2
assert output.shape[0] == num_graphs
assert output.shape[1] == len(label_cols)
def test_extract_graph_level_multitask_missing_cols(self):
df = pd.read_parquet(f"tests/converted_fake_multilevel_data.parquet")
num_graphs = len(df)
label_cols = ["graph_label", "graph_label"]
drop_index = [2, 5, 21, 237, 192, 23, 127, 11]
for replace in [1, 2]:
for missing_col in label_cols[:replace]:
df[missing_col].iloc[drop_index] = None
output = graphium.data.datamodule.extract_labels(df, "graph", label_cols)
assert isinstance(output, np.ndarray)
assert len(output.shape) == 2
assert output.shape[0] == num_graphs
assert output.shape[1] == len(label_cols)
def test_non_graph_level_extract_labels(self):
df = pd.read_parquet(f"tests/converted_fake_multilevel_data.parquet")
for level in ["node", "edge", "nodepair"]:
label_cols = [f"{level}_label_{suffix}" for suffix in ["list", "np"]]
output = graphium.data.datamodule.extract_labels(df, level, label_cols)
assert isinstance(output, list)
assert len(output[0].shape) == 2
assert output[0].shape[1] == len(label_cols)
def test_non_graph_level_extract_labels_missing_cols(self):
df = pd.read_parquet(f"tests/converted_fake_multilevel_data.parquet")
for level in ["node", "edge", "nodepair"]:
label_cols = [f"{level}_label_{suffix}" for suffix in ["list", "np"]]
drop_index = [2, 5, 21, 237, 192, 23, 127, 11]
for replace in [1, 2]:
for missing_col in label_cols[:replace]:
df.loc[drop_index, missing_col] = None
output = graphium.data.datamodule.extract_labels(df, level, label_cols)
for idx in drop_index:
assert len(output[idx].shape) == 2
assert output[idx].shape[1] == len(label_cols)
# Check that number of labels is adjusted correctly
if replace == 1:
non_missing_col = label_cols[1]
assert output[idx].shape[0] == len(df[non_missing_col][idx])
def test_tdc_admet_benchmark_data_module(self):
"""
Verifies that the ADMET-specific subclass of the MultiTaskDataModule works.
Checks if all main endpoints can be run and if the split is correct.
"""
try:
from tdc.benchmark_group import admet_group
from tdc.utils import retrieve_benchmark_names
except ImportError:
self.skipTest("PyTDC needs to be installed to run this test. Use `pip install PyTDC`.")
raise
# Make sure we can initialize the module and run the main endpoints
data_module = graphium.data.ADMETBenchmarkDataModule()
data_module.prepare_data()
data_module.setup()
for dl in [
data_module.train_dataloader(),
data_module.val_dataloader(),
data_module.test_dataloader(),
]:
batch = next(iter(dl))
assert set(batch.keys()) == {"labels", "features"}
# # Validate the split
group = admet_group(path=self.tmp_test_dir)
benchmark_names = retrieve_benchmark_names("admet_group")
# For each of the endpoints...
for name in benchmark_names:
# Get the split from the benchmark group (ground truth)
benchmark = group.get(name)
train, val = group.get_train_valid_split(0, name)
test = benchmark["test"]
# Get the split from the data module
params = data_module._get_task_specific_arguments(name, 0, self.tmp_test_dir)
split = pd.read_csv(params.splits_path)
data = params.df
# Check that the split is the same
for ground_truth, label in [(train, "train"), (val, "val"), (test, "test")]:
y_true = ground_truth["Y"].values
y_module = data.loc[split[label].dropna(), "Y"].values
assert len(y_true) == len(y_module)
assert np.allclose(np.sort(y_true), np.sort(y_module))
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_mup.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the implementation of mup
"""
import unittest as ut
from copy import deepcopy
import torch.nn as nn
import torch
import yaml
from torch_geometric.data import Batch, Data
from graphium.nn.architectures import FeedForwardNN, FeedForwardPyg, FullGraphMultiTaskNetwork
def get_pyg_graphs(in_dim, in_dim_edges):
edge_idx1 = torch.stack([torch.tensor([0, 1, 2, 3, 2]), torch.tensor([1, 2, 3, 0, 0])])
edge_idx2 = torch.stack([torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])])
x1 = torch.randn(edge_idx1.max() + 1, in_dim, dtype=torch.float32)
e1 = torch.randn(edge_idx1.shape[-1], in_dim_edges, dtype=torch.float32)
x2 = torch.randn(edge_idx2.max() + 1, in_dim, dtype=torch.float32)
e2 = torch.randn(edge_idx2.shape[-1], in_dim_edges, dtype=torch.float32)
g1 = Data(feat=x1, edge_index=edge_idx1, edge_feat=e1)
g2 = Data(feat=x2, edge_index=edge_idx2, edge_feat=e2)
bg = Batch.from_data_list([g1, g2])
return bg
class test_mup(ut.TestCase):
kwargs = dict(
in_dim=12,
out_dim=60,
hidden_dims=8 * [84],
depth=None,
activation="LeakyReLU",
last_activation="LeakyReLU",
dropout=0.1,
last_dropout=0.2,
normalization="batch_norm",
first_normalization="batch_norm",
last_normalization="batch_norm",
residual_type="simple",
residual_skip_steps=2,
name="testing",
layer_type="fc",
layer_kwargs=None,
)
def test_feedforwardnn_mup(self):
kwargs = deepcopy(self.kwargs)
model = FeedForwardNN(**kwargs, last_layer_is_readout=False)
model_lastreadout = FeedForwardNN(**kwargs, last_layer_is_readout=True)
base_1 = model.make_mup_base_kwargs(divide_factor=1)
base_1_lastreadout = model_lastreadout.make_mup_base_kwargs(divide_factor=1)
base_2 = model.make_mup_base_kwargs(divide_factor=2)
base_2_lastreadout = model_lastreadout.make_mup_base_kwargs(divide_factor=2)
kwargs_2 = deepcopy(base_1)
kwargs_2.update(dict(out_dim=30, hidden_dims=8 * [42]))
kwargs_2_lastreadout = deepcopy(base_1_lastreadout)
kwargs_2_lastreadout.update(dict(hidden_dims=8 * [42]))
# Check the kwargs matching
for key in kwargs_2.keys():
if isinstance(kwargs_2[key], nn.Module):
# Can't match the random weights
self.assertEqual(str(kwargs_2[key]), str(base_2[key]), msg=key)
else:
self.assertEqual(kwargs_2[key], base_2[key], msg=key)
# Check the kwargs matching
for key in kwargs_2_lastreadout.keys():
if isinstance(kwargs_2_lastreadout[key], nn.Module):
# Can't match the random weights
self.assertEqual(str(kwargs_2_lastreadout[key]), str(base_2_lastreadout[key]), msg=key)
else:
self.assertEqual(kwargs_2_lastreadout[key], base_2_lastreadout[key], msg=key)
# Test that the models with divide_factor=1 can be built run a forward pass
in_features = torch.randn((10, kwargs["in_dim"]))
model_1 = FeedForwardNN(**base_1)
model_1.forward(deepcopy(in_features))
model_1_lastreadout = FeedForwardNN(**base_1_lastreadout)
model_1_lastreadout.forward(deepcopy(in_features))
# Test that the models with divide_factor=2 can be built run a forward pass
model_2 = FeedForwardNN(**base_2)
model_2.forward(deepcopy(in_features))
model_2_lastreadout = FeedForwardNN(**base_2_lastreadout)
model_2_lastreadout.forward(deepcopy(in_features))
def test_feedforwardgraph_mup(self):
kwargs = deepcopy(self.kwargs)
in_dim_edges = kwargs["in_dim"]
kwargs.update(dict(layer_type="pyg:gine", in_dim_edges=in_dim_edges))
model = FeedForwardPyg(**kwargs, last_layer_is_readout=False)
model_lastreadout = FeedForwardPyg(**kwargs, last_layer_is_readout=True)
base_1 = model.make_mup_base_kwargs(divide_factor=1)
base_1_lastreadout = model_lastreadout.make_mup_base_kwargs(divide_factor=1)
base_2 = model.make_mup_base_kwargs(divide_factor=2)
base_2_lastreadout = model_lastreadout.make_mup_base_kwargs(divide_factor=2)
kwargs_2 = deepcopy(base_1)
kwargs_2.update(dict(out_dim=30, hidden_dims=8 * [42]))
kwargs_2_lastreadout = deepcopy(base_1_lastreadout)
kwargs_2_lastreadout.update(dict(hidden_dims=8 * [42]))
# Check the kwargs matching
for key in kwargs_2.keys():
if isinstance(kwargs_2[key], nn.Module):
# Can't match the random weights
self.assertEqual(str(kwargs_2[key]), str(base_2[key]), msg=key)
else:
self.assertEqual(kwargs_2[key], base_2[key], msg=key)
# Check the kwargs matching
for key in kwargs_2_lastreadout.keys():
if isinstance(kwargs_2_lastreadout[key], nn.Module):
# Can't match the random weights
self.assertEqual(str(kwargs_2_lastreadout[key]), str(base_2_lastreadout[key]), msg=key)
else:
self.assertEqual(kwargs_2_lastreadout[key], base_2_lastreadout[key], msg=key)
# Test that the models with divide_factor=1 can be built run a forward pass
in_features = get_pyg_graphs(in_dim=kwargs["in_dim"], in_dim_edges=in_dim_edges)
model_1 = FeedForwardPyg(**base_1)
model_1.forward(deepcopy(in_features))
model_1_lastreadout = FeedForwardPyg(**base_1_lastreadout)
model_1_lastreadout.forward(deepcopy(in_features))
# Test that the models with divide_factor=2 can be built run a forward pass
model_2 = FeedForwardPyg(**base_2)
model_2.forward(deepcopy(in_features))
model_2_lastreadout = FeedForwardPyg(**base_2_lastreadout)
model_2_lastreadout.forward(deepcopy(in_features))
def test_fullgraphmultitasknetwork(self):
# Load the configuration file for the model
CONFIG_FILE = "tests/config_test_ipu_dataloader.yaml"
with open(CONFIG_FILE, "r") as f:
cfg = yaml.safe_load(f)
# Make fake graphs
in_dim = 12
in_dim_edges = 12
pe_indims = {
"laplacian_eigvec": 5,
"laplacian_eigval": 5,
"rw_return_probs": 2,
"edge_rw_transition_probs": 2,
"nodepair_rw_return_probs": 1,
"electrostatic": 6,
"edge_commute": 1,
"nodepair_graphormer": 1,
"positions_3d": 3,
}
in_features = get_pyg_graphs(in_dim=in_dim, in_dim_edges=in_dim_edges)
for key, dim in pe_indims.items():
if key.startswith("edge_"):
in_features[key] = torch.randn(in_features.num_edges, dim)
elif key.startswith("nodepair_"):
in_features[key] = torch.randn(in_features.num_nodes, in_features.num_nodes, dim)
else:
in_features[key] = torch.randn(in_features.num_nodes, dim)
# Load the model
kwargs = {}
for key, val in cfg["architecture"].items():
if key in ["model_type", "mup_base_path"]:
continue
kwargs[key + "_kwargs"] = val
kwargs["pre_nn_kwargs"]["in_dim"] = in_dim + kwargs["pe_encoders_kwargs"]["out_dim"]
kwargs["pre_nn_edges_kwargs"]["in_dim"] = in_dim_edges + kwargs["pe_encoders_kwargs"]["edge_out_dim"]
kwargs["pe_encoders_kwargs"]["in_dims"] = pe_indims
model = FullGraphMultiTaskNetwork(**kwargs, last_layer_is_readout=True)
kw_1 = model.make_mup_base_kwargs(divide_factor=1)
kw_2 = model.make_mup_base_kwargs(divide_factor=2)
# Check the parameter sizes
for key, elem in kw_1.items():
if not isinstance(elem, dict):
continue
for subkey, subelem in elem.items():
if "dim" in subkey:
match = f"{key}:{subkey}"
if match in ["pre_nn_kwargs:in_dim"]:
self.assertNotEqual(subelem, kw_2[key][subkey], msg=match)
elif match in [
"pre_nn_kwargs:out_dim",
"pre_nn_edges_kwargs:out_dim",
"gnn_kwargs:in_dim",
"graph_output_nn_kwargs:in_dim",
"gnn_kwargs:out_dim",
"gnn_kwargs:in_dim_edges",
"pe_encoders_kwargs:out_dim",
"graph_output_nn_kwargs:out_dim",
]:
# Divide by 2
self.assertEqual(round(subelem / 2), kw_2[key][subkey], msg=match)
elif match in [
"pre_nn_kwargs:hidden_dims",
"pre_nn_edges_kwargs:hidden_dims",
"gnn_kwargs:hidden_dims",
"graph_output_nn_kwargs:hidden_dims",
"gnn_kwargs:hidden_dims_edges",
]:
# Arrays divide by 2
new_list = [round(e / 2) for e in subelem]
self.assertListEqual(new_list, kw_2[key][subkey], msg=match)
elif subkey in ["homo", "alpha", "cv"]:
for subsubkey, subsubelem in subelem.items():
match = f"{key}:{subsubkey}"
if match in [
"task_heads_kwargs:out_dim",
"task_heads_kwargs:out_dim",
"task_heads_kwargs:out_dim",
]:
# No divide
self.assertEqual(subsubelem, kw_2[key][subkey][subsubkey], msg=match)
elif match in [
"task_heads_kwargs:in_dim",
"task_heads_kwargs:in_dim",
"task_heads_kwargs:in_dim",
]:
# Divide by 2
self.assertEqual(round(subsubelem / 2), kw_2[key][subkey][subsubkey], msg=match)
elif match in [
"task_heads_kwargs:hidden_dims",
"task_heads_kwargs:hidden_dims",
"task_heads_kwargs:hidden_dims",
]:
# Divide by 2 a list
new_list = [round(e / 2) for e in subsubelem]
self.assertListEqual(new_list, kw_2[key][subkey][subsubkey], msg=match)
# Test that the models with divide_factor=1 can be built run a forward pass
kw_1["last_layer_is_readout"] = False
model_1 = FullGraphMultiTaskNetwork(**kw_1)
model_1.forward(deepcopy(in_features))
kw_1["last_layer_is_readout"] = True
model_1 = FullGraphMultiTaskNetwork(**kw_1)
model_1.forward(deepcopy(in_features))
# Test that the models with divide_factor=2 can be built run a forward pass
kw_2["last_layer_is_readout"] = False
model_2 = FullGraphMultiTaskNetwork(**kw_2)
model_2.forward(deepcopy(in_features))
kw_2["last_layer_is_readout"] = True
model_2 = FullGraphMultiTaskNetwork(**kw_2)
model_2.forward(deepcopy(in_features))
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_packing.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
# General imports
import unittest as ut
import numpy as np
import torch
from torch_geometric.data import Data, Batch
# Current library imports
from graphium.utils.packing import (
smart_packing,
get_pack_sizes,
fast_packing,
hybrid_packing,
node_to_pack_indices_mask,
)
def random_packing(num_nodes, batch_size):
ipu_batch_size = int(len(num_nodes) / batch_size)
indices = np.arange(len(num_nodes))
np.random.shuffle(indices)
indices = np.reshape(indices, (ipu_batch_size, batch_size)).tolist()
return indices
class test_Packing(ut.TestCase):
def test_smart_packing(self):
np.random.seed(42)
batch_sizes = [2, 4, 8, 16, 32, 64]
ipu_batch_sizes = [2, 3, 4, 8, 16, 32, 64]
for batch_size in batch_sizes:
for ipu_batch_size in ipu_batch_sizes:
err_msg = f"bz={batch_size}, ipu_bz={ipu_batch_size}"
# Generate random batch size
global_batch = batch_size * ipu_batch_size
num_nodes = np.abs(np.random.gamma(2, 20, size=global_batch)).astype(int)
# Use the smart packing
packed_indices = smart_packing(num_nodes=num_nodes, batch_size=batch_size)
pack_num_nodes = get_pack_sizes(packed_indices, num_nodes)
# Use the random packing
rand_packed_indices = random_packing(num_nodes=num_nodes, batch_size=batch_size)
rand_pack_num_nodes = get_pack_sizes(rand_packed_indices, num_nodes)
# Assert that the smart packing is better than the random packing
self.assertLessEqual(max(pack_num_nodes), max(rand_pack_num_nodes), msg=err_msg)
self.assertGreaterEqual(min(pack_num_nodes), min(rand_pack_num_nodes), msg=err_msg)
# Assert that the total number of atoms is right
self.assertEqual(sum(pack_num_nodes), sum(num_nodes), msg=err_msg)
self.assertEqual(sum(rand_pack_num_nodes), sum(num_nodes), msg=err_msg)
# Assert that all index are there
self.assertListEqual(
np.sort(np.asarray(packed_indices).flatten()).tolist(), np.arange(len(num_nodes)).tolist()
)
self.assertListEqual(
np.sort(np.asarray(rand_packed_indices).flatten()).tolist(),
np.arange(len(num_nodes)).tolist(),
)
def test_fast_packing(self):
np.random.seed(42)
# Start at 4 for fast_packing for better statistical significance
batch_sizes = [4, 8, 16, 32, 64]
ipu_batch_sizes = [4, 8, 16, 32, 64]
for batch_size in batch_sizes:
for ipu_batch_size in ipu_batch_sizes:
err_msg = f"bz={batch_size}, ipu_bz={ipu_batch_size}"
# Generate random batch size
global_batch = batch_size * ipu_batch_size
num_nodes = np.abs(np.random.gamma(2, 20, size=global_batch)).astype(int)
# Use the smart packing
packed_indices = fast_packing(num_nodes=num_nodes, batch_size=batch_size)
pack_num_nodes = get_pack_sizes(packed_indices, num_nodes)
# Use the random packing
rand_packed_indices = random_packing(num_nodes=num_nodes, batch_size=batch_size)
rand_pack_num_nodes = get_pack_sizes(rand_packed_indices, num_nodes)
# Assert that the smart packing is better than the random packing
self.assertLessEqual(max(pack_num_nodes), max(rand_pack_num_nodes), msg=err_msg)
self.assertGreaterEqual(min(pack_num_nodes), min(rand_pack_num_nodes), msg=err_msg)
# Assert that the total number of atoms is right
self.assertEqual(sum(pack_num_nodes), sum(num_nodes), msg=err_msg)
self.assertEqual(sum(rand_pack_num_nodes), sum(num_nodes), msg=err_msg)
# Assert that all index are there
self.assertListEqual(
np.sort(np.asarray(packed_indices).flatten()).tolist(), np.arange(len(num_nodes)).tolist()
)
self.assertListEqual(
np.sort(np.asarray(rand_packed_indices).flatten()).tolist(),
np.arange(len(num_nodes)).tolist(),
)
def test_hybrid_packing(self):
np.random.seed(42)
batch_sizes = [2, 4, 8, 16, 32, 64]
ipu_batch_sizes = [2, 3, 4, 8, 16, 32, 64]
for batch_size in batch_sizes:
for ipu_batch_size in ipu_batch_sizes:
err_msg = f"bz={batch_size}, ipu_bz={ipu_batch_size}"
# Generate random batch size
global_batch = batch_size * ipu_batch_size
num_nodes = np.abs(np.random.gamma(2, 20, size=global_batch)).astype(int)
# Use the smart packing
packed_indices = hybrid_packing(num_nodes=num_nodes, batch_size=batch_size)
pack_num_nodes = get_pack_sizes(packed_indices, num_nodes)
# Use the random packing
rand_packed_indices = random_packing(num_nodes=num_nodes, batch_size=batch_size)
rand_pack_num_nodes = get_pack_sizes(rand_packed_indices, num_nodes)
# Assert that the smart packing is better than the random packing
self.assertLessEqual(max(pack_num_nodes), max(rand_pack_num_nodes), msg=err_msg)
self.assertGreaterEqual(min(pack_num_nodes), min(rand_pack_num_nodes), msg=err_msg)
# Assert that the total number of atoms is right
self.assertEqual(sum(pack_num_nodes), sum(num_nodes), msg=err_msg)
self.assertEqual(sum(rand_pack_num_nodes), sum(num_nodes), msg=err_msg)
# Assert that all index are there
self.assertListEqual(
np.sort(np.asarray(packed_indices).flatten()).tolist(), np.arange(len(num_nodes)).tolist()
)
self.assertListEqual(
np.sort(np.asarray(rand_packed_indices).flatten()).tolist(),
np.arange(len(num_nodes)).tolist(),
)
def test_node_to_pack_indices_mask(self):
# Create a dummy batch
in_dim = 7
in_dim_edges = 11
max_num_nodes_per_graph = 20
batch_size_per_pack = 5
torch.manual_seed(42)
# Create a dummy batch of graphs
batch, all_num_nodes = [], []
for ii in range(100):
num_nodes = torch.randint(1, max_num_nodes_per_graph, (1,)).item()
all_num_nodes.append(num_nodes)
num_edges = abs(round(2.2 * num_nodes) + torch.randint(-2, 2, (1,)).item()) + 1
x = torch.randn(num_nodes, in_dim, dtype=torch.float32)
edge_idx = torch.randint(0, num_nodes, (2, num_edges))
e = torch.randn(edge_idx.shape[-1], in_dim_edges, dtype=torch.float32)
g = Data(h=x, edge_index=edge_idx, edge_attr=e)
batch.append(g)
batch = Batch.from_data_list(batch)
# Get the packing
packed_graph_idx = fast_packing(all_num_nodes, batch_size_per_pack)
pack_sizes = get_pack_sizes(packed_graph_idx, all_num_nodes)
max_pack_size = max(pack_sizes)
num_packs = len(pack_sizes)
# Get the node to pack indices and the mask
pack_from_node_idx, pack_attn_mask = node_to_pack_indices_mask(packed_graph_idx, all_num_nodes)
# Assert that the nodes to pack indices are correct
h = torch.arange(batch.num_nodes, dtype=torch.float32)
packed_shape = [num_packs, max_pack_size]
h_packed = torch.zeros(packed_shape)
h_packed[pack_from_node_idx[:, 0], pack_from_node_idx[:, 1]] = h
h_packed_unique = torch.sort(torch.unique(h_packed))[0]
np.testing.assert_array_equal(h_packed_unique, torch.arange(batch.num_nodes))
self.assertEqual(h_packed.sum(), h.sum())
# Test again with additional h dimension
h = batch.h
packed_shape = [num_packs, max_pack_size] + list(h.shape[1:])
h_packed = torch.zeros(packed_shape)
h_packed[pack_from_node_idx[:, 0], pack_from_node_idx[:, 1]] = h
h_packed_unique = torch.sort(torch.unique(h_packed))[0]
h_packed_unique = h_packed_unique[h_packed_unique != 0]
np.testing.assert_array_almost_equal(h_packed_unique, torch.unique(h))
self.assertAlmostEqual(h_packed.sum().item(), h.sum().item(), places=3)
# Assert that the mask is correct by counting the number of False values (the sum of squared number of nodes per pack)
num_false = (~pack_attn_mask).sum([1, 2])
num_expected = torch.as_tensor(
[sum([all_num_nodes[graph_idx] ** 2 for graph_idx in pack]) for pack in packed_graph_idx]
)
np.testing.assert_array_equal(num_false, num_expected)
# Assert that the mask is correct by counting the number of elements in each row and column
num_expected = []
for pack in packed_graph_idx:
pack_num_expected = []
for graph_idx in pack:
num_nodes = all_num_nodes[graph_idx]
for ii in range(num_nodes):
pack_num_expected.append(num_nodes)
pack_num_expected.extend([0] * (max_pack_size - len(pack_num_expected)))
num_expected.append(pack_num_expected)
num_expected = torch.as_tensor(num_expected)
num_false_row = (~pack_attn_mask).sum([2])
num_false_col = (~pack_attn_mask).sum([1])
np.testing.assert_array_equal(num_false_row, num_expected)
np.testing.assert_array_equal(num_false_col, num_expected)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_pe_nodepair.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the positional encodings in graphium/features/*
"""
import numpy as np
import networkx as nx
import unittest as ut
from graphium.features.electrostatic import compute_electrostatic_interactions
from graphium.features.commute import compute_commute_distances
from graphium.features.graphormer import compute_graphormer_distances
class test_positional_encodings(ut.TestCase):
# Test graphs
adj_dict = {}
max_dict = {}
# 6-ring
adj = np.asarray(
[
[0, 1, 0, 0, 0, 1],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 1, 0],
]
)
adj_dict["6-ring"] = adj
max_dict["6-ring"] = 3
# 5-path
G = nx.path_graph(5)
adj = nx.to_numpy_array(G)
adj_dict["5-path"] = adj
max_dict["5-path"] = 4
# 4-clique
adj = 1 - np.eye(4)
adj_dict["4-clique"] = adj
max_dict["4-clique"] = 1
# 4-barbell
H = nx.barbell_graph(4, 0)
adj = nx.to_numpy_array(H)
adj_dict["4-barbell"] = adj
max_dict["4-barbell"] = 3
def test_dimensions(self):
for _, adj in self.adj_dict.items():
pe, _, _ = compute_electrostatic_interactions(adj, cache={})
self.assertEqual(pe.shape, adj.shape)
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
self.assertEqual(pe.shape, adj.shape)
pe, _, _ = compute_commute_distances(adj, adj.shape[0], cache={})
self.assertEqual(pe.shape, adj.shape)
def test_symmetry(self):
for _, adj in self.adj_dict.items():
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe, pe.T)
pe, _, _ = compute_commute_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe, pe.T)
def test_max_dist(self):
for key, adj in self.adj_dict.items():
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe.max(), self.max_dict[key])
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_pe_rw.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the positional encodings in graphium/features/*
"""
import numpy as np
import networkx as nx
import unittest as ut
from graphium.features.rw import compute_rwse
class test_pe_spectral(ut.TestCase):
def test_caching_and_outputs(self):
# 4-barbell
G = nx.barbell_graph(4, 0)
adj = nx.to_numpy_array(G)
num_nodes = adj.shape[0]
cache = {}
ksteps1 = [4, 6]
ksteps2 = [2]
ksteps3 = [6, 7]
pe1, _, cache = compute_rwse(
adj.astype(np.float32), ksteps1, num_nodes, cache, pos_type="rw_transition_probs"
)
pe2, _, cache = compute_rwse(
adj.astype(np.float32), ksteps2, num_nodes, cache, pos_type="rw_return_probs"
)
pe3, _, cache = compute_rwse(
adj.astype(np.float32), ksteps3, num_nodes, cache, pos_type="rw_return_probs"
)
self.assertTrue(all([k in cache["ksteps"] for k in ksteps1 + ksteps2 + ksteps3]))
self.assertTrue(pe1.shape, np.zeros((num_nodes, num_nodes, len(ksteps1))))
self.assertTrue(pe2.shape, np.zeros((num_nodes, len(ksteps2))))
self.assertTrue(pe3.shape, np.zeros((num_nodes, len(ksteps3))))
for i in range(len(ksteps1)):
np.testing.assert_array_almost_equal(pe1[..., i].sum(1), np.ones(num_nodes))
self.assertGreaterEqual(pe1.min(), 0.0)
self.assertLessEqual(pe1.max(), 1.0)
self.assertGreaterEqual(pe2.min(), 0.0)
self.assertLessEqual(pe2.max(), 1.0)
self.assertGreaterEqual(pe3.min(), 0.0)
self.assertLessEqual(pe3.max(), 1.0)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_pe_spectral.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the positional encodings in graphium/features/*
"""
import numpy as np
import networkx as nx
import unittest as ut
from graphium.features.spectral import compute_laplacian_pe
class test_pe_spectral(ut.TestCase):
# 2 disconnected 3 cliques
adj1 = np.zeros((6, 6))
adj_3clq = 1 - np.eye(3)
adj1[:3, :3] = adj_3clq
adj1[3:, 3:] = adj_3clq
# 3-clique
adj2 = 1 - np.eye(6)
def test_for_connected_vs_disconnected_graph(self):
num_pos = 3
# test if pe works identically on connected vs disconnected graphs
eigvals_pe1, _, _, cache = compute_laplacian_pe(self.adj1, num_pos, cache={})
eigvals_pe1 = np.real(eigvals_pe1).astype(np.float32)
_, eigvecs_pe1, _, _ = compute_laplacian_pe(self.adj1, num_pos, cache=cache)
# We expect to cache 4 objects in when running the functon for the first time
self.assertEqual(len(cache.keys()), 4)
eigvals_pe2, _, _, _ = compute_laplacian_pe(self.adj2, num_pos, cache={})
eigvals_pe2 = np.real(eigvals_pe2).astype(np.float32)
_, eigvecs_pe2, _, _ = compute_laplacian_pe(self.adj2, num_pos, cache={})
np.testing.assert_array_almost_equal(2 * eigvals_pe1, eigvals_pe2)
self.assertListEqual(list(eigvals_pe2.shape), [self.adj2.shape[0], num_pos])
self.assertListEqual(list(eigvecs_pe2.shape), [self.adj2.shape[0], num_pos])
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_pos_transfer_funcs.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the positional encodings in graphium/features/*
"""
import numpy as np
import networkx as nx
import unittest as ut
from graphium.features.spectral import compute_laplacian_pe
from graphium.features.transfer_pos_level import (
node_to_edge,
node_to_nodepair,
edge_to_nodepair,
nodepair_to_node,
nodepair_to_edge,
graph_to_node,
)
class test_pos_transfer_funcs(ut.TestCase):
# 4-barbell
G = nx.barbell_graph(4, 0)
adj = nx.to_numpy_array(G)
num_nodes, num_feat = 8, 5
node_pe = np.random.rand(num_nodes, num_feat)
def test_different_pathways_from_node_to_edge(self):
edge_pe1, _ = node_to_edge(self.node_pe, self.adj, {})
nodepair_pe1 = node_to_nodepair(self.node_pe, self.num_nodes)
edge_pe2, _ = nodepair_to_edge(nodepair_pe1, self.adj, {})
nodepair_pe2, _ = edge_to_nodepair(edge_pe1, self.adj, self.num_nodes, {})
edge_pe3, _ = nodepair_to_edge(nodepair_pe2, self.adj, {})
np.testing.assert_array_almost_equal(edge_pe1, edge_pe2)
np.testing.assert_array_almost_equal(edge_pe1, edge_pe3)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_positional_encoders.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different datasets of graphium/features/featurizer.py
"""
import numpy as np
import unittest as ut
from copy import deepcopy
from rdkit import Chem
import datamol as dm
import torch
from scipy.sparse import coo_matrix
from graphium.features.featurizer import GraphDict
from graphium.features.positional_encoding import graph_positional_encoder
from graphium.nn.encoders import laplace_pos_encoder, mlp_encoder, signnet_pos_encoder
# TODO: Test the MLP_encoder and signnet_pos_encoder
class test_positional_encoder(ut.TestCase):
smiles = [
"C",
"CC",
"CC.CCCC",
"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",
"OCCc1c(C)[n+](cs1)Cc2cnc(C)nc2N",
"O1C=C[C@H]([C@H]1O2)c3c2cc(OC)c4c3OC(=O)C5=C4CCC(=O)5",
]
mols = [dm.to_mol(s) for s in smiles]
adjs = [Chem.rdmolops.GetAdjacencyMatrix(mol) for mol in mols]
def test_laplacian_eigvec_eigval(self):
for ii, adj in enumerate(deepcopy(self.adjs)):
for num_pos in [1, 2, 4]: # Can't test too much eigs because of multiplicities
for disconnected_comp in [True, False]:
err_msg = f"adj_id={ii}, num_pos={num_pos}, disconnected_comp={disconnected_comp}"
# returns a dictionary of computed pe
pos_kwargs = {
"pos_type": "laplacian_eigvec",
"num_pos": num_pos,
"disconnected_comp": disconnected_comp,
"pos_level": "node",
}
num_nodes = adj.shape[0]
eigvecs, cache = graph_positional_encoder(adj, num_nodes, pos_kwargs=pos_kwargs)
pos_kwargs["pos_type"] = "laplacian_eigval"
eigvals, cache = graph_positional_encoder(adj, num_nodes, pos_kwargs=pos_kwargs)
self.assertEqual(list(eigvecs.shape), [adj.shape[0], num_pos], msg=err_msg)
self.assertEqual(list(eigvals.shape), [adj.shape[0], num_pos], msg=err_msg)
# Compute eigvals and eigvecs
lap = np.diag(np.sum(adj, axis=1)) - adj
true_eigvals, true_eigvecs = np.linalg.eig(lap)
sort_idx = np.argsort(true_eigvals)
true_eigvals, true_eigvecs = true_eigvals[sort_idx], true_eigvecs[:, sort_idx]
true_eigvecs = true_eigvecs / (np.sum(true_eigvecs**2, axis=0, keepdims=True) + 1e-8)
true_num_pos = min(num_pos, len(true_eigvals))
true_eigvals, true_eigvecs = true_eigvals[:true_num_pos], true_eigvecs[:, :true_num_pos]
if not ("." in self.smiles[ii]):
np.testing.assert_array_almost_equal(
np.abs(true_eigvecs),
np.abs(eigvecs[:, :true_num_pos]),
decimal=6,
err_msg=err_msg,
)
self.assertAlmostEqual(np.sum(true_eigvecs[:, 1:]), 0, places=6, msg=err_msg)
np.testing.assert_array_almost_equal(
true_eigvals, eigvals[0, :true_num_pos], decimal=6, err_msg=err_msg
)
# didn't actually check the exact computation result because the code was adapted
def test_rwse(self):
for ii, adj in enumerate(deepcopy(self.adjs)):
for ksteps in [1, 2, 4]:
err_msg = f"adj_id={ii}, ksteps={ksteps}"
num_nodes = adj.shape[0]
pos_kwargs = {"pos_type": "rw_return_probs", "ksteps": ksteps, "pos_level": "node"}
rwse_embed, cache = graph_positional_encoder(adj, num_nodes, pos_kwargs=pos_kwargs)
self.assertEqual(list(rwse_embed.shape), [num_nodes, ksteps], msg=err_msg)
# TODO: work in progress
"""
continue debugging here, see how to adapt the laplace_pos_encoder
code running now, question is where to add the laplace_pos_encoder
"""
def test_laplacian_eigvec_with_encoder(self):
for ii, adj in enumerate(deepcopy(self.adjs)):
for num_pos in [2, 4, 8]: # Can't test too much eigs because of multiplicities
for disconnected_comp in [True, False]:
for model_type in ["Transformer", "DeepSet", "MLP"]:
err_msg = f"adj_id={ii}, num_pos={num_pos}, disconnected_comp={disconnected_comp}"
# returns a dictionary of computed pe
pos_kwargs = {
"pos_type": "laplacian_eigvec",
"num_pos": num_pos,
"disconnected_comp": disconnected_comp,
"pos_level": "node",
}
num_nodes = adj.shape[0]
eigvecs, cache = graph_positional_encoder(adj, num_nodes, pos_kwargs=pos_kwargs)
pos_kwargs["pos_type"] = "laplacian_eigval"
eigvals, cache = graph_positional_encoder(adj, num_nodes, pos_kwargs=pos_kwargs)
input_keys = ["laplacian_eigvec", "laplacian_eigval"]
in_dim = num_pos
hidden_dim = 64
out_dim = 64
num_layers = 1
eigvecs = torch.from_numpy(eigvecs)
eigvals = torch.from_numpy(eigvals)
g = GraphDict(
{
"adj": coo_matrix(adj),
"data": {"laplacian_eigval": eigvals, "laplacian_eigvec": eigvecs},
}
)
batch = g.make_pyg_graph()
encoder = laplace_pos_encoder.LapPENodeEncoder(
input_keys=input_keys,
output_keys=["node"],
in_dim=in_dim, # Size of Laplace PE embedding
hidden_dim=hidden_dim,
out_dim=out_dim,
model_type=model_type, # 'Transformer' or 'DeepSet'
num_layers=num_layers,
num_layers_post=2, # Num. layers to apply after pooling
dropout=0.1,
first_normalization=None,
)
hidden_embed = encoder(batch, key_prefix=None)
assert "node" in hidden_embed.keys()
self.assertEqual(list(hidden_embed["node"].shape), [num_nodes, out_dim], msg=err_msg)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_positional_encodings.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the positional encodings in graphium/features/*
"""
import numpy as np
import networkx as nx
import unittest as ut
# from graphium.features.spectral import compute_laplacian_positional_eigvecs # TODO: add tests
# from graphium.features.rw import compute_rwse # TODO: add tests
from graphium.features.electrostatic import compute_electrostatic_interactions
from graphium.features.commute import compute_commute_distances
from graphium.features.graphormer import compute_graphormer_distances
class test_positional_encodings(ut.TestCase):
# Test graphs
adj_dict = {}
max_dict = {}
# 6-ring
adj = np.asarray(
[
[0, 1, 0, 0, 0, 1],
[1, 0, 1, 0, 0, 0],
[0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 1, 0],
]
)
adj_dict["6-ring"] = adj
max_dict["6-ring"] = 3
# 5-path
G = nx.path_graph(5)
adj = nx.to_numpy_array(G)
adj_dict["5-path"] = adj
max_dict["5-path"] = 4
# 4-clique
adj = 1 - np.eye(4)
adj_dict["4-clique"] = adj
max_dict["4-clique"] = 1
# 4-barbell
H = nx.barbell_graph(4, 0)
adj = nx.to_numpy_array(H)
adj_dict["4-barbell"] = adj
max_dict["4-barbell"] = 3
def test_dimensions(self):
for _, adj in self.adj_dict.items():
pe, _, _ = compute_electrostatic_interactions(adj, cache={})
self.assertEqual(pe.shape, adj.shape)
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
self.assertEqual(pe.shape, adj.shape)
pe, _, _ = compute_commute_distances(adj, adj.shape[0], cache={})
self.assertEqual(pe.shape, adj.shape)
def test_symmetry(self):
for _, adj in self.adj_dict.items():
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe, pe.T)
pe, _, _ = compute_commute_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe, pe.T)
def test_max_dist(self):
for key, adj in self.adj_dict.items():
pe, _, _ = compute_graphormer_distances(adj, adj.shape[0], cache={})
np.testing.assert_array_almost_equal(pe.max(), self.max_dict[key])
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_predictor.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the file graphium/trainer/predictor.py
"""
import torch
from torch.nn import BCELoss, MSELoss
import unittest as ut
from graphium.trainer.predictor_options import EvalOptions
class test_Predictor(ut.TestCase):
def test_parse_loss_fun(self):
losses = ["bce", "mse", "mae", "l1", BCELoss(), MSELoss()]
preds = torch.rand(10, 5)
target = (torch.rand(10, 5) > 0.5).to(preds.dtype)
for this_loss in losses:
loss_fun = EvalOptions.parse_loss_fun(this_loss)
loss = loss_fun(preds, target)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_pyg_layers.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the different layers of graphium/nn/pyg_layers/...
The layers are not thoroughly tested due to the difficulty of testing them
"""
import numpy as np
import torch
import unittest as ut
from torch_geometric.data import Data, Batch
from copy import deepcopy
import pytest
from graphium.nn.pyg_layers import (
GINConvPyg,
GINEConvPyg,
MPNNPlusPyg,
GatedGCNPyg,
PNAMessagePassingPyg,
GPSLayerPyg,
VirtualNodePyg,
DimeNetPyg,
)
from graphium.nn.pyg_layers.utils import (
PreprocessPositions,
GaussianLayer,
)
class test_Pyg_Layers(ut.TestCase):
in_dim = 21
out_dim = 11
in_dim_edges = 13
out_dim_edges = 17
edge_idx1 = torch.stack([torch.tensor([0, 1, 2, 3, 2]), torch.tensor([1, 2, 3, 0, 0])])
edge_idx2 = torch.stack([torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])])
x1 = torch.randn(edge_idx1.max() + 1, in_dim, dtype=torch.float32)
e1 = torch.randn(edge_idx1.shape[-1], in_dim_edges, dtype=torch.float32)
x2 = torch.randn(edge_idx2.max() + 1, in_dim, dtype=torch.float32)
e2 = torch.randn(edge_idx2.shape[-1], in_dim_edges, dtype=torch.float32)
# edge_idx1, e1 = add_self_loops(edge_idx1, e1)
# edge_idx2, e2 = add_self_loops(edge_idx2, e2)
g1 = Data(feat=x1, edge_index=edge_idx1, edge_feat=e1)
g2 = Data(feat=x2, edge_index=edge_idx2, edge_feat=e2)
bg = Batch.from_data_list([g1, g2])
kwargs = {
"activation": "relu",
"dropout": 0.1,
"normalization": "batch_norm",
"droppath_rate": 0.1,
"layer_idx": 1,
"layer_depth": 10,
}
def test_gpslayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
kwargs = deepcopy(self.kwargs)
kwargs.pop("droppath_rate")
kwargs["droppath_rate_attn"] = 0.2
kwargs["droppath_rate_ffn"] = 0.3
kwargs["mpnn_kwargs"] = {"droppath_rate_ffn": 0.4}
layer = GPSLayerPyg(in_dim=self.in_dim, out_dim=self.out_dim, **kwargs)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertTrue(layer.layer_inputs_edges)
self.assertFalse(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer. Should crash due to different dim for nodes vs edges
with self.assertRaises(ValueError):
bg = layer.forward(bg)
# Create new edge attributes with same dim and check that it works
bg.edge_feat = torch.zeros((bg.edge_feat.shape[0], self.in_dim), dtype=torch.float32)
bg = layer.forward(bg)
self.assertEqual(bg.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg.feat.shape[1], self.out_dim * layer.out_dim_factor)
def test_ginlayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
layer = GINConvPyg(in_dim=self.in_dim, out_dim=self.out_dim, **self.kwargs)
# Check the re-implementation of abstract methods
self.assertFalse(layer.layer_supports_edges)
self.assertFalse(layer.layer_inputs_edges)
self.assertFalse(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer
bg = layer.forward(bg)
self.assertEqual(bg.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg.feat.shape[1], self.out_dim * layer.out_dim_factor)
def test_ginelayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
layer = GINEConvPyg(in_dim=self.in_dim, out_dim=self.out_dim, **self.kwargs)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertTrue(layer.layer_inputs_edges)
self.assertFalse(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer. Should crash due to different dim for nodes vs edges
with self.assertRaises(ValueError):
bg = layer.forward(bg)
# Create new edge attributes with same dim and check that it works
bg.edge_feat = torch.zeros((bg.edge_feat.shape[0], self.in_dim), dtype=torch.float32)
bg = layer.forward(bg)
self.assertEqual(bg.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg.feat.shape[1], self.out_dim * layer.out_dim_factor)
def test_mpnnlayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
# need in_dim = out_dim for skip connection
# mpnn layer accept different dimension for node and edge features
layer = MPNNPlusPyg(
in_dim=self.in_dim,
out_dim=self.in_dim,
use_edges=True,
in_dim_edges=self.in_dim_edges,
out_dim_edges=self.in_dim_edges,
**self.kwargs,
)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertTrue(layer.layer_inputs_edges)
self.assertTrue(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Create new edge attributes with same dim and check that it works
bg.edge_feat = torch.zeros((bg.edge_feat.shape[0], self.in_dim_edges), dtype=torch.float32)
bg = layer.forward(bg)
self.assertEqual(bg.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg.feat.shape[1], self.in_dim * layer.out_dim_factor)
def test_gatedgcnlayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
e_in = bg.edge_feat
layer = GatedGCNPyg(
in_dim=self.in_dim,
out_dim=self.out_dim,
in_dim_edges=self.in_dim_edges,
out_dim_edges=self.out_dim_edges,
**self.kwargs,
)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertTrue(layer.layer_inputs_edges)
self.assertTrue(layer.layer_outputs_edges)
self.assertIsInstance(layer.layer_supports_edges, bool)
self.assertIsInstance(layer.layer_inputs_edges, bool)
self.assertIsInstance(layer.layer_outputs_edges, bool)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer with edges
bg2 = layer.forward(bg)
self.assertEqual(bg2.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg2.feat.shape[1], self.out_dim * layer.out_dim_factor)
def test_pnamessagepassinglayer(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
aggregators = ["mean", "max", "min", "std", "sum"]
scalers = ["identity", "amplification", "attenuation"]
layer = PNAMessagePassingPyg(
in_dim=self.in_dim, out_dim=self.out_dim, aggregators=aggregators, scalers=scalers, **self.kwargs
)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertIsInstance(layer.layer_supports_edges, bool)
# self.assertFalse(layer.layer_inputs_edges)
self.assertFalse(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer
bg2 = layer.forward(bg)
self.assertEqual(bg2.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg2.feat.shape[1], self.out_dim * layer.out_dim_factor)
# Now try with edges
bg = deepcopy(self.bg)
layer = PNAMessagePassingPyg(
in_dim=self.in_dim,
out_dim=self.out_dim,
aggregators=aggregators,
scalers=scalers,
in_dim_edges=self.in_dim_edges,
**self.kwargs,
)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
# self.assertTrue(layer.layer_inputs_edges)
self.assertIsInstance(layer.layer_supports_edges, bool)
# self.assertIsInstance(layer.layer_inputs_edges, bool)
self.assertFalse(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
bg2 = layer.forward(bg)
self.assertEqual(bg2.feat.shape[0], feat_in.shape[0])
self.assertEqual(bg2.feat.shape[1], self.out_dim * layer.out_dim_factor)
self.assertTrue((bg2.edge_feat == self.bg.edge_feat).all)
@pytest.mark.skip_ipu
def test_dimenetlayer(self):
from graphium.nn.encoders.bessel_pos_encoder import BesselSphericalPosEncoder
bg = deepcopy(self.bg)
# dummy input pos
bg.pos = torch.randn((bg.feat.shape[0], 3), dtype=torch.float32)
# position 3d encoder
pos_enc = BesselSphericalPosEncoder(
input_keys=["pos"],
output_keys=[
"node_feat",
"edge_feat",
"edge_rbf",
"triplet_sbf",
"radius_edge_index",
], # The keys to return
in_dim=3,
out_dim=self.in_dim,
out_dim_edges=self.in_dim_edges,
num_output_layers=2,
num_layers=2,
num_spherical=4,
num_radial=32,
)
enc_output = pos_enc(bg, None) # forward requires: pos, edge_index, batch
bg.feat = bg.feat + enc_output["node_feat"] # [num_nodes, in_dim]
bg.edge_feat = enc_output["edge_feat"] # [num_edges, out_dim_edges]
bg.radius_edge_index = enc_output["radius_edge_index"] # [2, num_edges]
bg.edge_rbf = enc_output["edge_rbf"] # [num_edges, num_radial]
bg.triplet_sbf = enc_output["triplet_sbf"] # [num_triplets, num_spherical * num_radial]
kwargs = deepcopy(self.kwargs)
kwargs["num_bilinear"] = 32
kwargs["num_spherical"] = 4
kwargs["num_radial"] = 32
layer = DimeNetPyg(
in_dim=self.in_dim,
out_dim=self.out_dim,
in_dim_edges=self.in_dim_edges,
out_dim_edges=self.out_dim_edges,
**kwargs,
)
# Check the re-implementation of abstract methods
self.assertTrue(layer.layer_supports_edges)
self.assertTrue(layer.layer_inputs_edges)
self.assertTrue(layer.layer_outputs_edges)
self.assertEqual(layer.out_dim_factor, 1)
# Apply layer with encoded feats as input
bg2 = layer.forward(bg)
# output sanity check
self.assertEqual(bg2.edge_feat.shape, bg.edge_feat.shape)
self.assertEqual(bg2.edge_feat.shape[1], self.out_dim_edges)
self.assertEqual(bg2.feat.shape[1], self.out_dim)
# not change rbf/sbf embedding
self.assertTrue((bg2.edge_rbf == bg.edge_rbf).all)
self.assertTrue((bg2.triplet_sbf == bg.triplet_sbf).all)
def test_preprocess3Dfeaturelayer(self):
bg = deepcopy(self.bg)
num_heads = 2
num_kernel = 2
in_dim = 3
bg.positions_3d = torch.zeros(bg.feat.size()[0], in_dim)
layer = PreprocessPositions(
num_heads=num_heads,
embed_dim=self.out_dim,
num_kernel=num_kernel,
in_dim=in_dim,
first_normalization="layer_norm",
)
# bias: [batch, num_heads, nodes, nodes]
# node_feature: [total_nodes, embed_dim]
bias, node_feature = layer.forward(
bg, max_num_nodes_per_graph=4, on_ipu=False, positions_3d_key="positions_3d"
)
self.assertEqual(bias.size(), torch.Size([2, num_heads, 4, 4]))
self.assertFalse(np.isnan(bias.detach().numpy()).any())
self.assertEqual(node_feature.size(), torch.Size([7, self.out_dim]))
def test_gaussianlayer(self):
num_kernels = 3
input = torch.zeros(2, 4, 4)
layer = GaussianLayer(num_kernels=num_kernels)
# tensor_with_kernel: [batch, nodes, nodes, num_kernel]
tensor_with_kernel = layer.forward(input)
self.assertEqual(tensor_with_kernel.size(), torch.Size([2, 4, 4, 3]))
def test_pooling_virtual_node(self):
bg = deepcopy(self.bg)
feat_in = bg.feat
e_in = bg.edge_feat
vn_h = 0
vn_types = ["sum", "mean"]
for vn_type, use_edges, expected_v_node in zip(vn_types, [False, True], [(2, 21), (2, 34)]):
with self.subTest(
vn_type=vn_type,
use_edges=use_edges,
expected_v_node=expected_v_node,
):
vn_h = 0.0
print(vn_h, self.out_dim, feat_in.size())
layer = VirtualNodePyg(
in_dim=self.in_dim,
out_dim=self.in_dim,
in_dim_edges=self.in_dim_edges,
out_dim_edges=self.in_dim_edges,
vn_type=vn_type,
use_edges=use_edges,
**self.kwargs,
)
feat_out, vn_out, e_out = layer.forward(bg, feat_in, vn_h, edge_feat=e_in)
assert vn_out.shape == expected_v_node
assert feat_out.shape == (7, 21)
assert e_out.shape == (9, 13)
if use_edges is False:
# i.e. that the node features have been updated
assert torch.equal(feat_out, feat_in) == False
# And that the edge features have not
assert torch.equal(e_out, e_in) == True
else:
assert torch.equal(feat_out, feat_in) == False
assert torch.equal(e_out, e_in) == False
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_residual_connections.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the file residual_connections.py
"""
import numpy as np
import torch
import unittest as ut
from graphium.nn.residual_connections import (
ResidualConnectionConcat,
ResidualConnectionDenseNet,
ResidualConnectionNone,
ResidualConnectionSimple,
ResidualConnectionWeighted,
ResidualConnectionRandom,
)
class test_ResidualConnectionNone(ut.TestCase):
def test_get_true_out_dims_none(self):
full_dims = [4, 6, 8, 10, 12]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
rc = ResidualConnectionNone(skip_steps=1)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = out_dims[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
def test_forward_none(self):
rc = ResidualConnectionNone(skip_steps=1)
num_loops = 10
shape = (3, 11)
h_original = [torch.rand(shape) for _ in range(num_loops)]
h_prev = None
for ii in range(num_loops):
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
np.testing.assert_array_equal(feat.numpy(), h_original[ii].numpy(), err_msg=f"ii={ii}")
self.assertIsNone(h_prev)
class test_ResidualConnectionSimple(ut.TestCase):
def test_get_true_out_dims_simple(self):
full_dims = [4, 6, 8, 10, 12]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
rc = ResidualConnectionSimple(skip_steps=1)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = out_dims[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
def test_forward_simple(self):
for skip_steps in [1, 2, 3]:
rc = ResidualConnectionSimple(skip_steps=skip_steps)
num_loops = 10
shape = (3, 11)
h_original = [torch.ones(shape) * (ii + 1) for ii in range(num_loops)]
h_prev = None
for ii in range(num_loops):
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
if ((ii % skip_steps) == 0) and (ii > 0):
h_expected = (
torch.sum(torch.stack(h_original[0 : ii + 1 : skip_steps], dim=0), dim=0)
).numpy()
h_expected_prev = h_expected
else:
h_expected = h_original[ii].numpy()
if ii == 0:
h_expected_prev = h_expected
np.testing.assert_array_equal(
feat.numpy(), h_expected, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
np.testing.assert_array_equal(
h_prev.numpy(), h_expected_prev, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
class test_ResidualConnectionRandom(ut.TestCase):
def test_get_true_out_dims_random(self):
full_dims = [4, 6, 8, 10, 12]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
rc = ResidualConnectionRandom(skip_steps=1, out_dims=out_dims)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = out_dims[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
rc = ResidualConnectionRandom(skip_steps=1, num_layers=len(out_dims))
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = out_dims[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
with self.assertRaises(ValueError):
rc = ResidualConnectionRandom(skip_steps=1, out_dims=None, num_layers=None)
def test_forward_random(self):
for skip_steps in [1, 2, 3]:
num_loops = 10
if skip_steps > 1:
with self.assertRaises(ValueError):
rc = ResidualConnectionRandom(skip_steps=skip_steps, num_layers=num_loops)
continue
rc = ResidualConnectionRandom(skip_steps=skip_steps, num_layers=num_loops + 1)
shape = (3, 11)
h_original = [torch.ones(shape) * (ii + 1) for ii in range(num_loops)]
h_prev = None
# Not really testing the expected values due to randomness, just testing if it runs
for ii in range(num_loops):
print(ii)
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
class test_ResidualConnectionWeighted(ut.TestCase):
def test_get_true_out_dims_weighted(self):
full_dims = [4, 6, 8, 10, 12]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
rc = ResidualConnectionWeighted(skip_steps=1, out_dims=full_dims[1:])
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = out_dims[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
def test_forward_weighted(self):
for skip_steps in [1, 2, 3]:
num_loops = 10
shape = (3, 11)
full_dims = [shape[1]] * (num_loops + 1)
rc = ResidualConnectionWeighted(
skip_steps=skip_steps,
out_dims=full_dims[1:],
activation="none",
normalization="none",
bias=False,
)
h_original = [torch.ones(shape) * (ii + 1) for ii in range(num_loops)]
h_forward = []
h_prev = None
step_counter = 0
for ii in range(num_loops):
h_prev_backup = h_prev
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
if ((ii % skip_steps) == 0) and (ii > 0):
h_forward.append(rc.residual_list[step_counter].forward(h_prev_backup))
h_expected = (h_forward[-1] + h_original[ii]).detach().numpy()
h_expected_prev = h_expected
step_counter += 1
else:
h_expected = h_original[ii].detach().numpy()
if ii == 0:
h_expected_prev = h_expected
np.testing.assert_array_equal(
feat.detach().numpy(), h_expected, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
np.testing.assert_array_equal(
h_prev.detach().numpy(),
h_expected_prev,
err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}",
)
class test_ResidualConnectionConcat(ut.TestCase):
def test_get_true_out_dims_concat(self):
full_dims = [4, 6, 8, 10, 12, 14, 16, 18, 20]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
# skip_steps=1
rc = ResidualConnectionConcat(skip_steps=1)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = [6, 14, 18, 22, 26, 30, 34]
self.assertListEqual(expected_out_dims, true_out_dims)
# skip_steps=2
rc = ResidualConnectionConcat(skip_steps=2)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = [6, 8, 16, 12, 24, 16, 32]
self.assertListEqual(expected_out_dims, true_out_dims)
# skip_steps=3
rc = ResidualConnectionConcat(skip_steps=3)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = [6, 8, 10, 18, 14, 16, 30]
self.assertListEqual(expected_out_dims, true_out_dims)
def test_forward_concat(self):
for skip_steps in [1, 2, 3]:
rc = ResidualConnectionConcat(skip_steps=skip_steps)
num_loops = 10
shape = (3, 11)
h_original = [torch.ones(shape) * (ii + 1) for ii in range(num_loops)]
h_prev = None
for ii in range(num_loops):
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
if ((ii % skip_steps) == 0) and (ii > 0):
h_expected = (
torch.cat(h_original[ii - skip_steps : ii + 1 : skip_steps][::-1], dim=-1)
).numpy()
h_expected_prev = h_original[ii].numpy()
else:
h_expected = h_original[ii].numpy()
if ii == 0:
h_expected_prev = h_expected
np.testing.assert_array_equal(
feat.numpy(), h_expected, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
np.testing.assert_array_equal(
h_prev.numpy(), h_expected_prev, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
class test_ResidualConnectionDenseNet(ut.TestCase):
def test_get_true_out_dims_densenet(self):
full_dims = [4, 6, 8, 10, 12, 14, 16, 18, 20]
in_dims, out_dims = full_dims[:-1], full_dims[1:]
# skip_steps=1
rc = ResidualConnectionDenseNet(skip_steps=1)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = np.cumsum(out_dims).tolist()[:-1]
self.assertListEqual(expected_out_dims, true_out_dims)
# skip_steps=2
rc = ResidualConnectionDenseNet(skip_steps=2)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = [6, 8, 10 + 6, 12, 14 + 10 + 6, 16, 18 + 14 + 10 + 6]
self.assertListEqual(expected_out_dims, true_out_dims)
# skip_steps=3
rc = ResidualConnectionDenseNet(skip_steps=3)
true_out_dims = rc.get_true_out_dims(out_dims)
expected_out_dims = [6, 8, 10, 12 + 6, 14, 16, 18 + 12 + 6]
self.assertListEqual(expected_out_dims, true_out_dims)
def test_forward_densenet(self):
for skip_steps in [1, 2, 3]:
rc = ResidualConnectionDenseNet(skip_steps=skip_steps)
num_loops = 10
shape = (3, 11)
h_original = [torch.ones(shape) * (ii + 1) for ii in range(num_loops)]
h_prev = None
for ii in range(num_loops):
feat, h_prev = rc.forward(h_original[ii], h_prev, step_idx=ii)
if ((ii % skip_steps) == 0) and (ii > 0):
h_expected = (torch.cat(h_original[0 : ii + 1 : skip_steps][::-1], dim=-1)).numpy()
h_expected_prev = h_expected
else:
h_expected = h_original[ii].numpy()
if ii == 0:
h_expected_prev = h_expected
np.testing.assert_array_equal(
feat.numpy(), h_expected, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
np.testing.assert_array_equal(
h_prev.numpy(), h_expected_prev, err_msg=f"Error at: skip_steps={skip_steps}, ii={ii}"
)
if __name__ == "__main__":
ut.main()
================================================
FILE: tests/test_training.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
import pytest
from graphium.cli.train_finetune_test import cli
import sys
import subprocess
import os
from unittest.mock import patch
class TestCLITraining:
@classmethod
def setup_class(cls):
print("Setting up the test class...")
# Equivalent of the bash commands to download the data files
toymix_dir = "expts/data/neurips2023/small-dataset/"
subprocess.run(["mkdir", "-p", toymix_dir])
base_url = "https://storage.valencelabs.com/graphium/datasets/neurips_2023/Small-dataset/"
files = [
"ZINC12k.csv.gz",
"Tox21-7k-12-labels.csv.gz",
"qm9.csv.gz",
"qm9_random_splits.pt",
"Tox21_random_splits.pt",
"ZINC12k_random_splits.pt",
]
for file in files:
file_path = f"{toymix_dir}{file}"
if not os.path.exists(file_path):
print(f"Downloading {file}...")
subprocess.run(["wget", "-P", toymix_dir, f"{base_url}{file}"])
else:
print(f"{file} already exists. Skipping...")
print("Data has been successfully downloaded.")
def call_cli_with_overrides(self, acc_type: str, acc_prec: str, load_type: str) -> None:
overrides = [
f"accelerator={acc_type}",
"tasks=toymix",
"training=toymix",
# Reducing number of parameters in the toymix architecture
"architecture=toymix",
"architecture.pe_encoders.encoders.la_pos.hidden_dim=16",
"architecture.pe_encoders.encoders.la_pos.num_layers=1",
"architecture.pe_encoders.encoders.rw_pos.hidden_dim=16",
"architecture.pe_encoders.encoders.rw_pos.num_layers=1",
"architecture.pre_nn.hidden_dims=32",
"architecture.pre_nn.depth=1",
"architecture.pre_nn.out_dim=16",
"architecture.gnn.in_dim=16",
"architecture.gnn.out_dim=16",
"architecture.gnn.depth=2",
"architecture.task_heads.qm9.depth=1",
"architecture.task_heads.tox21.depth=1",
"architecture.task_heads.zinc.depth=1",
# Set the number of epochs
"constants.max_epochs=2",
"+datamodule.args.task_specific_args.qm9.sample_size=1000",
"+datamodule.args.task_specific_args.tox21.sample_size=1000",
"+datamodule.args.task_specific_args.zinc.sample_size=1000",
"trainer.trainer.check_val_every_n_epoch=1",
f"trainer.trainer.precision={acc_prec}",
f"datamodule.args.dataloading_from={load_type}",
]
if acc_type == "ipu":
overrides.append("accelerator.ipu_config=['useIpuModel(True)']")
overrides.append("accelerator.ipu_inference_config=['useIpuModel(True)']")
# Backup the original sys.argv
original_argv = sys.argv.copy()
# Replace sys.argv with the desired overrides
hydra_overrides = ["script_name"] + overrides
sys.argv = hydra_overrides
# Call the function
cli()
# Restore the original sys.argv
sys.argv = original_argv
@pytest.mark.parametrize("load_type", ["RAM", "disk"])
def test_cpu_cli_training(self, load_type):
self.call_cli_with_overrides("cpu", "32", load_type)
@pytest.mark.ipu
@pytest.mark.skip
@pytest.mark.parametrize("load_type", ["RAM", "disk"])
def test_ipu_cli_training(self, load_type):
with patch("poptorch.ipuHardwareIsAvailable", return_value=True):
with patch("lightning_graphcore.accelerator._IPU_AVAILABLE", new=True):
import poptorch
assert poptorch.ipuHardwareIsAvailable()
from lightning_graphcore.accelerator import _IPU_AVAILABLE
assert _IPU_AVAILABLE is True
self.call_cli_with_overrides("ipu", "16-true", load_type)
================================================
FILE: tests/test_utils.py
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
"""
Unit tests for the metrics and wrappers of graphium/utils/...
"""
import torch
import numpy as np
import scipy as sp
import unittest as ut
import gzip
from graphium.utils.read_file import file_opener
from graphium.utils.tensor import (
nan_mad,
nan_mean,
nan_std,
nan_var,
nan_median,
dict_tensor_fp16_to_fp32,
tensor_fp16_to_fp32,
)
from graphium.utils.safe_run import SafeRun
class test_nan_statistics(ut.TestCase):
torch.manual_seed(42)
dims = [
None,
(0),
(1),
(2),
(-1),
(-2),
(-3),
(0, 1),
(0, 2),
]
# Create tensor
sz = (10, 6, 7)
tensor = torch.randn(sz, dtype=torch.float32) ** 2 + 3
is_nan = torch.rand(sz) > 0.4
tensor[is_nan] = float("nan")
def test_nan_mean(self):
for keepdim in [False, True]:
for dim in self.dims:
err_msg = f"Error for :\n dim = {dim}\n keepdim = {keepdim}"
tensor = self.tensor.clone()
# Prepare the arguments for numpy vs torch
if dim is not None:
torch_kwargs = {"dim": dim, "keepdim": keepdim}
numpy_kwargs = {"axis": dim, "keepdims": keepdim}
else:
torch_kwargs = {}
numpy_kwargs = {}
# Compare the nan-mean
torch_mean = nan_mean(tensor, **torch_kwargs)
numpy_mean = np.nanmean(tensor.numpy(), **numpy_kwargs)
np.testing.assert_almost_equal(torch_mean.numpy(), numpy_mean, decimal=6, err_msg=err_msg)
def test_nan_std_var(self):
for unbiased in [True, False]:
for keepdim in [False, True]:
for dim in self.dims:
err_msg = f"Error for :\n\tdim = {dim}\n\tkeepdim = {keepdim}\n\tunbiased = {unbiased}"
tensor = self.tensor.clone()
# Prepare the arguments for numpy vs torch
if dim is not None:
torch_kwargs = {"dim": dim, "keepdim": keepdim, "unbiased": unbiased}
numpy_kwargs = {"axis": dim, "keepdims": keepdim, "ddof": float(unbiased)}
else:
torch_kwargs = {"unbiased": unbiased}
numpy_kwargs = {"ddof": float(unbiased)}
# Compare the std
torch_std = nan_std(tensor, **torch_kwargs)
numpy_std = np.nanstd(tensor.numpy(), **numpy_kwargs)
np.testing.assert_almost_equal(torch_std.numpy(), numpy_std, decimal=6, err_msg=err_msg)
# Compare the variance
torch_var = nan_var(tensor, **torch_kwargs)
numpy_var = np.nanvar(tensor.numpy(), **numpy_kwargs)
np.testing.assert_almost_equal(torch_var.numpy(), numpy_var, decimal=6, err_msg=err_msg)
def test_nan_median(self):
for keepdim in [False, True]:
# Cannot test
for dim in self.dims: # in [d for d in self.dims if not isinstance(d, Tuple)]:
err_msg = f"Error for :\n dim = {dim}\n keepdim = {keepdim}"
tensor = torch.randn(
(7, 9, 11, 13)
) # Need odd number of values to properly compare torch to numpy
# Prepare the arguments for numpy vs torch
if dim is not None:
torch_kwargs = {"dim": dim, "keepdim": keepdim}
numpy_kwargs = {"axis": dim, "keepdims": keepdim}
else:
torch_kwargs = {}
numpy_kwargs = {}
# Compare the nan-median
torch_med = nan_median(tensor, **torch_kwargs)
numpy_med = np.nanmedian(tensor.numpy(), **numpy_kwargs)
torch_sum = torch.nansum(tensor, **torch_kwargs)
np.testing.assert_almost_equal(torch_med.numpy(), numpy_med, decimal=4, err_msg=err_msg)
self.assertListEqual(list(torch_med.shape), list(torch_sum.shape))
def test_nan_mad(self):
for normal in [False, True]:
# Cannot test
for dim in self.dims: # in [d for d in self.dims if not isinstance(d, Tuple)]:
err_msg = f"Error for :\n dim = {dim}\n normal = {normal}"
tensor = torch.randn(
(7, 9, 11, 13)
) # Need odd number of values to properly compare torch to numpy
# Prepare the arguments for numpy vs torch
if dim is not None:
torch_kwargs = {"dim": dim, "keepdim": False, "normal": normal}
numpy_kwargs = {"axis": dim, "nan_policy": "omit", "scale": 1 / 1.4826 if normal else 1.0}
else:
torch_kwargs = {"normal": normal}
numpy_kwargs = {"axis": dim, "nan_policy": "omit", "scale": 1 / 1.4826 if normal else 1.0}
# Compare the nan-median
torch_mad = nan_mad(tensor, **torch_kwargs)
numpy_mad = sp.stats.median_abs_deviation(tensor.numpy(), **numpy_kwargs)
np.testing.assert_almost_equal(torch_mad.numpy(), numpy_mad, decimal=4, err_msg=err_msg)
class test_SafeRun(ut.TestCase):
def test_safe_run(self):
# Error is caught
with SafeRun(name="bob", raise_error=False, verbose=0):
raise ValueError("This is an error")
# Error is caught
with SafeRun(name="bob", raise_error=False, verbose=0):
2 + None
# Error is not caught
with self.assertRaises(ValueError):
with SafeRun(name="bob", raise_error=True, verbose=0):
raise ValueError("This is an error")
# Error is not caught
with self.assertRaises(TypeError):
with SafeRun(name="bob", raise_error=True, verbose=0):
2 + None
# No error. Runs correctly
with SafeRun(name="bob", raise_error=True, verbose=0):
print("This is not an error")
# No error. Runs correctly
with SafeRun(name="bob", raise_error=False, verbose=0):
print("This is not an error")
class TestTensorFp16ToFp32(ut.TestCase):
def test_tensor_fp16_to_fp32(self):
# Create a tensor
tensor = torch.randn(10, 10).half()
# Convert the tensor to fp32
tensor_fp32 = tensor_fp16_to_fp32(tensor)
self.assertTrue(tensor_fp32.dtype == torch.float32)
# Don't convert the tensor to fp32
tensor = torch.randn(10, 10).int()
tensor_fp32 = tensor_fp16_to_fp32(tensor)
self.assertFalse(tensor_fp32.dtype == torch.float32)
# Don't convert the tensor to fp32
tensor = torch.randn(10, 10).double()
tensor_fp32 = tensor_fp16_to_fp32(tensor)
self.assertFalse(tensor_fp32.dtype == torch.float32)
def test_dict_tensor_fp16_to_fp32(self):
# Create a dictionary of tensors
tensor_dict = {
"a": torch.randn(10, 10).half(),
"b": torch.randn(10, 10).half(),
"c": torch.randn(10, 10).double(),
"d": torch.randn(10, 10).half(),
"e": torch.randn(10, 10).float(),
"f": torch.randn(10, 10).half(),
"g": torch.randn(10, 10).int(),
"h": {
"h1": torch.randn(10, 10).double(),
"h2": torch.randn(10, 10).half(),
"h3": torch.randn(10, 10).float(),
"h4": torch.randn(10, 10).half(),
"h5": torch.randn(10, 10).int(),
},
}
# Convert the dictionary to fp32
tensor_dict_fp32 = dict_tensor_fp16_to_fp32(tensor_dict)
# Check that the dictionary is correctly converted
for key, tensor in tensor_dict_fp32.items():
if key in ["a", "b", "d", "e", "f"]:
self.assertEqual(tensor.dtype, torch.float32)
elif key in ["h"]:
for key2, tensor2 in tensor.items():
if key2 in ["h2", "h3", "h4"]:
self.assertEqual(tensor2.dtype, torch.float32)
else:
self.assertNotEqual(tensor2.dtype, torch.float32)
else:
self.assertNotEqual(tensor.dtype, torch.float32)
if __name__ == "__main__":
ut.main()
- Diagram for Muti-task network in Graphium
================================================
FILE: cleanup_files.sh
================================================
"""
--------------------------------------------------------------------------------
Copyright (c) 2023 Valence Labs, Recursion Pharmaceuticals and Graphcore Limited.
Use of this software is subject to the terms and conditions outlined in the LICENSE file.
Unauthorized modification, distribution, or use is prohibited. Provided 'as is' without
warranties of any kind.
Valence Labs, Recursion Pharmaceuticals and Graphcore Limited are not liable for any damages arising from its use.
Refer to the LICENSE file for the full terms and conditions.
--------------------------------------------------------------------------------
"""
# Delete saved files like wandb, checkpoints, profiling etc.
# Usage: bash cleanup_files.sh
rm -rf wandb/*
rm -rf checkpoints/*
rm -rf pro_vision/*
rm -rf outputs/*
rm -rf models_checkpoints/*
rm -rf datacache/*
================================================
FILE: codecov.yml
================================================
coverage:
range: "50...80"
status:
project:
default:
threshold: 1%
patch: false
comment:
layout: "header, diff, flags, components" # show component info in the PR comment
component_management:
default_rules: # default rules that will be inherited by all components
statuses:
- type: project # in this case every component that doens't have a status defined will have a project type one
target: auto
branches:
- "!main"
individual_components:
- component_id: ipu # this is an identifier that should not be changed
name: ipu # this is a display name, and can be changed freely
paths:
- graphium/ipu/**
================================================
FILE: docs/_assets/css/custom-graphium.css
================================================
:root {
--graphium-primary: #548235;
--graphium-secondary: #343a40;
/* Primary color shades */
--md-primary-fg-color: var(--graphium-primary);
--md-primary-fg-color--light: var(--graphium-primary);
--md-primary-fg-color--dark: var(--graphium-primary);
--md-primary-bg-color: var(--graphium-secondary);
--md-primary-bg-color--light: var(--graphium-secondary);
--md-text-link-color: var(--graphium-secondary);
/* Accent color shades */
--md-accent-fg-color: var(--graphium-secondary);
--md-accent-fg-color--transparent: var(--graphium-secondary);
--md-accent-bg-color: var(--graphium-secondary);
--md-accent-bg-color--light: var(--graphium-secondary);
}
:root > * {
/* Code block color shades */
--md-code-bg-color: hsla(0, 0%, 96%, 1);
--md-code-fg-color: hsla(200, 18%, 26%, 1);
/* Footer */
--md-footer-bg-color: var(--graphium-primary);
/* --md-footer-bg-color--dark: hsla(0, 0%, 0%, 0.32); */
--md-footer-fg-color: var(--graphium-secondary);
--md-footer-fg-color--light: var(--graphium-secondary);
--md-footer-fg-color--lighter: var(--graphium-secondary);
}
.md-header {
background-image: linear-gradient(to right, #548235, #8DB771);
}
.md-footer {
background-image: linear-gradient(to right, #548235, #8DB771);
}
.md-tabs {
background-image: linear-gradient(to right, #f4f6f9, #e2cec3);
}
.md-header__topic {
color: #fff;
}
.md-source__repository,
.md-source__icon,
.md-search__input,
.md-search__input::placeholder,
.md-search__input ~ .md-search__icon,
.md-footer__inner.md-grid,
.md-copyright__highlight,
.md-copyright,
.md-footer-meta.md-typeset a,
.md-version {
color: #fff !important;
}
.md-search__form {
background-color: rgba(255, 255, 255, 0.2);
}
.md-search__input {
color: #222 !important;
}
.md-header__topic {
color: #fff;
font-size: 1.4em;
}
/* Increase the size of the logo */
.md-header__button.md-logo img,
.md-header__button.md-logo svg {
height: 2rem !important;
}
/* Reduce the margin around the logo */
.md-header__button.md-logo {
margin: 0.4em;
padding: 0.4em;
}
/* Remove the `In` and `Out` block in rendered Jupyter notebooks */
.md-container .jp-Cell-outputWrapper .jp-OutputPrompt.jp-OutputArea-prompt,
.md-container .jp-Cell-inputWrapper .jp-InputPrompt.jp-InputArea-prompt {
display: none !important;
}
================================================
FILE: docs/_assets/css/custom.css
================================================
/* Indentation. */
div.doc-contents:not(.first) {
padding-left: 25px;
border-left: 4px solid #e6e6e6;
margin-bottom: 80px;
}
/* Don't capitalize names. */
h5.doc-heading {
text-transform: none !important;
}
/* Don't use vertical space on hidden ToC entries. */
.hidden-toc::before {
margin-top: 0 !important;
padding-top: 0 !important;
}
/* Don't show permalink of hidden ToC entries. */
.hidden-toc a.headerlink {
display: none;
}
/* Avoid breaking parameters name, etc. in table cells. */
td code {
word-break: normal !important;
}
/* For pieces of Markdown rendered in table cells. */
td p {
margin-top: 0 !important;
margin-bottom: 0 !important;
}
================================================
FILE: docs/_assets/js/google-analytics.js
================================================
var gtag_id = "G-K76VNHBRM4";
var script = document.createElement("script");
script.src = "https://www.googletagmanager.com/gtag/js?id=" + gtag_id;
document.head.appendChild(script);
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag("js", new Date());
gtag("config", gtag_id);
================================================
FILE: docs/api/graphium.config.md
================================================
graphium.config
====================
helper functions to load and convert config files
::: graphium.config._loader
::: graphium.config.config_convert
::: graphium.config._load
================================================
FILE: docs/api/graphium.data.md
================================================
graphium.data
====================
module for loading datasets and collating batches
=== "Contents"
* [Data Module](#data-module)
* [Collate Module](#collate-module)
* [Util Functions](#util-functions)
## Data Module
------------
::: graphium.data.datamodule
## Collate Module
------------
::: graphium.data.collate
## Util Functions
------------
::: graphium.data.utils
================================================
FILE: docs/api/graphium.features.md
================================================
graphium.features
====================
Feature extraction and manipulation
=== "Contents"
* [Featurizer](#featurizer)
* [Positional Encoding](#positional-encoding)
* [Properties](#properties)
* [Spectral PE](#spectral-pe)
* [Random Walk PE](#random-walk-pe)
* [NMP](#nmp)
## Featurizer
------------
::: graphium.features.featurizer
## Positional Encoding
------------
::: graphium.features.positional_encoding
## Properties
------------
::: graphium.features.properties
## Spectral PE
------------
::: graphium.features.spectral
## Random Walk PE
------------
::: graphium.features.rw
## NMP
------------
::: graphium.features.nmp
================================================
FILE: docs/api/graphium.finetuning.md
================================================
graphium.finetuning
====================
Module for finetuning models and doing linear probing (fingerprinting).
::: graphium.finetuning.finetuning.GraphFinetuning
::: graphium.finetuning.finetuning_architecture.FullGraphFinetuningNetwork
::: graphium.finetuning.finetuning_architecture.PretrainedModel
::: graphium.finetuning.finetuning_architecture.FinetuningHead
::: graphium.finetuning.fingerprinting.Fingerprinter
================================================
FILE: docs/api/graphium.ipu.md
================================================
graphium.ipu
====================
Code for adapting to run on IPU
=== "Contents"
* [IPU Dataloader](#ipu-dataloader)
* [IPU Losses](#ipu-losses)
* [IPU Metrics](#ipu-metrics)
* [IPU Simple Lightning](#ipu-simple-lightning)
* [IPU Utils](#ipu-utils)
* [IPU Wrapper](#ipu-wrapper)
* [To Dense Batch](#to-dense-batch)
## IPU Dataloader
------------
::: graphium.ipu.ipu_dataloader
## IPU Losses
------------
::: graphium.ipu.ipu_losses
## IPU Metrics
------------
::: graphium.ipu.ipu_metrics
## IPU Simple Lightning
------------
::: graphium.ipu.ipu_simple_lightning
## IPU Utils
------------
::: graphium.ipu.ipu_utils
## IPU Wrapper
------------
::: graphium.ipu.ipu_wrapper
## To Dense Batch
------------
::: graphium.ipu.to_dense_batch
================================================
FILE: docs/api/graphium.nn/architectures.md
================================================
graphium.nn.architectures
====================
High level architectures in the library
=== "Contents"
* [Global Architectures](#global-architectures)
* [PyG Architectures](#pyg-architectures)
* [Encoder Manager](#encoder-manager)
## Global Architectures
------------
::: graphium.nn.architectures.global_architectures
## PyG Architectures
------------
::: graphium.nn.architectures.pyg_architectures
## Encoder Manager
------------
::: graphium.nn.architectures.encoder_manager
================================================
FILE: docs/api/graphium.nn/encoders.md
================================================
graphium.nn.encoders
====================
Implementations of positional encoders in the library
=== "Contents"
* [Base Encoder](#base-encoder)
* [Gaussian Kernal Positional Encoder](#gaussian-kernal-positional-encoder)
* [Laplacian Positional Encoder](#laplacian-positional-encoder)
* [MLP Encoder](#mlp-encoder)
* [Signnet Positional Encoder](#signnet-positional-encoder)
## Base Encoder
------------
::: graphium.nn.encoders.base_encoder
## Gaussian Kernal Positional Encoder
------------
::: graphium.nn.encoders.gaussian_kernel_pos_encoder
## Laplacian Positional Encoder
------------
::: graphium.nn.encoders.laplace_pos_encoder
## MLP Encoder
------------
::: graphium.nn.encoders.mlp_encoder
## Signnet Positional Encoder
------------
::: graphium.nn.encoders.signnet_pos_encoder
================================================
FILE: docs/api/graphium.nn/graphium.nn.md
================================================
graphium.nn
====================
Base structures for neural networks in the library (to be inherented by other modules)
=== "Contents"
* [Base Graph Layer](#base-graph-layer)
* [Base Layers](#base-layers)
* [Residual Connection](#residual-connections)
## Base Graph Layer
------------
::: graphium.nn.base_graph_layer
## Base Layers
------------
::: graphium.nn.base_layers
## Residual Connections
------------
::: graphium.nn.residual_connections
================================================
FILE: docs/api/graphium.nn/pyg_layers.md
================================================
graphium.nn.pyg_layers
====================
Implementations of GNN layers based on PyG in the library
=== "Contents"
* [Gated GCN Layer](#gated-gcn-layer)
* [GPS Layer](#gps-layer)
* [GIN and GINE Layers](#gin-and-gine-layers)
* [MPNN Layer](#mpnn-layer)
* [PNA Layer](#pna-layer)
* [Pooling Layer](#pooling-layers)
## Gated GCN Layer
------------
::: graphium.nn.pyg_layers.gated_gcn_pyg
## GPS Layer
------------
::: graphium.nn.pyg_layers.gps_pyg
## GIN and GINE Layers
------------
::: graphium.nn.pyg_layers.gin_pyg
## MPNN Layer
------------
::: graphium.nn.pyg_layers.mpnn_pyg
## PNA Layer
------------
::: graphium.nn.pyg_layers.pna_pyg
## Pooling Layers
------------
::: graphium.nn.pyg_layers.pooling_pyg
## Utils
------------
::: graphium.nn.pyg_layers.utils
================================================
FILE: docs/api/graphium.trainer.md
================================================
graphium.trainer
====================
Code for training models
=== "Contents"
* [Predictor](#predictor)
* [Metrics](#metrics)
* [Predictor Summaries](#predictor-summaries)
* [Predictor Options](#predictor-options)
## Predictor
------------
::: graphium.trainer.predictor
## Metrics
------------
::: graphium.trainer.metrics
## Predictor Summaries
------------
::: graphium.trainer.predictor_summaries
## Predictor Options
------------
::: graphium.trainer.predictor_options
================================================
FILE: docs/api/graphium.utils.md
================================================
graphium.utils
====================
module for utility functions
=== "Contents"
* [Argument Checker](#argument-checker)
* [Decorators](#decorators)
* [Dictionary of Tensors](#dictionary-of-tensors)
* [File System](#file-system)
* [Hashing](#hashing)
* [Moving Average Tracker](#moving-average-tracker)
* [MUP](#mup)
* [Read File](#read-file)
* [Safe Run](#safe-run)
* [Spaces](#spaces)
* [Tensor](#tensor)
## Argument Checker
----------------
::: graphium.utils.arg_checker
## Decorators
----------------
::: graphium.utils.decorators
## File System
----------------
::: graphium.utils.fs
## Hashing
----------------
::: graphium.utils.hashing
## Moving Average Tracker
----------------
::: graphium.utils.moving_average_tracker
## MUP
----------------
::: graphium.utils.mup
## Read File
----------------
::: graphium.utils.read_file
## Safe Run
----------------
::: graphium.utils.safe_run
## Spaces
----------------
::: graphium.utils.spaces
## Tensor
----------------
::: graphium.utils.tensor
================================================
FILE: docs/baseline.md
================================================
# ToyMix Baseline - Test set metrics
From the paper to be released soon. Below, you can see the baselines for the `ToyMix` dataset, a multitasking dataset comprising of `QM9`, `Zinc12k` and `Tox21`. The datasets and their splits are available on [this link](https://zenodo.org/record/7998401). The following baselines are all for models with ~150k parameters.
One can observe that the smaller datasets (`Zinc12k` and `Tox21`) beneficiate from adding another unrelated task (`QM9`), where the labels are computed from DFT simulations.
**NEW baselines added 2023/09/18**: Multitask baselines have been added for GatedGCN and MPNN++ (sum aggretator) using 3 random seeds. They achieve the best performance by a significant margin on Zinc12k and Tox21, while sacrificing a little on QM9.
| Dataset | Model | MAE ↓ | Pearson ↑ | R² ↑ | MAE ↓ | Pearson ↑ | R² ↑ |
|-----------|-------|-----------|-----------|-----------|---------|-----------|---------|
| | 
### Diagram for Muti-task network in Graphium
As mentioned, we want to be able to pperform massive multi-tasking to enable us to work across a huge diversity of datasets. The idea is to use a combination of multiple task-heads, where a different MLP is applied to each task predictions. However, it is also designed such that each task can have as many labels as desired, thus enabling to group labels together according to whether they should share weights/losses.
The design is better explained in the image below. Notice how the *keys* outputed by GraphDict are used differently across the different GNN layers.
## Structure of the code
The code is built to rapidly iterate on different architectures of neural networks (NN) and graph neural networks (GNN) with Pytorch. The main focus of this work is molecular tasks, and we use the package `rdkit` to transform molecular SMILES into graphs.
Below are a list of directory and their respective documentations:
- cli
- [config](https://github.com/datamol-io/graphium/blob/main/graphium/config/README.md)
- [data](https://github.com/datamol-io/graphium/blob/main/graphium/data/README.md)
- [features](https://github.com/datamol-io/graphium/tree/main/graphium/features/README.md)
- finetuning
- [ipu](https://github.com/datamol-io/graphium/tree/main/graphium/ipu/README.md)
- [nn](https://github.com/datamol-io/graphium/tree/main/graphium/nn/README.md)
- [trainer](https://github.com/datamol-io/graphium/tree/main/graphium/trainer/README.md)
- [utils](https://github.com/datamol-io/graphium/tree/main/graphium/features/README.md)
## Structure of the configs
Making the library very modular requires to have configuration files that have >200 lines, which becomes intractable, especially when we only want to have minor changes between configurations.
Hence, we use [hydra](https://hydra.cc/docs/intro/) to enable splitting the configurations into smaller and composable configuration files.
Examples of possibilities include:
- Switching between accelerators (CPU, GPU and IPU)
- Benchmarking different models on the same dataset
- Fine-tuning a pre-trained model on a new dataset
[In this document](https://github.com/datamol-io/graphium/tree/main/expts/hydra-configs#readme), we describe in details how each of the above functionality is achieved and how users can benefit from this design to achieve the most with Graphium with as little configuration as possible.
================================================
FILE: docs/index.md
================================================
# Overview
A deep learning library focused on graph representation learning for real-world chemical tasks.
- ✅ State-of-the-art GNN architectures.
- 🐍 Extensible API: build your own GNN model and train it with ease.
- ⚗️ Rich featurization: powerful and flexible built-in molecular featurization.
- 🧠 Pretrained models: for fast and easy inference or transfer learning.
- ⮔ Read-to-use training loop based on [Pytorch Lightning](https://www.pytorchlightning.ai/).
- 🔌 Have a new dataset? Graphium provides a simple plug-and-play interface. Change the path, the name of the columns to predict, the atomic featurization, and you’re ready to play!
## Installation
### For CPU or GPU
Use [`mamba`](https://github.com/mamba-org/mamba):
```bash
# Install Graphium
mamba install -c conda-forge graphium
```
or pip:
```bash
pip install graphium
```
### For IPU
```bash
# Install Graphcore's SDK and Graphium dependencies in a new environment called `.graphium_ipu`
./install_ipu.sh .graphium_ipu
```
The above step needs to be done once. After that, enable the SDK and the environment as follows:
```bash
source enable_ipu.sh .graphium_ipu
```
Finally, you will need to install graphium with pip
```bash
pip install graphium
```
================================================
FILE: docs/license.md
================================================
```
{!LICENSE!}
```
================================================
FILE: docs/pretrained_models.md
================================================
# Graphium pretrained models
Graphium aims to provide a set of pretrained models that you can use for inference or transfer learning. The models will be made available once trained and validated.
## Listing all available models
To know which models are available, you can run the following command
```python
import graphium
print(graphium.trainer.PredictorModule.list_pretrained_models())
```
## Dummy pre-trained model
At the moment, only `tests/dummy-pretrained-model.ckpt` is provided, which is mostly useful for development and debugging of the checkpointing and finetuning pipelines.
You can load a pretrained models using the Graphium API:
```python
import graphium
predictor = graphium.trainer.PredictorModule.load_pretrained_models("dummy-pretrained-model")
```
================================================
FILE: docs/tutorials/feature_processing/add_new_positional_encoding.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add new positional encoding\n",
"\n",
"One of the main advantage of this library is the ability to easily incorporate novel positional encodings on the node, edge and graph level. The positional encodings are computed and feed into respective encoders and then the hidden embeddings from all pe encoders are pooled (according to if they are node, edge, or graph level) and then feed into the GNN layers as features. The designs allow any combination of positional encodings to be used by modifying the configuration file. For more details on the data processing part, please visit the [design page of the doc](https://graphium-docs.datamol.io/stable/design.html).\n",
"\n",
"Here is the workflow for computing and processing positional encoding in the library:\n",
"1. edit related parts in the yaml configuration file\n",
"\n",
"2. compute the raw positional encoding from the graph in [`graphium/features/positional_encoding.py`](https://graphium-docs.datamol.io/stable/api/graphium.features.html#graphium.features.positional_encoding) (from the [`graph positional encoder`](https://graphium-docs.datamol.io/stable/api/graphium.features.html#graphium.features.positional_encoding.graph_positional_encoder))\n",
"\n",
"3. feed the raw positional encoding into the respective (specialized) encoders in [`graphium/nn/encoders`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html). For example, a simple [`MLP positional encoder`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.mlp_encoder) can be found. \n",
"\n",
"4. Output the hidden embeddings of pe from the encoders in their respective output keys: `feat`(node feature), `edge_feat`(edge feature), `graph_feat`(graph feature) and potentially other keys if needed such as `nodepair_feat` \n",
"\n",
"5. pool the hidden embeddings with same keys together: for example, all output with `feat` key will be pooled together\n",
"\n",
"6. Construct the [`PyG Batch`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Batch.html#torch_geometric.data.Batch), batch of graphs, each contain the output keys seen above, ready for use in the [GNN layers](https://graphium-docs.datamol.io/stable/api/graphium.nn/pyg_layers.html) \n",
"\n",
"Since this library is built using PyG, we recommend looking at their [Docs](https://pytorch-geometric.readthedocs.io/en/latest/) and [Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html) for more info. \n",
"\n",
"We start by editing the configuration file first.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of content\n",
"1. [edit the config file](#Edit-the-yaml-Configuration-File)\n",
"2. [compute the pe from mol](#Compute-the-Positional-Encoding)\n",
"3. [add existing encoder](#Add-Existing-Encoder)\n",
"4. [add specialized encoder](#Add-Specialized-Encoder)\n",
"5. [add the keys to spaces](#Add-the-Keys-to-Spaces)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Edit the yaml Configuration File\n",
"\n",
"### Computing Raw PE\n",
"We will use the degree of each node as a positional encoding in this tutorial. \n",
"First start with an existing yaml configuration file, you can find them in `expts/configs`\n",
"\n",
"We first look at where in the yaml file is the raw positional encodings computed. `deg_pos` is added as an example below. You can add relevant arguments for computing the positional encoding here as well such as `normalize` in the example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"pos_encoding_as_features:\n",
" pos_types:\n",
" deg_pos: #example, degree centrality\n",
" pos_type: degree\n",
" normalize: False\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specifying Encoders for the PE\n",
"Now we want to specify arguments for the encoders associated with the pe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"pe_encoders:\n",
" out_dim: 64\n",
" pool: \"sum\" #choice of pooling across multiple pe encoders\n",
" last_norm: None #\"batch_norm\", \"layer_norm\"\n",
" encoders: \n",
" deg_pos: #same name from the previous cell\n",
" encoder_type: \"mlp\" #or you can specify your own specialized encoder\n",
" input_keys: [\"degree\"] #same as the pos_type configured before\n",
" output_keys: [\"feat\"] #node feature\n",
" hidden_dim: 64\n",
" num_layers: 1\n",
" dropout: 0.1\n",
" normalization: \"none\" #\"batch_norm\" or \"layer_norm\"\n",
" first_normalization: \"layer_norm\" #\"batch_norm\" or \"layer_norm\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute the Positional Encoding\n",
"Next, we want to compute the raw degree of each node from the molecule graph.\n",
"\n",
"### add function to compute the pe\n",
"Go to [graphium/features](https://graphium-docs.datamol.io/stable/api/graphium.features.html) and add a new file `deg.py` to add the function to compute the pe. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from typing import Tuple, Union, Optional\n",
"\n",
"from scipy import sparse\n",
"from scipy.sparse import spmatrix\n",
"import numpy as np\n",
"\n",
"def compute_deg(adj: Union[np.ndarray, spmatrix], normalize: bool) -> np.ndarray:\n",
" \"\"\"\n",
" Compute the node degree positional encoding \n",
"\n",
" Parameters:\n",
" adj: Adjacency matrix\n",
" normalize: indicate if the degree across all nodes are normalized to [0,1] or not\n",
" Returns:\n",
" 2D array with shape (num_nodes, 1) specifying (outgoing) degree for each node\n",
" \"\"\"\n",
" \n",
" #first adj convert to scipy sparse matrix if not already\n",
" if type(adj) is np.ndarray:\n",
" adj = sparse.csr_matrix(adj)\n",
" \n",
" #https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.sum.html\n",
" degs = adj.sum(axis=0) #sum over each row\n",
" \n",
" if (normalize): #normalize the degree sequence to [0,1]\n",
" degs = degs / np.max(degs)\n",
" return degs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test with toy matrix\n",
"\n",
"here we will test if our code compute the degrees of each node correctly"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[1., 1., 1., 1., 1.]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adj = np.identity(5) #make an identity matrix\n",
"normalize = True\n",
"\n",
"degs = compute_deg(adj, normalize=normalize)\n",
"\n",
"degs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### add to positional_encoding.py\n",
"\n",
"To compute the new pe along with all existing pe, we need to add the function we wrote to [`graphium/feature/positional_encoding.py`](https://graphium-docs.datamol.io/stable/api/graphium.features.html#graphium.features.positional_encoding). Modify the [`graph_positional_encoder`](https://graphium-docs.datamol.io/stable/api/graphium.features.html#graphium.features.positional_encoding.graph_positional_encoder) function by adding `pos_type == \"degree\"` logic "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add Existing Encoder\n",
"\n",
"In order to pool over all the positional encodings, we need to add encoder to process the raw computed positional encoding and ensure the output dimension from all pe encoders are the same. When designing the encoder, you can either use an existing encoder or write a specialized encoder you made\n",
"\n",
"here we can simply specify [`MLPEncoder`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.mlp_encoder.MLPEncoder) in the yaml file and the library will automatically feed the raw positional encoding to a mlp encoder based on the input arguments. Note that in this example, the encoder takes in the pe stored at the input key `degree` and then outputs to the output key `feat`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"encoders: \n",
" deg_pos: \n",
" encoder_type: \"mlp\" \n",
" input_keys: [\"degree\"] \n",
" output_keys: [\"feat\"] # node feature\n",
" hidden_dim: 64\n",
" num_layers: 1\n",
" dropout: 0.1\n",
" normalization: \"none\" #\"batch_norm\" or \"layer_norm\"\n",
" first_normalization: \"layer_norm\" #\"batch_norm\" or \"layer_norm\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add Specialized Encoder\n",
"\n",
"You can also add specialized encoder, such as `laplacian_pe` for the laplacian eigenvectors and eigenvalues. Here, we can add a new `deg_pos_encoder.py` in [`graphium/nn/encoders`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html). As an example and template, please see the [`MLPEncoder`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.mlp_encoder.MLPEncoder)\n",
"\n",
"Note that all new encoders must inherent from [`BaseEncoder`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.base_encoder.BaseEncoder) class and implement the following abstract methods\n",
"\n",
"- [`forward`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.base_encoder.BaseEncoder.forward): the forward function of the encoder, how to process the input\n",
"\n",
"- [`parse_input_keys`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.base_encoder.BaseEncoder.parse_input_keys): how to parse the input keys\n",
"\n",
"- [`parse_output_keys`](https://graphium-docs.datamol.io/stable/api/graphium.nn/encoders.html#graphium.nn.encoders.laplace_pos_encoder.LapPENodeEncoder.parse_output_keys): how to parse the output keys\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add the Keys to Spaces\n",
"\n",
"In order to directly find the correct encoders from the yaml file, we need to specify which key corresponding to what class. \n",
"\n",
"- add our new `deg_pos_encoder` to `graphium/utils/spaces.py` in the `PE_ENCODERS_DICT`\n",
"- add our new `deg_pos_encoder` to [`graphium/nn/architectures/encoder_manager.py`](https://graphium-docs.datamol.io/stable/api/graphium.nn/architectures.html#graphium.nn.architectures.encoder_manager.EncoderManager) in the `PE_ENCODERS_DICT`\n",
"- add the import of our encoder to `graphium/nn/encoders/__init__.py`\n",
"\n",
"Now we can modify the yaml file to use our new encoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"encoders: \n",
" deg_pos: \n",
" encoder_type: \"deg_pos_encoder\" \n",
" input_keys: [\"degree\"] \n",
" output_keys: [\"feat\"] # node feature\n",
" hidden_dim: 64\n",
" #any other keys that might be used for initialization\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "f4a99d018a205fcbcc0480c84566beaebcb91b08d0414b39a842df533e2a1d25"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: docs/tutorials/feature_processing/choosing_parallelization.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "0693c5c9",
"metadata": {},
"source": [
"# Choosing the right parallelization\n",
"\n",
"This tutorial is meant to help you benchmark different parallel processing methods for the processing of molecules into graphs. This will allow you to chose the one most suitable for your machine, since the benchmarks vary per machine.\n",
"\n",
"In general, we find that using `joblib` with the `loky` parallel processing and a batch size of `1000` is most beneficial. The logic is abstracted into `datamol.parallelized_with_batches`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b5df2ac6-2ded-4597-a445-f2b5fb106330",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import joblib\n",
"\n",
"import numpy as np\n",
"import datamol as dm\n",
"import pandas as pd\n",
"\n",
"# from pandarallel import pandarallel\n",
"\n",
"# pandarallel.initialize(progress_bar=True, nb_workers=joblib.cpu_count())"
]
},
{
"cell_type": "markdown",
"id": "f81fe0f5-055f-436e-9ef4-e58a89fd50ec",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0f31e18d-bdd9-4d9b-8ba5-81e5887b857e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# download from https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv\n",
"# data = pd.read_csv(\"/home/hadim/250k_rndm_zinc_drugs_clean_3.csv\", usecols=[\"smiles\"])\n",
"\n",
"# download from https://storage.valencelabs.com/graphium/datasets/QM9/norm_qm9.csv\n",
"data = pd.read_csv(\"https://storage.valencelabs.com/graphium/datasets/QM9/norm_qm9.csv\", usecols=[\"smiles\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a1197c31-7dbc-4fd7-a69a-5215e1a96b8e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"rows_number_list = [250_000]\n",
"batch_size_list = [10, 100, 1_000, 10_000]\n",
"\n",
"\n",
"def smiles_to_unique_mol_id(smiles):\n",
" try:\n",
" mol = dm.to_mol(mol=smiles)\n",
" mol_id = dm.unique_id(mol)\n",
" except:\n",
" mol_id = \"\"\n",
" if mol_id is None:\n",
" mol_id = \"\"\n",
" return mol_id\n",
"\n",
"\n",
"def smiles_to_unique_mol_id_batch(smiles_list):\n",
" mol_id_list = []\n",
" for smiles in smiles_list:\n",
" mol_id_list.append(smiles_to_unique_mol_id(smiles))\n",
" return mol_id_list"
]
},
{
"cell_type": "markdown",
"id": "5d8eea8f-b983-46e7-bfb4-b8012b3ede1a",
"metadata": {},
"source": [
"## Benchmarks"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2f8ce5c3-4232-4279-8ea3-7a74832303be",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"benchmark = []"
]
},
{
"cell_type": "markdown",
"id": "691f8963-6cac-4d58-b8a0-8049f1185486",
"metadata": {},
"source": [
"### No batch"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a246cdcf-b5ea-4c9e-9ccc-dd3c544587bb",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc396220c7144c8d8b195fb87694bbfe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/133885 [00:00\n",
"\n",
"