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Repository: OptiMaL-PSE-Lab/DeepDock
Branch: main
Commit: ab1e45044c5e
Files: 86
Total size: 23.2 MB
Directory structure:
gitextract_irimushr/
├── .gitmodules
├── Dockerfile
├── LICENSE
├── README.md
├── Trained_models/
│ ├── DeepDock_pdbbindv2019_13K_loss.csv
│ └── DeepDock_pdbbindv2019_13K_minTestLoss.chk
├── Validation_CASF2016/
│ ├── CASF2016_DockingPower_DeepDock.ipynb
│ ├── CASF2016_ScoringPower_DeepDock.ipynb
│ ├── CASF2016_ScreeningPower_DeepDock.ipynb
│ ├── DockingPower_DeepDock_10A/
│ │ └── DockingPower_DeepDock_10A.out
│ ├── DockingPower_DeepDock_3A/
│ │ └── DockingPower_DeepDock_3A.out
│ ├── DockingPower_DeepDock_5A/
│ │ └── DockingPower_DeepDock_5A.out
│ ├── DockingPower_DeepDock_7A/
│ │ └── DockingPower_DeepDock_7A.out
│ ├── DockingPower_DeepDock_all/
│ │ └── DockingPower_DeepDock_all.out
│ ├── Score_CoreSet_docking_CASF2016.csv
│ ├── Score_decoys_docking_CASF2016.csv
│ ├── ScoringPower_Deepdock/
│ │ ├── RankingPower_Deepdock_10A.out
│ │ ├── RankingPower_Deepdock_3A.out
│ │ ├── RankingPower_Deepdock_5A.out
│ │ ├── RankingPower_Deepdock_7A.out
│ │ ├── RankingPower_Deepdock_all.out
│ │ ├── ScoringPower_Deepdock_10A.out
│ │ ├── ScoringPower_Deepdock_3A.out
│ │ ├── ScoringPower_Deepdock_5A.out
│ │ ├── ScoringPower_Deepdock_7A.out
│ │ └── ScoringPower_Deepdock_all.out
│ ├── ScreeningPower_DeepDock_10A/
│ │ ├── ForwardScreeningPower_DeepDock_10A.out
│ │ └── ReverseScreeningPower_DeepDock_10A.out
│ ├── ScreeningPower_DeepDock_3A/
│ │ ├── ForwardScreeningPower_DeepDock_3A.out
│ │ └── ReverseScreeningPower_DeepDock_3A.out
│ ├── ScreeningPower_DeepDock_5A/
│ │ ├── ForwardScreeningPower_DeepDock_5A.out
│ │ └── ReverseScreeningPower_DeepDock_5A.out
│ ├── ScreeningPower_DeepDock_7A/
│ │ ├── ForwardScreeningPower_DeepDock_7A.out
│ │ └── ReverseScreeningPower_DeepDock_7A.out
│ └── ScreeningPower_DeepDock_all/
│ ├── ForwardScreeningPower_DeepDock_all.out
│ └── ReverseScreeningPower_DeepDock_all.out
├── Validation_Docking/
│ ├── DockingResults_CASF2016_CoreSet.chk
│ ├── DockingResults_CASF2016_CoreSet.csv
│ ├── DockingResults_TestSet.chk
│ ├── DockingResults_TestSet.csv
│ ├── Docking_CASF2016_CoreSet.ipynb
│ └── Docking_TestSet.ipynb
├── data/
│ ├── 1z6e_ligand.mol2
│ ├── 1z6e_protein.pdb
│ ├── 1z6e_protein.ply
│ ├── 2br1_ligand.mol2
│ ├── 2br1_protein.pdb
│ ├── 2br1_protein.ply
│ ├── 2wtv_ligand.mol2
│ ├── 2wtv_protein.pdb
│ ├── 2wtv_protein.ply
│ ├── 2yge_ligand.mol2
│ ├── 2yge_protein.pdb
│ ├── 2yge_protein.ply
│ ├── 4f2w_ligand.mol2
│ ├── 4f2w_protein.pdb
│ ├── 4f2w_protein.ply
│ ├── 4ivd_ligand.mol2
│ ├── 4ivd_protein.pdb
│ ├── 4ivd_protein.ply
│ ├── 4twp_ligand.mol2
│ ├── 4twp_protein.pdb
│ ├── get_CASF_2016.sh
│ └── get_deepdock_data.sh
├── deepdock/
│ ├── DockingFunction.py
│ ├── __init__.py
│ ├── models.py
│ ├── prepare_target/
│ │ ├── __init_.py
│ │ ├── computeAPBS.py
│ │ ├── computeCharges.py
│ │ ├── computeHydrophobicity.py
│ │ ├── computeMSMS.py
│ │ ├── computeTargetMesh.py
│ │ ├── compute_normal.py
│ │ ├── fixmesh.py
│ │ └── save_ply.py
│ └── utils/
│ ├── __init__.py
│ ├── data.py
│ ├── distributions.py
│ └── mol2graph.py
├── examples/
│ ├── Docking_example.ipynb
│ ├── Score_example.ipynb
│ └── Train_DeepDock.ipynb
├── images/
│ └── Fig1.tiff
├── requirements.txt
└── setup.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitmodules
================================================
[submodule "deepdock/masif"]
path = deepdock/masif
url = https://github.com/LPDI-EPFL/masif.git
================================================
FILE: Dockerfile
================================================
# The base image we are going to use.
FROM nvcr.io/nvidia/pytorch:19.10-py3
#Do some basic preparations
RUN conda install -c anaconda joblib -y
RUN pip install ipython jupyter jupyter-tensorboard --upgrade && \
jupyter tensorboard enable --system
#RUN pip install packages
RUN conda install -c conda-forge rdkit=2019.09.1 -y && \
pip install \
cupy-cuda101 \
torch==1.4.0 \
torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html \
torch-sparse==0.6.1 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html \
torch-cluster==1.5.3 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html \
torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html \
torch_geometric==1.4.3 \
numpy==1.16.6 \
plyfile==0.7.2 \
pandas==0.25.1 \
networkx==2.5 \
scikit_learn==0.21.3 \
matplotlib \
transformers==4.2.2 \
trimesh==3.6.5 \
Biopython \
py3Dmol
WORKDIR /
RUN wget --no-check-certificate https://github.com/PyMesh/PyMesh/releases/download/v0.2.0/pymesh2-0.2.0-cp36-cp36m-linux_x86_64.whl && \
pip install pymesh2-0.2.0-cp36-cp36m-linux_x86_64.whl && \
git clone https://github.com/shenwanxiang/ChemBench.git && \
cd ChemBench && \
pip install -e .
# Install APBS, PDB2PQR and MSMSto calculate target mesh with MaSIF
# install necessary dependencies
RUN apt-get update && \
apt-get install -y wget git unzip cmake vim libgl1-mesa-glx
# DOWNLOAD/INSTALL APBS
RUN mkdir /install
WORKDIR /install
RUN git clone https://github.com/Electrostatics/apbs-pdb2pqr && \
git clone https://github.com/swig/swig.git
WORKDIR /install/swig
RUN git checkout tags/v4.0.2 && \
apt-get install -y automake && \
./autogen.sh && \
./configure && \
apt-get install -y bison flex && \
make && \
make install
WORKDIR /install/apbs-pdb2pqr
RUN git checkout b3bfeec && \
git submodule update --init --recursive && \
cmake -DGET_MSMS=ON apbs && \
make && \
make install && \
cp -r /install/apbs-pdb2pqr/apbs/externals/mesh_routines/msms/msms_i86_64Linux2_2.6.1 /root/msms/ && \
curl https://bootstrap.pypa.io/pip/3.6/get-pip.py -o get-pip.py && \
python get-pip.py
# INSTALL PDB2PQR
WORKDIR /install/apbs-pdb2pqr/pdb2pqr
RUN apt-get -y install python-dev python3-dev && \
python2.7 scons/scons.py install PREFIX="/usr/local/bin/pdb2pqr"
# Setup environment variables
ENV MSMS_BIN /usr/local/bin/msms
ENV APBS_BIN /usr/local/bin/apbs
ENV MULTIVALUE_BIN /usr/local/share/apbs/tools/bin/multivalue
ENV PDB2PQR_BIN /usr/local/bin/pdb2pqr/pdb2pqr.py
# DOWNLOAD reduce (for protonation)
WORKDIR /install
RUN git clone https://github.com/rlabduke/reduce.git && \
cd reduce && \
git checkout b3aac6e && \
make && \
apt-get install sudo -y && \
sudo make install
# Clone deepdock and install
WORKDIR /
RUN git clone https://github.com/OptiMaL-PSE-Lab/DeepDock.git && \
cd DeepDock && \
git checkout v1.0.0 && \
git submodule update --init --recursive && \
pip install -e . && \
cd data && \
wget https://ndownloader.figshare.com/files/27800817 -O dataset_CASF-2016_285.tar
# create a conda environment to run python2.7
RUN conda create -y -n python2_env \
python=2.7 \
numpy \
pandas \
scipy \
scikit-learn \
jinja2 && \
conda init bash
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2021 OptiMaL PSE Lab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# DeepDock
Code related to: [O. Mendez-Lucio, M. Ahmad, E.A. del Rio-Chanona, J.K. Wegner, A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules, Nature Machine Intelligence volume 3, pages1033–1039 (2021)](https://rdcu.be/cDy5f)
Open access preprint [available here](https://doi.org/10.26434/chemrxiv.14453106.v1)
#### Use [v1.0.0](https://github.com/OptiMaL-PSE-Lab/DeepDock/releases/tag/v1.0.0) to reproduce results reported in the paper
https://user-images.githubusercontent.com/48085126/116097409-68553d80-a6aa-11eb-9426-91713394c3c3.mp4
<!-- TABLE OF CONTENTS -->
<details open="open">
<summary>Table of Contents</summary>
<ol>
<li><a href="#about-the-project">About The Project</a></li>
<li>
<a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#prerequisites">Prerequisites</a></li>
<li><a href="#installation">Installation</a></li>
<li><a href="#data">Data</a></li>
</ul>
</li>
<li><a href="#usage">Usage</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
<li><a href="#acknowledgements">Acknowledgements</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
## About The Project
This method is based on geometric deep learning and is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands.
We showed that:
* Geometric deep learning can learn a potential based on distance likelihood for ligand-target interactions
* This potential performs similar or better than well-established scoring functions for docking and screening tasks
* It can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands
<!-- GETTING STARTED -->
## Getting Started
### Prerequisites
This package runs using Pytorch and Pytorch Geometric. On top it uses standard packages such as pandas and numpy. For the complete list have a look into the [requirements.txt](https://github.com/OptiMaL-PSE-Lab/DeepDock/blob/main/requirements.txt) file
* install requirements.txt
```sh
pip install -r requirements.txt
```
* install RDKIT
```sh
conda install -c conda-forge rdkit=2019.09.1
```
### Installation
#### Using Docker image
1. Install [docker](https://docs.docker.com/install/)
2. Pull docker image from [DockerHub](https://hub.docker.com/repository/docker/omendezlucio/deepdock)
```sh
docker pull omendezlucio/deepdock
```
3. Launch the container.
```sh
docker run -it omendezlucio/deepdock:latest
```
#### Using Dockerfile
To build an image and run it from scratch:
1. Install [docker](https://docs.docker.com/install/)
2. Clone repo and move into project folder.
```sh
git clone https://github.com/OptiMaL-PSE-Lab/DeepDock.git
cd DeepDock
```
3. Build the docker image. This takes 20-30 mins to build
```sh
docker build -t deepdock:latest .
```
4. Launch the container.
```sh
docker run -it --rm --name deepdock-env deepdock:latest
```
#### From source
1. Clone the repo
```sh
git clone https://github.com/OptiMaL-PSE-Lab/DeepDock.git
```
2. Move into the project folder and update submodules
```sh
cd DeepDock
git submodule update --init --recursive
```
3. Install prerequisite packages
```sh
conda install -c conda-forge rdkit=2019.09.1
pip install -r requirements.txt
```
4. Install DeepDock pacakge
```sh
pip install -e .
```
## Data
You can get training and testing data following the next steps.
1. Move into the project data folder
```sh
cd DeepDock/data
```
2. Use the following line to download the preprocessed data used to train and test the model. This will download two files, one containing PDBbind (2.3 GB) used for training and another containing CASF-2016 (32 MB) used for testing. These two files are enough to run all [examples](https://github.com/OptiMaL-PSE-Lab/DeepDock/blob/main/examples).
```sh
source get_deepdock_data.sh
```
2. In case you want to reproduce all results of the paper you will need to download the complete CASF-2016 set (~1.5 GB). You can do so with this command line from the data folder.
```sh
source get_CASF_2016.sh
```
<!-- USAGE EXAMPLES -->
## Usage
Usage examples can be seen directly in the jupyter notebooks included in the repo. We added examples for:
* [Training the model](https://github.com/OptiMaL-PSE-Lab/DeepDock/blob/main/examples/Train_DeepDock.ipynb)
* [Score molecules](https://github.com/OptiMaL-PSE-Lab/DeepDock/blob/main/examples/Score_example.ipynb)
* [Predict binding conformation (docking)](https://github.com/OptiMaL-PSE-Lab/DeepDock/blob/main/examples/Docking_example.ipynb)
<!-- LICENSE -->
## License
Distributed under the MIT License. See `LICENSE` for more information.
<!-- CONTACT -->
## Contact
[@omendezl](https://twitter.com/omendezlucio) and [@AntonioE89](https://twitter.com/antonioe89)
Project Link: [DeepDock](https://github.com/OptiMaL-PSE-Lab/DeepDock)
<!-- ACKNOWLEDGEMENTS -->
## Acknowledgements
* [MaSIF](https://github.com/LPDI-EPFL/masif)
* [Best-README-Template](https://github.com/othneildrew/Best-README-Template)
================================================
FILE: Trained_models/DeepDock_pdbbindv2019_13K_loss.csv
================================================
,total_loss,mdn_loss,atom_loss,bond_loss,test_total_loss,test_mdn_loss,test_atom_loss,test_bond_loss
0,1.6920992707054496,1.6917326012388432,0.24679810699621837,0.11987134235898654,1.5734917675128992,1.573409904973778,0.04726326830777794,0.03459926679779468
1,1.610236895104439,1.6101755372489612,0.031637756643195944,0.02972009589721759,1.5628051858872047,1.5627680532665014,0.014466850126220763,0.022665768858364185
2,1.6016512088590076,1.6016154615471367,0.014949649538348118,0.02079766066422065,1.5732743248264247,1.5732390921041672,0.011811776276086488,0.023420944209697696
3,1.5925913400979934,1.5925652304309026,0.009394873999555905,0.01671479183944563,1.5653635439773743,1.565338476276029,0.005886255919486393,0.0191814442680932
4,1.5832638935562056,1.5832422238343624,0.008037392827123404,0.013632327960990369,1.5506651497907609,1.5506529312598212,0.004389845098931909,0.007828685324216683
5,1.5730397230772166,1.5730213709428504,0.006404550955755015,0.011947582545410841,1.5393705450973745,1.5393587719868433,0.003630704429302427,0.00814240556275002
6,1.5609068209832202,1.560889734450215,0.006096887725343307,0.010989644433713208,1.5393710020127156,1.5393605988804882,0.00393268780206448,0.006470443858613819
7,1.5556960111242013,1.5556800250597032,0.004851385372908165,0.011134678353990117,1.5364253887729997,1.536416051318104,0.002215998123398891,0.007121456295354721
8,1.5472293716084806,1.5472141359730893,0.004449881408487757,0.010785753250991304,1.5376727798629384,1.5376629631763563,0.0024724828640384643,0.007344203258438094
9,1.5377386652202871,1.5377246089208432,0.004001394683836649,0.010054904117737897,1.5320333086831421,1.5320234381479583,0.002515968979825487,0.0073545656927289675
10,1.5303763355983993,1.5303632570137253,0.0035464020372834057,0.009532182021113113,1.5252370608472567,1.5252292471414812,0.0017665801012226679,0.006047125301086572
11,1.5239413742602708,1.5239284287974,0.0036216123550819853,0.009323849921351454,1.526087568801462,1.5260790228395513,0.0019220287373630791,0.006623932773725286
12,1.5178844040938395,1.5178719599118922,0.003628853487720092,0.008815327861346305,1.5285864948306551,1.5285805592804418,0.00145784480745571,0.004477705150412092
13,1.513366448128461,1.5133541209039456,0.003697154223965481,0.008630069699161686,1.5228137651621834,1.5228063605461608,0.0011594079170243977,0.006245207723706856
14,1.493964278939106,1.4939521066592554,0.003826044987514615,0.008346234283262553,1.4625929038895271,1.462585111948382,0.002434664405531069,0.005357276342384075
15,1.442450902311755,1.4424375580667184,0.004395450007123873,0.008948794400699747,1.4372369659689193,1.4372274929803037,0.0031128954720576435,0.006360092633553721
16,1.4218484690335353,1.421833739867128,0.004823902113502845,0.009905263574219619,1.4184015962452226,1.418393577862445,0.001477057120545434,0.006541325324358917
17,1.4046474573712333,1.404634032148502,0.00408609597516867,0.00933912614043802,1.4029322391525334,1.4029241780234665,0.001214231661423261,0.0068468970701629165
18,1.393782944497606,1.3937697241280582,0.004237730846391059,0.008982638067790929,1.393839820505432,1.3938328304255965,0.0015756715582836546,0.005414407915481208
19,1.3836190794389494,1.3836049969396156,0.0037157638509757816,0.010366734823118895,1.3816915160783931,1.3816833395434813,0.00159585416919842,0.006580680322203337
20,1.3752576826035874,1.3752437305224599,0.0034024670153778667,0.010549613449365522,1.37725967017135,1.3772512945903275,0.0013816499311988593,0.006993930695895937
21,1.3682033459485727,1.3681883330573135,0.003497376031327682,0.01151551448830093,1.3569608714690733,1.356952237021647,0.0016319878868418544,0.007002459119885463
22,1.3625047054382544,1.3624894145385076,0.0037960920457184937,0.011494806978246197,1.354082554906149,1.3540733218517205,0.002211584785362188,0.007021469186007672
23,1.3565242391879504,1.3565088536742487,0.003464339782227762,0.011921173189735661,1.348388082003562,1.3483795003157981,0.00167128045592672,0.006910406894586373
24,1.3514116937970326,1.3513946540940358,0.0034876324504070607,0.013552069724475344,1.3479498228434061,1.3479407093725295,0.001757056643105597,0.007356413782812006
25,1.346477924079756,1.3464606443364384,0.003474392659139509,0.01380534981985887,1.3329010174973603,1.332891297526596,0.0016401604997858439,0.008079809819510985
26,1.342115865111611,1.3420978367422474,0.0035916522352956236,0.014436716268801442,1.3335975665473514,1.333586626325037,0.001855687289883675,0.009084534502154571
27,1.3371858425632883,1.337166845563751,0.004317887863943663,0.014679110791984324,1.3375967630911212,1.337586511759464,0.0018480784138190254,0.008403252781625637
28,1.332329151291059,1.3323087993705442,0.004543405316843807,0.015808514225048323,1.3290810069455739,1.32907059360668,0.0016266307488467745,0.008786707625236572
29,1.3276113688007833,1.327590043110573,0.00435470470150467,0.016970984523246687,1.31586983192116,1.3158569589821718,0.0017300802994723518,0.0111428580506437
30,1.3229092144771808,1.3228868513941403,0.004740059903392103,0.017623022104240955,1.3160240777779413,1.3160125110858845,0.0015123361154589202,0.010054355463722957
31,1.3189346349638353,1.318911649693862,0.004340909880021354,0.018644358993073304,1.307094532143137,1.3070827713829298,0.0014560696595609778,0.010304689973840748
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128,1.0987918005478532,1.0987227214185695,0.01549821989076833,0.05358090614428123,1.1574740549676144,1.1574273199562681,0.010423474568034295,0.036311534758665394
129,1.097936906507925,1.097866611123858,0.01573018241431564,0.05456519829134147,1.17646131215858,1.1764111641048147,0.010843964044247218,0.039304087420853284
130,1.0963286039676112,1.096257730599811,0.016531729388982057,0.054341634995738665,1.1641203865032863,1.1640771051501126,0.00925416419801698,0.034027186713584075
131,1.0954336608812514,1.0953628024613526,0.016605859159181516,0.05425255740086238,1.167589733765025,1.1675413260507084,0.011489737290417572,0.03691797470776112
132,1.0949290720277003,1.0948575271603156,0.01615555529544751,0.055389308756589886,1.1645407236296461,1.1644969236320375,0.009219139322485902,0.034580856359999114
133,1.0932088060644156,1.0931376210799868,0.01606268973412613,0.055122291190425554,1.1616800353341055,1.1616311589914639,0.010062530057010157,0.03881381019192319
134,1.0911050979599004,1.0910329187861683,0.01625478942456345,0.055924380965034166,1.1639982395062975,1.1639501550440279,0.010831357206435277,0.03725310277272814
135,1.0902300318818192,1.0901578361055608,0.017418327573562663,0.05477744528452555,1.169708026298507,1.1696625361486175,0.010366947889524214,0.03512319979130481
136,1.0893180483942433,1.0892455820703244,0.016344809635542332,0.0561215108046929,1.1573103816485597,1.1572609050767633,0.01016809413347178,0.03930847528823205
137,1.0881204923898404,1.0880444842214587,0.01777587878505389,0.05823228598634402,1.1646966360710898,1.164648430457445,0.00910114537074279,0.03910446608195186
138,1.0867420351127903,1.086669418839409,0.01637926310089727,0.056237006856997805,1.154515856249022,1.1544688589031409,0.010443763123060786,0.03655358035752349
139,1.0858163016556737,1.0857435543514973,0.017750837769856056,0.05499646292279164,1.1708537156349703,1.1708045382490404,0.010705676579394193,0.03847170684652862
140,1.0848932800438824,1.0848204776043504,0.01705099232221643,0.05575144381622473,1.1566992016820445,1.1566509332799209,0.010274558522940859,0.03799384125612605
141,1.0838102029688552,1.0837364500532418,0.01774556956415375,0.05600734253923098,1.165036605855451,1.1649830709855906,0.01236207319809166,0.041172794128366864
142,1.082329976164038,1.0822565493321186,0.017822257623448967,0.05560457075039546,1.1602403150230531,1.1601926323962832,0.010840140997780311,0.0368424836292743
143,1.0817375559581013,1.0816640982189087,0.017782981498663624,0.05567475408613682,1.1563600736515323,1.156307287207743,0.01155951153776515,0.04122692995569134
144,1.0812540918115645,1.0811798367919228,0.01659942527214686,0.05765559082229932,1.1587367155345751,1.1586904966638765,0.010777310460762291,0.035441557978277846
145,1.0796190532414331,1.0795452983560287,0.01655470008465151,0.05720018182595571,1.1649731530876775,1.1649221037313207,0.009566046342344977,0.04148330755978714
146,1.0785637602859277,1.0784889548869052,0.016893696349114178,0.05791169910132885,1.1608038146323927,1.1607520897607893,0.010624139945771499,0.041100729245967735
147,1.0780288675338354,1.077955676300388,0.01637685895115137,0.056814371033012866,1.1674047762196564,1.1673554676941063,0.010706330180146351,0.03860219284870281
148,1.078225809988631,1.0781528622164651,0.0165121356876567,0.05643563301066558,1.1586187191390929,1.1585667149230583,0.008510634291851681,0.04349357944088167
149,1.07494183770226,1.0748690716380032,0.016246318714941543,0.056519742129246396,1.1636775165478372,1.1636287508007672,0.010345299943842827,0.03842044485448134
================================================
FILE: Trained_models/DeepDock_pdbbindv2019_13K_minTestLoss.chk
================================================
[File too large to display: 14.6 MB]
================================================
FILE: Validation_CASF2016/CASF2016_DockingPower_DeepDock.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"RDKit WARNING: [13:44:39] Enabling RDKit 2019.09.1 jupyter extensions\n"
]
},
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x7fa66808cf90>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch_geometric.data import DataLoader\n",
"import torch_geometric\n",
"import torch.distributions as D\n",
"import matplotlib.pyplot as plt\n",
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import AllChem, Draw, rdFMCS, rdMolTransforms\n",
"from rdkit.Chem.rdMolAlign import AlignMol\n",
"from rdkit.Chem import PandasTools\n",
"from rdkit import rdBase\n",
"import glob\n",
"import os\n",
"\n",
"import deepdock \n",
"from deepdock.utils.distributions import *\n",
"from deepdock.utils.data import *\n",
"from deepdock.models import *\n",
"\n",
"%matplotlib inline\n",
"np.random.seed(123)\n",
"torch.cuda.manual_seed_all(123)\n",
"torch.manual_seed(123)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"#device = 'cpu'\n",
"\n",
"ligand_model = LigandNet(28, residual_layers=10, dropout_rate=0.10)\n",
"target_model = TargetNet(4, residual_layers=10, dropout_rate=0.10)\n",
"model = DeepDock(ligand_model, target_model, hidden_dim=64, n_gaussians=10, dropout_rate=0.10, dist_threhold=7.).to(device)\n",
"\n",
"checkpoint = torch.load(deepdock.__path__[0]+'/../Trained_models/DeepDock_pdbbindv2019_13K_minTestLoss.chk', map_location=torch.device(device))\n",
"model.load_state_dict(checkpoint['model_state_dict']) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Complexes from pdbBind: 285\n"
]
}
],
"source": [
"db_complex = PDBbind_complex_dataset(data_path=deepdock.__path__[0]+'/../data/dataset_CASF-2016_285.tar', \n",
" min_target_nodes=None, max_ligand_nodes=None)\n",
"print('Complexes from pdbBind:', len(db_complex))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class input_dataset(Dataset):\n",
" def __init__(self, mols, target_mesh, labels=None, transform=None, pre_transform=None):\n",
" super(input_dataset, self).__init__()\n",
" \n",
" self.mols = [from_networkx(mol2graph.mol_to_nx(m)) for m in mols]\n",
" self.target_mesh = target_mesh\n",
" self.labels = labels\n",
" if labels is None:\n",
" self.labels = range(len(self.mols))\n",
" \n",
" def len(self):\n",
" return len(self.mols)\n",
"\n",
" def get(self, idx):\n",
" return self.mols[idx], self.target_mesh, self.labels[idx]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6min 1s, sys: 7.56 s, total: 6min 9s\n",
"Wall time: 3min 50s\n"
]
}
],
"source": [
"%%time\n",
"from torch_scatter import scatter_add\n",
"results = []\n",
"\n",
"for target_data in db_complex:\n",
" model.eval()\n",
" ligand, target, _, pdbid = target_data\n",
" decoys = Mol2MolSupplier(file=deepdock.__path__[0]+'/../data/CASF-2016/decoys_docking/'+pdbid+'_decoys.mol2', \n",
" sanitize=False, cleanupSubstructures=False)\n",
" decoy_names = [m.GetProp('Name') for m in decoys]\n",
" db_decoys = input_dataset(decoys, target, decoy_names)\n",
" loader_decoys = DataLoader(db_decoys, batch_size=20, shuffle=False)\n",
" #print(pdbid)\n",
"\n",
" for data in loader_decoys:\n",
" decoy, target, cpd_name = data\n",
" decoy, target = decoy.to(device), target.to(device)\n",
" pi, sigma, mu, dist, atom_types, bond_types, batch = model(decoy, target)\n",
"\n",
" normal = Normal(mu, sigma)\n",
" logprob = normal.log_prob(dist.expand_as(normal.loc))\n",
" logprob += torch.log(pi)\n",
" prob = logprob.exp().sum(1)\n",
" prob_all = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 10)[0]] = 0.\n",
" prob_10 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 7)[0]] = 0.\n",
" prob_7 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 5)[0]] = 0.\n",
" prob_5 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 3)[0]] = 0.\n",
" prob_3 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob = torch.stack([prob_3, prob_5, prob_7, prob_10, prob_all],dim=1)\n",
" #print(pdbid, cpd_name, prob_all.cpu().detach().numpy())\n",
" results.append(np.concatenate([np.expand_dims(np.repeat(pdbid, len(cpd_name)), axis=1), \n",
" np.expand_dims(cpd_name, axis=1), \n",
" prob.cpu().detach().numpy()], axis=1))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(22492, 7)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PDB_ID</th>\n",
" <th>Cpd_Name</th>\n",
" <th>Score_3A</th>\n",
" <th>Score_5A</th>\n",
" <th>Score_7A</th>\n",
" <th>Score_10A</th>\n",
" <th>Score_all</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4k18</td>\n",
" <td>4k18_100</td>\n",
" <td>41.7124709276148</td>\n",
" <td>355.00999393874434</td>\n",
" <td>1234.1988897433216</td>\n",
" <td>1265.8331969710105</td>\n",
" <td>1265.8335978696152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4k18</td>\n",
" <td>4k18_105</td>\n",
" <td>36.50107725111608</td>\n",
" <td>320.5826389497487</td>\n",
" <td>1098.7196528481893</td>\n",
" <td>1128.4331804224062</td>\n",
" <td>1128.4344001617458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4k18</td>\n",
" <td>4k18_107</td>\n",
" <td>56.13413997037191</td>\n",
" <td>406.6658214522255</td>\n",
" <td>1261.5476637591444</td>\n",
" <td>1298.835192687907</td>\n",
" <td>1298.835338794891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4k18</td>\n",
" <td>4k18_118</td>\n",
" <td>44.23784542273988</td>\n",
" <td>336.9721288402966</td>\n",
" <td>1252.596765731967</td>\n",
" <td>1287.006223774616</td>\n",
" <td>1287.0066010553867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4k18</td>\n",
" <td>4k18_122</td>\n",
" <td>42.72467596870927</td>\n",
" <td>342.7847157990617</td>\n",
" <td>1211.367931058725</td>\n",
" <td>1252.029400636168</td>\n",
" <td>1252.029755610901</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDB_ID Cpd_Name Score_3A Score_5A Score_7A \\\n",
"0 4k18 4k18_100 41.7124709276148 355.00999393874434 1234.1988897433216 \n",
"1 4k18 4k18_105 36.50107725111608 320.5826389497487 1098.7196528481893 \n",
"2 4k18 4k18_107 56.13413997037191 406.6658214522255 1261.5476637591444 \n",
"3 4k18 4k18_118 44.23784542273988 336.9721288402966 1252.596765731967 \n",
"4 4k18 4k18_122 42.72467596870927 342.7847157990617 1211.367931058725 \n",
"\n",
" Score_10A Score_all \n",
"0 1265.8331969710105 1265.8335978696152 \n",
"1 1128.4331804224062 1128.4344001617458 \n",
"2 1298.835192687907 1298.835338794891 \n",
"3 1287.006223774616 1287.0066010553867 \n",
"4 1252.029400636168 1252.029755610901 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = np.concatenate(results, axis=0)\n",
"results = pd.DataFrame(np.asarray(results), columns=['PDB_ID', 'Cpd_Name', 'Score_3A', 'Score_5A', 'Score_7A', 'Score_10A', 'Score_all'])\n",
"results.to_csv('Score_decoys_docking_CASF2016.csv', index=False)\n",
"print(results.shape)\n",
"results.head() "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('Score_decoys_docking_CASF2016.csv')\n",
"pdbids = df.PDB_ID.unique()\n",
"print(len(pdbids))\n",
"\n",
"for pdbid in pdbids:\n",
"\n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_3A']]\n",
" df1.columns= ['#code', 'score']\n",
" df1.to_csv('DockingPower_DeepDock_3A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_5A']]\n",
" df1.columns= ['#code', 'score']\n",
" df1.to_csv('DockingPower_DeepDock_5A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_7A']]\n",
" df1.columns= ['#code', 'score']\n",
" df1.to_csv('DockingPower_DeepDock_7A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_10A']]\n",
" df1.columns= ['#code', 'score']\n",
" df1.to_csv('DockingPower_DeepDock_10A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_all']]\n",
" df1.columns= ['#code', 'score']\n",
" df1.to_csv('DockingPower_DeepDock_all/scores/'+pdbid+'_score.dat', index=False, sep='\\t')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"python /data/CASF-2016/power_docking/docking_power.py -c /data/CASF-2016/power_docking/CoreSet.dat -s scores -r /data/CASF-2016/decoys_docking/ -p 'positive' -l 2 -o 'DeepDock' > DockingPower_DeepDock_all.out\n"
]
},
{
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: Validation_CASF2016/CASF2016_ScoringPower_DeepDock.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"RDKit WARNING: [13:42:42] Enabling RDKit 2019.09.1 jupyter extensions\n"
]
},
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x7fbb761d30d0>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch_geometric.data import DataLoader\n",
"import torch_geometric\n",
"import torch.distributions as D\n",
"import matplotlib.pyplot as plt\n",
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import AllChem, Draw, rdFMCS, rdMolTransforms\n",
"from rdkit.Chem.rdMolAlign import AlignMol\n",
"from rdkit.Chem import PandasTools\n",
"from rdkit import rdBase\n",
"import glob\n",
"import os\n",
"\n",
"import deepdock\n",
"from deepdock.utils.distributions import *\n",
"from deepdock.utils.data import *\n",
"from deepdock.models import *\n",
"\n",
"%matplotlib inline\n",
"np.random.seed(123)\n",
"torch.cuda.manual_seed_all(123)\n",
"torch.manual_seed(123)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"#device = 'cpu'\n",
"\n",
"ligand_model = LigandNet(28, residual_layers=10, dropout_rate=0.10)\n",
"target_model = TargetNet(4, residual_layers=10, dropout_rate=0.10)\n",
"model = DeepDock(ligand_model, target_model, hidden_dim=64, n_gaussians=10, dropout_rate=0.10, dist_threhold=7.).to(device)\n",
"\n",
"checkpoint = torch.load(deepdock.__path__[0]+'/../Trained_models/DeepDock_pdbbindv2019_13K_minTestLoss.chk', map_location=torch.device(device))\n",
"model.load_state_dict(checkpoint['model_state_dict']) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Complexes from pdbBind: 285\n"
]
}
],
"source": [
"db_complex = PDBbind_complex_dataset(data_path=deepdock.__path__[0]+'/../data/dataset_CASF-2016_285.tar', \n",
" min_target_nodes=None, max_ligand_nodes=None)\n",
"print('Complexes from pdbBind:', len(db_complex))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.46 s, sys: 333 ms, total: 6.79 s\n",
"Wall time: 4.8 s\n"
]
}
],
"source": [
"%%time\n",
"from torch_scatter import scatter_add\n",
"results = []\n",
"\n",
"model.eval()\n",
"loader = DataLoader(db_complex, batch_size=20, shuffle=False)\n",
"\n",
"for data in loader:\n",
" ligand, target, _, pdbid = data\n",
" ligand, target = ligand.to(device), target.to(device)\n",
" pi, sigma, mu, dist, atom_types, bond_types, batch = model(ligand, target)\n",
"\n",
" normal = Normal(mu, sigma)\n",
" logprob = normal.log_prob(dist.expand_as(normal.loc))\n",
" logprob += torch.log(pi)\n",
" prob = logprob.exp().sum(1)\n",
" prob_all = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 10)[0]] = 0.\n",
" prob_10 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 7)[0]] = 0.\n",
" prob_7 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 5)[0]] = 0.\n",
" prob_5 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 3)[0]] = 0.\n",
" prob_3 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob = torch.stack([prob_3, prob_5, prob_7, prob_10, prob_all],dim=1)\n",
" #print(pdbid, cpd_name, prob_all.cpu().detach().numpy())\n",
" results.append(np.concatenate([np.expand_dims(pdbid, axis=1), \n",
" prob.cpu().detach().numpy()], axis=1))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(285, 6)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PDB_ID</th>\n",
" <th>Score_3A</th>\n",
" <th>Score_5A</th>\n",
" <th>Score_7A</th>\n",
" <th>Score_10A</th>\n",
" <th>Score_all</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4k18</td>\n",
" <td>64.25368680000449</td>\n",
" <td>420.5898705181299</td>\n",
" <td>1376.762661813891</td>\n",
" <td>1410.0162568367755</td>\n",
" <td>1410.01638602799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4qac</td>\n",
" <td>83.2844061298711</td>\n",
" <td>450.01256968999587</td>\n",
" <td>1128.0662764096696</td>\n",
" <td>1154.7673063010277</td>\n",
" <td>1154.7690904503804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1o3f</td>\n",
" <td>187.7132053065723</td>\n",
" <td>778.5596122182768</td>\n",
" <td>1779.2439875640941</td>\n",
" <td>1823.1001001165655</td>\n",
" <td>1823.110781221914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4ih7</td>\n",
" <td>35.630378250691514</td>\n",
" <td>270.11676199153</td>\n",
" <td>787.7759051254867</td>\n",
" <td>814.7537476029596</td>\n",
" <td>814.755215144911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3dx1</td>\n",
" <td>49.94476787861658</td>\n",
" <td>202.24570100387868</td>\n",
" <td>449.72902901247784</td>\n",
" <td>462.38353806385936</td>\n",
" <td>462.39191325604116</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDB_ID Score_3A Score_5A Score_7A \\\n",
"0 4k18 64.25368680000449 420.5898705181299 1376.762661813891 \n",
"1 4qac 83.2844061298711 450.01256968999587 1128.0662764096696 \n",
"2 1o3f 187.7132053065723 778.5596122182768 1779.2439875640941 \n",
"3 4ih7 35.630378250691514 270.11676199153 787.7759051254867 \n",
"4 3dx1 49.94476787861658 202.24570100387868 449.72902901247784 \n",
"\n",
" Score_10A Score_all \n",
"0 1410.0162568367755 1410.01638602799 \n",
"1 1154.7673063010277 1154.7690904503804 \n",
"2 1823.1001001165655 1823.110781221914 \n",
"3 814.7537476029596 814.755215144911 \n",
"4 462.38353806385936 462.39191325604116 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = np.concatenate(results, axis=0)\n",
"results = pd.DataFrame(np.asarray(results), columns=['PDB_ID', 'Score_3A', 'Score_5A', 'Score_7A', 'Score_10A', 'Score_all'])\n",
"results.to_csv('Score_CoreSet_docking_CASF2016.csv', index=False)\n",
"print(results.shape)\n",
"results.head() "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('Score_CoreSet_docking_CASF2016.csv')\n",
"\n",
"df1 = df[['PDB_ID', 'Score_3A']]\n",
"df1.columns= ['#code', 'score']\n",
"df1.to_csv('ScoringPower_Deepdock/scores/Deepdock_3A.dat', index=False, sep='\\t')\n",
" \n",
"df1 = df[['PDB_ID', 'Score_5A']]\n",
"df1.columns= ['#code', 'score']\n",
"df1.to_csv('ScoringPower_Deepdock/scores/Deepdock_5A.dat', index=False, sep='\\t')\n",
" \n",
"df1 = df[['PDB_ID', 'Score_7A']]\n",
"df1.columns= ['#code', 'score']\n",
"df1.to_csv('ScoringPower_Deepdock/scores/Deepdock_7A.dat', index=False, sep='\\t')\n",
" \n",
"df1 = df[['PDB_ID', 'Score_10A']]\n",
"df1.columns= ['#code', 'score']\n",
"df1.to_csv('ScoringPower_Deepdock/scores/Deepdock_10A.dat', index=False, sep='\\t')\n",
" \n",
"df1 = df[['PDB_ID', 'Score_all']]\n",
"df1.columns= ['#code', 'score']\n",
"df1.to_csv('ScoringPower_Deepdock/scores/Deepdock_all.dat', index=False, sep='\\t')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"python /data/CASF-2016/power_scoring/scoring_power.py -c /data/CASF-2016/power_scoring/CoreSet.dat -s ./scores/Deepdock_all.dat -p 'positive' -o 'Deepdock' > ScoringPower_Deepdock_all.out\n",
"\n",
"python /data/CASF-2016/power_ranking/ranking_power.py -c /data/CASF-2016/power_ranking/CoreSet.dat -s ./scores/Deepdock_all.dat -p 'positive' -o 'Deepdock' > RankingPower_Deepdock_all.out\n"
]
}
],
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: Validation_CASF2016/CASF2016_ScreeningPower_DeepDock.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"RDKit WARNING: [14:02:01] Enabling RDKit 2019.09.1 jupyter extensions\n"
]
},
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x7f9b8d51b270>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch_geometric.data import DataLoader\n",
"import torch_geometric\n",
"import torch.distributions as D\n",
"import matplotlib.pyplot as plt\n",
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import AllChem, Draw, rdFMCS, rdMolTransforms\n",
"from rdkit.Chem.rdMolAlign import AlignMol\n",
"from rdkit.Chem import PandasTools\n",
"from rdkit import rdBase\n",
"import glob\n",
"import os\n",
"\n",
"import deepdock\n",
"from deepdock.utils.distributions import *\n",
"from deepdock.utils.data import *\n",
"from deepdock.models import *\n",
"\n",
"%matplotlib inline\n",
"np.random.seed(123)\n",
"torch.cuda.manual_seed_all(123)\n",
"torch.manual_seed(123)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"ligand_model = LigandNet(28, residual_layers=10, dropout_rate=0.10)\n",
"target_model = TargetNet(4, residual_layers=10, dropout_rate=0.10)\n",
"model = DeepDock(ligand_model, target_model, hidden_dim=64, n_gaussians=10, dropout_rate=0.10, dist_threhold=7.).to(device)\n",
"\n",
"checkpoint = torch.load(deepdock.__path__[0]+'/../Trained_models/DeepDock_pdbbindv2019_13K_minTestLoss.chk', map_location=torch.device(device))\n",
"model.load_state_dict(checkpoint['model_state_dict']) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Complexes from pdbBind: 285\n"
]
}
],
"source": [
"db_complex = PDBbind_complex_dataset(data_path=deepdock.__path__[0]+'/../data/dataset_CASF-2016_285.tar', \n",
" min_target_nodes=None, max_ligand_nodes=None)\n",
"print('Complexes from pdbBind:', len(db_complex))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class input_dataset(Dataset):\n",
" def __init__(self, mols, target_mesh, labels=None, transform=None, pre_transform=None):\n",
" super(input_dataset, self).__init__()\n",
" \n",
" self.mols = [from_networkx(mol2graph.mol_to_nx(m)) for m in mols]\n",
" self.target_mesh = target_mesh\n",
" self.labels = labels\n",
" if labels is None:\n",
" self.labels = range(len(self.mols))\n",
" \n",
" def len(self):\n",
" return len(self.mols)\n",
"\n",
" def get(self, idx):\n",
" return self.mols[idx], self.target_mesh, self.labels[idx]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6h 54min 8s, sys: 9min 26s, total: 7h 3min 35s\n",
"Wall time: 4h 25min 33s\n"
]
}
],
"source": [
"%%time\n",
"import glob\n",
"from torch_scatter import scatter_add\n",
"target_files = [f.split('/')[-1] for f in glob.glob(deepdock.__path__[0]+'/../data/CASF-2016/decoys_screening/*', recursive=False)]\n",
"results = []\n",
"\n",
"\n",
"for target_data in db_complex:\n",
" model.eval()\n",
" ligand, target, _, pdbid = target_data\n",
" if pdbid in target_files: \n",
" decoy_files = [f.split('/')[-1] for f in glob.glob(deepdock.__path__[0]+'/../data/CASF-2016/decoys_screening/'+pdbid+'/*.mol2', recursive=False)]\n",
" else:\n",
" continue\n",
" \n",
" for file in decoy_files:\n",
" decoys = Mol2MolSupplier(file=deepdock.__path__[0]+'/../data/CASF-2016/decoys_screening/'+pdbid+'/'+file, \n",
" sanitize=False, cleanupSubstructures=False)\n",
" decoy_names = [m.GetProp('Name') for m in decoys]\n",
" db_decoys = input_dataset(decoys, target, decoy_names)\n",
" loader_decoys = DataLoader(db_decoys, batch_size=20, shuffle=False)\n",
" #print(pdbid)\n",
"\n",
" for data in loader_decoys:\n",
" decoy, target_temp, cpd_name = data\n",
" decoy, target_temp = decoy.to(device), target_temp.to(device)\n",
" pi, sigma, mu, dist, atom_types, bond_types, batch = model(decoy, target_temp)\n",
"\n",
" normal = Normal(mu, sigma)\n",
" logprob = normal.log_prob(dist.expand_as(normal.loc))\n",
" logprob += torch.log(pi)\n",
" prob = logprob.exp().sum(1)\n",
" prob_all = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 10)[0]] = 0.\n",
" prob_10 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob[torch.where(dist > 7)[0]] = 0.\n",
" prob_7 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 5)[0]] = 0.\n",
" prob_5 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
"\n",
" prob[torch.where(dist > 3)[0]] = 0.\n",
" prob_3 = scatter_add(prob, batch, dim=0, dim_size=batch.unique().size(0))\n",
" \n",
" prob = torch.stack([prob_3, prob_5, prob_7, prob_10, prob_all],dim=1)\n",
" #print(pdbid, cpd_name, prob_all.cpu().detach().numpy())\n",
" results.append(np.concatenate([np.expand_dims(np.repeat(pdbid, len(cpd_name)), axis=1), \n",
" np.expand_dims(cpd_name, axis=1), \n",
" prob.cpu().detach().numpy()], axis=1))\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1624500, 7)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PDB_ID</th>\n",
" <th>Cpd_Name</th>\n",
" <th>Score_3A</th>\n",
" <th>Score_5A</th>\n",
" <th>Score_7A</th>\n",
" <th>Score_10A</th>\n",
" <th>Score_all</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1o3f</td>\n",
" <td>4de2_ligand_1</td>\n",
" <td>7.254809622430958</td>\n",
" <td>89.94999521484337</td>\n",
" <td>447.3108304509815</td>\n",
" <td>471.33409258768546</td>\n",
" <td>471.33795008409413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1o3f</td>\n",
" <td>4de2_ligand_10</td>\n",
" <td>2.2435853524573153</td>\n",
" <td>64.8291232744619</td>\n",
" <td>469.6149662619709</td>\n",
" <td>497.22048202034364</td>\n",
" <td>497.22063628573727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1o3f</td>\n",
" <td>4de2_ligand_100</td>\n",
" <td>3.451115585675536</td>\n",
" <td>78.15551585586073</td>\n",
" <td>506.3139331427199</td>\n",
" <td>554.3860309102873</td>\n",
" <td>554.3878273892042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1o3f</td>\n",
" <td>4de2_ligand_106</td>\n",
" <td>4.784300354428733</td>\n",
" <td>102.6058833211317</td>\n",
" <td>590.7510395276462</td>\n",
" <td>620.5011685339421</td>\n",
" <td>620.5046101480829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1o3f</td>\n",
" <td>4de2_ligand_108</td>\n",
" <td>2.349412635601983</td>\n",
" <td>76.80465950772964</td>\n",
" <td>539.9298935579708</td>\n",
" <td>575.3306395604491</td>\n",
" <td>575.3335251313035</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDB_ID Cpd_Name Score_3A Score_5A \\\n",
"0 1o3f 4de2_ligand_1 7.254809622430958 89.94999521484337 \n",
"1 1o3f 4de2_ligand_10 2.2435853524573153 64.8291232744619 \n",
"2 1o3f 4de2_ligand_100 3.451115585675536 78.15551585586073 \n",
"3 1o3f 4de2_ligand_106 4.784300354428733 102.6058833211317 \n",
"4 1o3f 4de2_ligand_108 2.349412635601983 76.80465950772964 \n",
"\n",
" Score_7A Score_10A Score_all \n",
"0 447.3108304509815 471.33409258768546 471.33795008409413 \n",
"1 469.6149662619709 497.22048202034364 497.22063628573727 \n",
"2 506.3139331427199 554.3860309102873 554.3878273892042 \n",
"3 590.7510395276462 620.5011685339421 620.5046101480829 \n",
"4 539.9298935579708 575.3306395604491 575.3335251313035 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = np.concatenate(results, axis=0)\n",
"results = pd.DataFrame(np.asarray(results), columns=['PDB_ID', 'Cpd_Name', 'Score_3A', 'Score_5A', 'Score_7A', 'Score_10A', 'Score_all'])\n",
"results.to_csv('Score_decoys_screening_CASF2016.csv', index=False)\n",
"print(results.shape)\n",
"results.head() "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('Score_decoys_screening_CASF2016.csv')\n",
"pdbids = df.PDB_ID.unique()\n",
"print(len(pdbids))\n",
"\n",
"for pdbid in pdbids:\n",
"\n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_3A']]\n",
" df1.columns= ['#code_ligand_num', 'score']\n",
" df1.to_csv('ScreeningPower_DeepDock_3A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_5A']]\n",
" df1.columns= ['#code_ligand_num', 'score']\n",
" df1.to_csv('ScreeningPower_DeepDock_5A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_7A']]\n",
" df1.columns= ['#code_ligand_num', 'score']\n",
" df1.to_csv('ScreeningPower_DeepDock_7A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_10A']]\n",
" df1.columns= ['#code_ligand_num', 'score']\n",
" df1.to_csv('ScreeningPower_DeepDock_10A/scores/'+pdbid+'_score.dat', index=False, sep='\\t')\n",
" \n",
" df1 = df[df.PDB_ID==pdbid][['Cpd_Name', 'Score_all']]\n",
" df1.columns= ['#code_ligand_num', 'score']\n",
" df1.to_csv('ScreeningPower_DeepDock_all/scores/'+pdbid+'_score.dat', index=False, sep='\\t')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"python /data/CASF-2016/power_screening/forward_screening_power.py -c /data/CASF-2016/power_screening/CoreSet.dat -s scores -t /data/CASF-2016/power_screening/TargetInfo.dat -p 'positive' -o 'Forward_DeepDock' > ForwardScreeningPower_DeepDock_all.out\n",
"\n",
"python /data/CASF-2016/power_screening/reverse_screening_power.py -c /data/CASF-2016/power_screening/CoreSet.dat -s scores -l /data/CASF-2016/power_screening/LigandInfo.dat -p 'positive' -o 'Reverse_DeepDock' > ReverseScreeningPower_DeepDock_all.out\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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================================================
FILE: Validation_CASF2016/DockingPower_DeepDock_10A/DockingPower_DeepDock_10A.out
================================================
code Rank1 RMSD1 Rank2 RMSD2 Rank3 RMSD3
1 4llx 4llx_386 0.33 4llx_302 0.27 4llx_519 1.25
2 5c28 5c28_47 0.56 5c28_531 2.52 5c28_382 2.10
3 3uuo 3uuo_709 0.60 3uuo_712 0.60 3uuo_711 0.55
4 3ui7 3ui7_333 0.45 3ui7_16 0.63 3ui7_335 0.90
5 5c2h 5c2h_244 0.59 5c2h_247 0.94 5c2h_240 0.46
6 2v00 2v00_79 0.74 2v00_544 1.87 2v00_605 1.16
7 3wz8 3wz8_376 0.84 3wz8_371 0.83 3wz8_391 0.96
8 3pww 3pww_889 1.50 3pww_394 1.13 3pww_816 1.65
9 3prs 3prs_711 1.08 3prs_701 1.22 3prs_706 1.74
10 3uri 3uri_1000 0.94 3uri_1002 0.93 3uri_1001 0.91
11 4m0z 4m0z_516 0.59 4m0z_686 1.31 4m0z_41 0.68
12 4m0y 4m0y_814 1.81 4m0y_920 1.74 4m0y_952 1.65
13 3qgy 3qgy_306 1.03 3qgy_20 1.13 3qgy_318 0.90
14 4qd6 4qd6_954 1.59 4qd6_978 0.70 4qd6_939 1.27
15 4rfm 4rfm_85 0.43 4rfm_354 0.71 4rfm_9 0.37
16 4cr9 4cr9_423 1.85 4cr9_358 0.95 4cr9_690 1.66
17 4cra 4cra_258 0.87 4cra_318 0.47 4cra_364 0.45
18 4x6p 4x6p_312 0.67 4x6p_201 0.86 4x6p_254 0.57
19 4crc 4crc_291 0.85 4crc_217 0.88 4crc_368 0.73
20 4ty7 4ty7_106 0.56 4ty7_925 0.95 4ty7_62 1.05
21 5aba 5aba_772 1.67 5aba_525 9.03 5aba_657 2.90
22 5a7b 5a7b_623 1.71 5a7b_243 1.60 5a7b_286 0.74
23 4agn 4agn_228 1.07 4agn_603 1.71 4agn_280 0.89
24 4agp 4agp_656 1.92 4agp_612 2.14 4agp_350 0.97
25 4agq 4agq_529 1.95 4agq_234 1.94 4agq_272 2.01
26 3bgz 3bgz_653 1.48 3bgz_621 1.05 3bgz_472 1.94
27 3jya 3jya_471 1.04 3jya_413 1.38 3jya_490 0.85
28 2c3i 2c3i_818 1.74 2c3i_556 2.88 2c3i_823 0.88
29 4k18 4k18_225 0.31 4k18_85 0.53 4k18_254 0.36
30 5dwr 5dwr_382 0.46 5dwr_289 0.54 5dwr_193 0.94
31 3mss 3mss_948 2.58 3mss_836 3.05 3mss_873 2.89
32 3k5v 3k5v_152 8.28 3k5v_626 6.68 3k5v_810 3.09
33 3pyy 3pyy_812 1.63 3pyy_822 1.59 3pyy_832 2.38
34 2v7a 2v7a_338 0.60 2v7a_859 0.95 2v7a_784 1.76
35 4twp 4twp_379 0.76 4twp_358 0.80 4twp_352 0.95
36 3wtj 3wtj_328 0.31 3wtj_307 0.38 3wtj_222 1.05
37 3zdg 3zdg_286 2.01 3zdg_758 2.33 3zdg_792 1.53
38 3u8k 3u8k_564 4.38 3u8k_557 5.05 3u8k_584 4.59
39 4qac 4qac_476 2.48 4qac_581 2.49 4qac_945 0.87
40 3u8n 3u8n_928 0.84 3u8n_531 1.17 3u8n_869 2.66
41 1a30 1a30_353 3.15 1a30_140 2.70 1a30_170 2.97
42 2qnq 2qnq_277 2.31 2qnq_207 0.98 2qnq_287 1.42
43 1g2k 1g2k_265 0.69 1g2k_242 1.13 1g2k_74 0.60
44 1eby 1eby_262 1.01 1eby_295 0.87 1eby_238 0.95
45 3o9i 3o9i_227 0.79 3o9i_38 0.68 3o9i_222 0.93
46 4lzs 4lzs_70 0.69 4lzs_1 0.75 4lzs_61 0.87
47 3u5j 3u5j_65 0.41 3u5j_54 0.63 3u5j_251 0.79
48 4wiv 4wiv_829 0.86 4wiv_486 3.84 4wiv_838 0.87
49 4ogj 4ogj_297 4.94 4ogj_795 3.64 4ogj_661 5.40
50 3p5o 3p5o_759 0.69 3p5o_818 0.83 3p5o_108 0.92
51 1ps3 1ps3_707 0.98 1ps3_734 0.86 1ps3_161 0.28
52 3dx1 3dx1_382 0.79 3dx1_457 2.00 3dx1_25 0.33
53 3d4z 3d4z_340 0.47 3d4z_376 0.31 3d4z_132 0.34
54 3dx2 3dx2_990 0.58 3dx2_820 0.37 3dx2_380 0.41
55 3ejr 3ejr_87 1.26 3ejr_360 0.41 3ejr_964 0.65
56 3l7b 3l7b_38 0.42 3l7b_9 0.40 3l7b_47 0.77
57 4eky 4eky_899 0.67 4eky_38 0.63 4eky_32 0.89
58 3g2n 3g2n_35 0.79 3g2n_49 0.48 3g2n_55 0.78
59 3syr 3syr_862 0.78 3syr_477 1.35 3syr_408 0.85
60 3ebp 3ebp_476 3.98 3ebp_691 2.91 3ebp_597 3.81
61 2w66 2w66_534 3.26 2w66_453 2.28 2w66_773 3.27
62 2w4x 2w4x_756 2.01 2w4x_407 0.70 2w4x_417 0.86
63 2wca 2wca_773 5.59 2wca_731 1.88 2wca_951 3.72
64 2xj7 2xj7_812 0.40 2xj7_871 1.64 2xj7_914 0.27
65 2vvn 2vvn_406 1.13 2vvn_401 1.83 2vvn_205 0.61
66 3aru 3aru_573 2.65 3aru_311 0.67 3aru_450 2.24
67 3arv 3arv_515 3.27 3arv_821 4.42 3arv_345 0.70
68 3ary 3ary_858 6.03 3ary_762 1.40 3ary_844 1.23
69 3arq 3arq_639 9.10 3arq_655 7.80 3arq_643 8.72
70 3arp 3arp_17 1.39 3arp_67 0.98 3arp_284 1.83
71 4ih5 4ih5_867 1.96 4ih5_192 3.09 4ih5_159 2.57
72 4ih7 4ih7_461 2.23 4ih7_12 0.69 4ih7_438 1.86
73 3cj4 3cj4_166 0.50 3cj4_179 0.76 3cj4_338 0.64
74 4eo8 4eo8_30 0.50 4eo8_318 0.54 4eo8_206 0.55
75 3gnw 3gnw_729 2.21 3gnw_155 5.15 3gnw_722 1.53
76 1gpk 1gpk_159 0.76 1gpk_415 1.62 1gpk_328 0.52
77 1gpn 1gpn_781 4.18 1gpn_600 1.09 1gpn_658 2.56
78 1h23 1h23_815 1.15 1h23_347 0.89 1h23_12 1.81
79 1h22 1h22_346 0.64 1h22_187 1.33 1h22_384 1.95
80 1e66 1e66_661 2.74 1e66_712 1.76 1e66_404 0.97
81 3f3a 3f3a_612 1.97 3f3a_552 2.20 3f3a_65 0.32
82 3f3c 3f3c_508 0.97 3f3c_686 2.83 3f3c_466 1.55
83 4mme 4mme_16 0.59 4mme_71 0.55 4mme_324 0.60
84 3f3d 3f3d_287 1.55 3f3d_666 2.02 3f3d_516 1.38
85 3f3e 3f3e_844 0.93 3f3e_451 1.15 3f3e_457 0.79
86 2wbg 2wbg_331 0.93 2wbg_719 0.68 2wbg_734 1.53
87 2cbv 2cbv_453 2.15 2cbv_945 0.99 2cbv_872 3.09
88 2j78 2j78_311 0.37 2j78_326 0.70 2j78_458 1.14
89 2j7h 2j7h_720 0.45 2j7h_912 0.48 2j7h_48 0.17
90 2cet 2cet_313 0.85 2cet_734 0.98 2cet_756 0.77
91 3udh 3udh_523 2.02 3udh_477 1.40 3udh_72 0.43
92 3rsx 3rsx_249 0.76 3rsx_388 0.91 3rsx_515 0.79
93 4djv 4djv_314 1.09 4djv_105 1.84 4djv_335 0.61
94 2vkm 2vkm_296 1.05 2vkm_955 1.47 2vkm_707 0.95
95 4gid 4gid_197 0.77 4gid_291 0.81 4gid_201 0.38
96 4jfs 4jfs_899 0.85 4jfs_139 0.69 4jfs_362 0.66
97 4j28 4j28_110 0.90 4j28_794 0.34 4j28_196 0.87
98 2wvt 2wvt_736 0.85 2wvt_217 0.34 2wvt_922 0.61
99 2xii 2xii_763 0.88 2xii_237 0.53 2xii_146 0.53
100 4pcs 4pcs_196 0.41 4pcs_173 0.77 4pcs_812 0.56
101 3rr4 3rr4_369 0.22 3rr4_701 0.44 3rr4_529 0.89
102 1s38 1s38_305 0.47 1s38_432 0.84 1s38_485 0.99
103 1r5y 1r5y_167 0.16 1r5y_341 0.31 1r5y_113 0.44
104 3gc5 3gc5_426 0.97 3gc5_402 0.72 3gc5_464 0.92
105 3ge7 3ge7_795 0.99 3ge7_794 1.03 3ge7_895 0.91
106 4dli 4dli_96 0.44 4dli_948 1.19 4dli_524 1.15
107 2zb1 2zb1_510 0.99 2zb1_726 0.89 2zb1_875 2.11
108 4f9w 4f9w_25 0.53 4f9w_96 0.53 4f9w_86 0.49
109 3e92 3e92_106 0.32 3e92_482 0.91 3e92_17 0.59
110 3e93 3e93_390 2.73 3e93_7 0.68 3e93_51 0.34
111 4owm 4owm_362 0.56 4owm_232 0.61 4owm_135 2.35
112 3twp 3twp_510 0.46 3twp_362 2.42 3twp_608 1.94
113 3r88 3r88_628 4.18 3r88_533 0.98 3r88_632 1.17
114 4gkm 4gkm_375 0.56 4gkm_301 2.32 4gkm_111 0.46
115 3qqs 3qqs_35 0.61 3qqs_105 0.81 3qqs_511 1.32
116 3gv9 3gv9_30 0.64 3gv9_7 0.59 3gv9_59 0.59
117 3gr2 3gr2_973 1.50 3gr2_337 1.94 3gr2_872 2.26
118 4kz6 4kz6_315 0.54 4kz6_723 1.76 4kz6_308 0.28
119 4jxs 4jxs_334 1.00 4jxs_306 0.81 4jxs_147 0.77
120 2r9w 2r9w_460 3.92 2r9w_910 0.78 2r9w_927 0.68
121 2hb1 2hb1_167 0.53 2hb1_313 0.32 2hb1_981 0.79
122 1bzc 1bzc_225 1.01 1bzc_375 0.69 1bzc_265 0.70
123 2qbr 2qbr_385 0.80 2qbr_301 1.10 2qbr_282 0.91
124 2qbq 2qbq_235 0.92 2qbq_276 0.55 2qbq_752 0.38
125 2qbp 2qbp_272 0.59 2qbp_288 0.77 2qbp_259 0.84
126 1q8t 1q8t_706 1.15 1q8t_879 0.79 1q8t_60 0.92
127 1ydr 1ydr_801 1.00 1ydr_701 0.98 1ydr_80 0.79
128 1q8u 1q8u_796 0.54 1q8u_902 0.69 1q8u_901 0.63
129 1ydt 1ydt_817 1.01 1ydt_251 0.54 1ydt_283 0.93
130 3ag9 3ag9_891 2.82 3ag9_712 3.32 3ag9_850 2.93
131 3fcq 3fcq_107 1.83 3fcq_365 0.79 3fcq_135 3.09
132 1z9g 1z9g_849 0.89 1z9g_789 0.75 1z9g_790 0.64
133 1qf1 1qf1_198 0.99 1qf1_814 0.64 1qf1_397 0.95
134 5tmn 5tmn_950 0.93 5tmn_717 1.71 5tmn_996 1.96
135 4tmn 4tmn_746 2.14 4tmn_710 1.15 4tmn_209 1.03
136 4ddk 4ddk_568 3.45 4ddk_632 3.88 4ddk_496 4.33
137 4ddh 4ddh_462 3.71 4ddh_882 3.96 4ddh_427 1.25
138 3ivg 3ivg_209 0.97 3ivg_250 0.82 3ivg_252 0.80
139 3coz 3coz_7 3.03 3coz_401 3.19 3coz_561 4.61
140 3coy 3coy_258 2.90 3coy_695 8.03 3coy_291 2.65
141 3pxf 3pxf_539 1.98 3pxf_631 1.93 3pxf_775 1.61
142 4eor 4eor_701 0.53 4eor_121 0.52 4eor_850 0.62
143 2xnb 2xnb_770 1.55 2xnb_291 0.89 2xnb_751 0.63
144 1pxn 1pxn_96 0.51 1pxn_197 0.77 1pxn_86 0.83
145 2fvd 2fvd_286 1.08 2fvd_288 0.54 2fvd_345 1.41
146 4k77 4k77_328 0.37 4k77_167 0.22 4k77_257 0.54
147 4e5w 4e5w_286 0.40 4e5w_247 0.23 4e5w_121 0.15
148 4ivb 4ivb_142 0.45 4ivb_253 0.22 4ivb_875 0.84
149 4ivd 4ivd_771 0.83 4ivd_962 1.03 4ivd_823 0.37
150 4ivc 4ivc_232 0.28 4ivc_831 1.33 4ivc_841 0.90
151 4f09 4f09_903 0.51 4f09_270 0.27 4f09_893 0.30
152 4gfm 4gfm_225 0.43 4gfm_214 0.46 4gfm_913 0.44
153 4hge 4hge_797 2.55 4hge_617 4.13 4hge_471 1.72
154 4e6q 4e6q_120 0.39 4e6q_936 0.96 4e6q_132 0.96
155 4jia 4jia_60 0.65 4jia_85 0.80 4jia_66 0.82
156 2brb 2brb_302 0.71 2brb_318 0.71 2brb_383 0.62
157 2br1 2br1_354 0.83 2br1_349 1.00 2br1_392 0.83
158 3jvr 3jvr_580 2.06 3jvr_484 4.51 3jvr_471 5.37
159 3jvs 3jvs_884 0.98 3jvs_904 1.30 3jvs_897 0.84
160 1nvq 1nvq_927 0.40 1nvq_710 0.21 1nvq_871 0.85
161 3acw 3acw_411 0.85 3acw_809 0.58 3acw_281 1.61
162 4ea2 4ea2_565 4.95 4ea2_819 2.38 4ea2_798 3.64
163 2zcr 2zcr_495 1.68 2zcr_383 1.39 2zcr_324 0.85
164 2zy1 2zy1_175 1.13 2zy1_119 1.42 2zy1_172 0.95
165 2zcq 2zcq_593 8.22 2zcq_168 1.98 2zcq_471 8.00
166 1bcu 1bcu_684 0.92 1bcu_397 0.66 1bcu_852 2.22
167 3bv9 3bv9_124 1.88 3bv9_35 2.28 3bv9_325 0.88
168 1oyt 1oyt_711 0.40 1oyt_947 0.62 1oyt_59 0.51
169 2zda 2zda_264 0.76 2zda_277 0.43 2zda_299 0.46
170 3utu 3utu_237 0.90 3utu_701 1.31 3utu_374 0.45
171 3u9q 3u9q_539 2.60 3u9q_621 1.91 3u9q_416 0.88
172 2yfe 2yfe_784 1.90 2yfe_232 5.16 2yfe_240 1.03
173 3fur 3fur_960 0.93 3fur_243 0.46 3fur_138 0.91
174 3b1m 3b1m_657 7.09 3b1m_778 2.92 3b1m_215 1.33
175 2p4y 2p4y_314 2.87 2p4y_765 2.77 2p4y_472 3.48
176 3uo4 3uo4_388 0.96 3uo4_343 0.54 3uo4_329 0.43
177 3up2 3up2_231 0.82 3up2_29 0.80 3up2_4 0.67
178 3e5a 3e5a_129 0.64 3e5a_313 0.83 3e5a_398 1.00
179 2wtv 2wtv_108 0.49 2wtv_174 0.53 2wtv_320 0.40
180 3myg 3myg_965 2.04 3myg_333 0.91 3myg_925 1.93
181 3kgp 3kgp_5 1.00 3kgp_31 1.00 3kgp_20 0.99
182 1c5z 1c5z_3 0.40 1c5z_8 0.45 1c5z_452 0.77
183 1o5b 1o5b_400 0.41 1o5b_329 0.60 1o5b_396 0.56
184 1owh 1owh_3 0.66 1owh_391 0.26 1owh_307 0.53
185 1sqa 1sqa_787 0.64 1sqa_776 0.65 1sqa_366 0.83
186 4jsz 4jsz_672 2.00 4jsz_491 2.64 4jsz_815 1.73
187 3kwa 3kwa_758 2.38 3kwa_541 1.91 3kwa_852 2.92
188 2weg 2weg_838 0.90 2weg_335 1.54 2weg_515 1.33
189 3ryj 3ryj_387 0.81 3ryj_229 0.40 3ryj_395 0.74
190 3dd0 3dd0_645 0.76 3dd0_479 0.92 3dd0_570 1.32
191 2xdl 2xdl_549 5.58 2xdl_559 5.12 2xdl_495 5.33
192 3b27 3b27_307 0.24 3b27_106 0.64 3b27_366 0.38
193 1yc1 1yc1_246 0.96 1yc1_214 1.24 1yc1_38 0.59
194 3rlr 3rlr_365 0.54 3rlr_197 0.43 3rlr_397 1.07
195 2yki 2yki_893 0.94 2yki_830 0.98 2yki_841 1.13
196 1z95 1z95_313 0.57 1z95_337 0.51 1z95_303 0.38
197 3b68 3b68_304 0.87 3b68_475 4.89 3b68_291 0.90
198 3b5r 3b5r_443 4.99 3b5r_575 5.00 3b5r_557 1.73
199 3b65 3b65_533 3.17 3b65_639 3.80 3b65_542 3.38
200 3g0w 3g0w_874 1.83 3g0w_413 0.93 3g0w_196 0.33
201 4u4s 4u4s_927 0.73 4u4s_787 0.72 4u4s_71 0.37
202 1p1q 1p1q_394 0.86 1p1q_357 0.60 1p1q_121 0.71
203 1syi 1syi_234 0.38 1syi_411 0.89 1syi_259 0.63
204 1p1n 1p1n_216 1.13 1p1n_825 0.77 1p1n_333 0.64
205 2al5 2al5_436 0.76 2al5_339 0.41 2al5_323 0.23
206 3g2z 3g2z_132 2.47 3g2z_1008 1.26 3g2z_1011 0.97
207 3g31 3g31_596 1.96 3g31_781 2.03 3g31_786 2.00
208 4de2 4de2_567 2.06 4de2_108 0.72 4de2_333 0.43
209 4de3 4de3_358 0.53 4de3_322 0.54 4de3_350 0.97
210 4de1 4de1_190 0.24 4de1_366 0.56 4de1_195 0.77
211 1vso 1vso_115 0.67 1vso_198 0.58 1vso_301 0.60
212 4dld 4dld_211 0.34 4dld_410 0.80 4dld_207 0.53
213 3gbb 3gbb_616 4.30 3gbb_783 4.11 3gbb_796 4.04
214 3fv2 3fv2_177 0.59 3fv2_302 0.36 3fv2_298 0.33
215 3fv1 3fv1_436 1.51 3fv1_491 2.03 3fv1_407 1.52
216 4mgd 4mgd_24 0.43 4mgd_96 0.52 4mgd_425 0.98
217 2qe4 2qe4_629 1.55 2qe4_689 2.03 2qe4_693 1.73
218 1qkt 1qkt_766 4.54 1qkt_776 2.15 1qkt_86 0.47
219 2pog 2pog_552 1.52 2pog_853 1.20 2pog_876 0.85
220 2p15 2p15_550 3.78 2p15_331 0.27 2p15_262 0.40
221 2y5h 2y5h_284 0.82 2y5h_364 0.82 2y5h_703 0.82
222 1lpg 1lpg_244 0.75 1lpg_266 1.02 1lpg_254 0.74
223 2xbv 2xbv_317 0.69 2xbv_392 1.22 2xbv_267 0.74
224 1z6e 1z6e_160 0.96 1z6e_350 0.85 1z6e_31 0.84
225 1mq6 1mq6_416 1.99 1mq6_750 1.80 1mq6_266 2.06
226 1nc3 1nc3_524 5.73 1nc3_622 4.76 1nc3_665 5.75
227 1nc1 1nc1_6 0.51 1nc1_739 0.59 1nc1_18 0.58
228 1y6r 1y6r_809 0.67 1y6r_947 0.54 1y6r_232 0.46
229 4f2w 4f2w_142 0.57 4f2w_105 0.60 4f2w_907 0.44
230 4f3c 4f3c_744 0.73 4f3c_251 0.78 4f3c_755 0.32
231 1uto 1uto_428 0.71 1uto_834 0.67 1uto_203 1.76
232 4abg 4abg_824 0.96 4abg_149 0.93 4abg_814 1.00
233 3gy4 3gy4_184 0.34 3gy4_39 0.50 3gy4_14 0.64
234 1k1i 1k1i_896 1.65 1k1i_79 1.99 1k1i_71 1.97
235 1o3f 1o3f_372 0.49 1o3f_752 0.66 1o3f_910 0.58
236 2yge 2yge_35 0.83 2yge_198 1.01 2yge_74 0.68
237 2fxs 2fxs_927 0.79 2fxs_905 1.62 2fxs_953 0.54
238 2iwx 2iwx_394 0.36 2iwx_351 0.33 2iwx_366 0.54
239 2wer 2wer_888 0.72 2wer_73 0.27 2wer_390 0.41
240 2vw5 2vw5_227 0.60 2vw5_381 0.43 2vw5_249 0.28
241 4kzq 4kzq_14 0.45 4kzq_5 0.66 4kzq_133 0.74
242 4kzu 4kzu_256 0.53 4kzu_206 0.45 4kzu_5 0.88
243 4j21 4j21_50 0.34 4j21_430 0.88 4j21_119 1.35
244 4j3l 4j3l_761 0.60 4j3l_858 0.90 4j3l_733 0.63
245 3kr8 3kr8_884 0.56 3kr8_894 0.62 3kr8_785 0.68
246 2ymd 2ymd_484 1.53 2ymd_587 1.52 2ymd_434 4.18
247 2wnc 2wnc_514 1.65 2wnc_587 3.03 2wnc_532 1.79
248 2xys 2xys_499 1.73 2xys_443 2.02 2xys_398 0.44
249 2wn9 2wn9_11 4.21 2wn9_448 2.49 2wn9_474 3.64
250 2x00 2x00_817 1.42 2x00_705 1.23 2x00_464 1.72
251 3ozt 3ozt_202 0.52 3ozt_702 0.78 3ozt_70 0.26
252 3ozs 3ozs_255 0.69 3ozs_201 0.79 3ozs_224 1.24
253 3oe5 3oe5_236 0.42 3oe5_203 0.70 3oe5_266 0.67
254 3oe4 3oe4_203 0.47 3oe4_292 0.34 3oe4_328 0.42
255 3nw9 3nw9_210 0.81 3nw9_945 0.53 3nw9_975 1.01
256 3ao4 3ao4_165 0.61 3ao4_195 0.68 3ao4_456 0.80
257 3zt2 3zt2_5 0.39 3zt2_8 0.49 3zt2_323 0.82
258 3zsx 3zsx_293 1.23 3zsx_955 1.91 3zsx_961 1.78
259 4cig 4cig_809 1.14 4cig_784 2.57 4cig_787 1.79
260 3zso 3zso_923 2.49 3zso_799 1.59 3zso_902 1.59
261 3n7a 3n7a_552 1.06 3n7a_495 1.11 3n7a_993 1.36
262 4ciw 4ciw_482 1.12 4ciw_451 0.78 4ciw_267 0.46
263 3n86 3n86_531 1.45 3n86_870 0.87 3n86_861 0.90
264 3n76 3n76_586 2.15 3n76_758 1.26 3n76_578 1.34
265 2xb8 2xb8_35 0.74 2xb8_133 0.62 2xb8_28 0.66
266 4bkt 4bkt_17 0.99 4bkt_369 1.00 4bkt_541 1.90
267 4w9c 4w9c_719 0.92 4w9c_925 1.15 4w9c_379 0.53
268 4w9l 4w9l_368 0.71 4w9l_204 0.96 4w9l_264 0.88
269 4w9i 4w9i_204 0.42 4w9i_795 0.74 4w9i_221 0.45
270 4w9h 4w9h_368 0.40 4w9h_307 0.38 4w9h_765 0.96
271 3nq9 3nq9_700 2.59 3nq9_470 1.37 3nq9_473 1.47
272 3ueu 3ueu_52 0.87 3ueu_438 1.14 3ueu_425 1.51
273 3uev 3uev_684 6.20 3uev_655 4.50 3uev_700 1.00
274 3uew 3uew_613 1.76 3uew_624 1.57 3uew_282 1.09
275 3uex 3uex_588 6.75 3uex_640 4.72 3uex_227 0.73
276 3lka 3lka_495 1.55 3lka_443 2.05 3lka_498 4.67
277 3ehy 3ehy_685 1.05 3ehy_706 0.66 3ehy_977 0.73
278 3tsk 3tsk_713 3.96 3tsk_423 3.84 3tsk_263 3.98
279 3nx7 3nx7_36 0.73 3nx7_854 1.92 3nx7_778 0.99
280 4gr0 4gr0_326 0.91 4gr0_206 0.97 4gr0_279 0.95
281 3dxg 3dxg_465 4.27 3dxg_464 4.51 3dxg_450 3.06
282 3d6q 3d6q_23 2.26 3d6q_50 2.46 3d6q_276 2.75
283 1w4o 1w4o_367 0.69 1w4o_376 0.67 1w4o_313 0.81
284 1o0h 1o0h_603 2.49 1o0h_564 2.05 1o0h_353 0.92
285 1u1b 1u1b_919 2.04 1u1b_2 2.80 1u1b_929 1.97
Summary of the docking power: ========================================
Among the top1 binding pose ranked by the given scoring function:
Number of correct binding poses = 225, success rate = 78.9%
Among the top2 binding pose ranked by the given scoring function:
Number of correct binding poses = 249, success rate = 87.4%
Among the top3 binding pose ranked by the given scoring function:
Number of correct binding poses = 262, success rate = 91.9%
Spearman correlation coefficient in rmsd range [0-2]: 0.503
Spearman correlation coefficient in rmsd range [0-3]: 0.606
Spearman correlation coefficient in rmsd range [0-4]: 0.664
Spearman correlation coefficient in rmsd range [0-5]: 0.690
Spearman correlation coefficient in rmsd range [0-6]: 0.710
Spearman correlation coefficient in rmsd range [0-7]: 0.718
Spearman correlation coefficient in rmsd range [0-8]: 0.724
Spearman correlation coefficient in rmsd range [0-9]: 0.727
Spearman correlation coefficient in rmsd range [0-10]: 0.735
======================================================================
Template command for running the bootstrap in R program===============
rm(list=ls());
require(boot);
data_all<-read.table("DeepDock_Top1.results",header=TRUE);
data<-as.matrix(data_all[,2]);
mymean<-function(x,indices) sum(x[indices])/285;
data.boot<-boot(aa,mymean,R=10000,stype="i",sim="ordinary");
sink("DeepDock_Top1-ci.results");
a<-boot.ci(data.boot,conf=0.9,type=c("bca"));
print(a);
sink();
========================================================================
================================================
FILE: Validation_CASF2016/DockingPower_DeepDock_3A/DockingPower_DeepDock_3A.out
================================================
code Rank1 RMSD1 Rank2 RMSD2 Rank3 RMSD3
1 4llx 4llx_276 0.96 4llx_208 1.53 4llx_213 0.45
2 5c28 5c28_382 2.10 5c28_456 0.96 5c28_47 0.56
3 3uuo 3uuo_709 0.60 3uuo_187 0.77 3uuo_711 0.55
4 3ui7 3ui7_227 0.53 3ui7_282 0.50 3ui7_208 0.63
5 5c2h 5c2h_259 0.51 5c2h_240 0.46 5c2h_244 0.59
6 2v00 2v00_79 0.74 2v00_619 1.00 2v00_516 1.58
7 3wz8 3wz8_371 0.83 3wz8_380 1.12 3wz8_391 0.96
8 3pww 3pww_256 0.88 3pww_888 1.50 3pww_889 1.50
9 3prs 3prs_213 2.45 3prs_711 1.08 3prs_706 1.74
10 3uri 3uri_1001 0.91 3uri_1003 0.91 3uri_1002 0.93
11 4m0z 4m0z_686 1.31 4m0z_41 0.68 4m0z_516 0.59
12 4m0y 4m0y_920 1.74 4m0y_814 1.81 4m0y_740 2.41
13 3qgy 3qgy_318 0.90 3qgy_306 1.03 3qgy_374 1.12
14 4qd6 4qd6_825 2.55 4qd6_978 0.70 4qd6_709 2.79
15 4rfm 4rfm_9 0.37 4rfm_85 0.43 4rfm_327 0.80
16 4cr9 4cr9_423 1.85 4cr9_358 0.95 4cr9_411 1.77
17 4cra 4cra_258 0.87 4cra_283 0.60 4cra_292 1.87
18 4x6p 4x6p_312 0.67 4x6p_204 0.65 4x6p_254 0.57
19 4crc 4crc_291 0.85 4crc_215 0.79 4crc_217 0.88
20 4ty7 4ty7_224 0.81 4ty7_254 0.79 4ty7_808 0.91
21 5aba 5aba_966 2.47 5aba_712 1.21 5aba_786 1.79
22 5a7b 5a7b_178 5.86 5a7b_553 5.42 5a7b_698 4.30
23 4agn 4agn_228 1.07 4agn_251 0.99 4agn_603 1.71
24 4agp 4agp_261 1.37 4agp_350 0.97 4agp_288 2.09
25 4agq 4agq_151 1.96 4agq_529 1.95 4agq_339 0.95
26 3bgz 3bgz_97 0.23 3bgz_81 0.44 3bgz_621 1.05
27 3jya 3jya_238 0.60 3jya_315 0.83 3jya_20 1.00
28 2c3i 2c3i_650 1.80 2c3i_729 0.68 2c3i_387 0.98
29 4k18 4k18_254 0.36 4k18_225 0.31 4k18_85 0.53
30 5dwr 5dwr_382 0.46 5dwr_289 0.54 5dwr_157 0.82
31 3mss 3mss_9 2.61 3mss_451 2.58 3mss_18 1.98
32 3k5v 3k5v_386 0.64 3k5v_367 0.82 3k5v_664 1.01
33 3pyy 3pyy_296 0.31 3pyy_812 1.63 3pyy_92 0.44
34 2v7a 2v7a_859 0.95 2v7a_388 0.50 2v7a_983 1.29
35 4twp 4twp_379 0.76 4twp_352 0.95 4twp_358 0.80
36 3wtj 3wtj_544 1.19 3wtj_648 1.13 3wtj_4 6.27
37 3zdg 3zdg_896 1.66 3zdg_842 0.86 3zdg_300 1.85
38 3u8k 3u8k_412 2.67 3u8k_494 3.05 3u8k_224 0.66
39 4qac 4qac_372 0.70 4qac_115 0.90 4qac_456 1.26
40 3u8n 3u8n_928 0.84 3u8n_816 0.42 3u8n_894 1.23
41 1a30 1a30_671 2.01 1a30_130 2.09 1a30_170 2.97
42 2qnq 2qnq_207 0.98 2qnq_314 0.74 2qnq_274 1.24
43 1g2k 1g2k_247 0.64 1g2k_74 0.60 1g2k_328 0.54
44 1eby 1eby_211 0.97 1eby_295 0.87 1eby_361 0.86
45 3o9i 3o9i_227 0.79 3o9i_38 0.68 3o9i_256 0.58
46 4lzs 4lzs_1 0.75 4lzs_70 0.69 4lzs_905 0.99
47 3u5j 3u5j_65 0.41 3u5j_294 0.65 3u5j_233 0.56
48 4wiv 4wiv_486 3.84 4wiv_679 3.31 4wiv_475 4.61
49 4ogj 4ogj_999 1.36 4ogj_993 1.16 4ogj_757 0.80
50 3p5o 3p5o_108 0.92 3p5o_759 0.69 3p5o_93 0.92
51 1ps3 1ps3_943 0.44 1ps3_161 0.28 1ps3_968 0.66
52 3dx1 3dx1_989 0.74 3dx1_802 0.51 3dx1_25 0.33
53 3d4z 3d4z_340 0.47 3d4z_403 0.92 3d4z_376 0.31
54 3dx2 3dx2_836 0.99 3dx2_402 0.87 3dx2_380 0.41
55 3ejr 3ejr_360 0.41 3ejr_459 0.89 3ejr_964 0.65
56 3l7b 3l7b_163 0.69 3l7b_66 0.82 3l7b_93 0.90
57 4eky 4eky_38 0.63 4eky_899 0.67 4eky_725 0.96
58 3g2n 3g2n_197 0.65 3g2n_49 0.48 3g2n_35 0.79
59 3syr 3syr_862 0.78 3syr_408 0.85 3syr_47 0.36
60 3ebp 3ebp_476 3.98 3ebp_691 2.91 3ebp_251 2.88
61 2w66 2w66_702 0.36 2w66_809 0.81 2w66_190 0.53
62 2w4x 2w4x_306 1.13 2w4x_211 0.74 2w4x_289 1.03
63 2wca 2wca_20 2.86 2wca_201 2.03 2wca_144 1.94
64 2xj7 2xj7_812 0.40 2xj7_796 0.36 2xj7_914 0.27
65 2vvn 2vvn_802 0.44 2vvn_780 0.83 2vvn_205 0.61
66 3aru 3aru_205 1.48 3aru_314 0.79 3aru_930 1.67
67 3arv 3arv_324 0.85 3arv_345 0.70 3arv_359 0.91
68 3ary 3ary_222 6.00 3ary_561 4.87 3ary_687 5.72
69 3arq 3arq_742 2.35 3arq_422 5.21 3arq_449 5.44
70 3arp 3arp_394 2.82 3arp_37 0.97 3arp_17 1.39
71 4ih5 4ih5_227 3.85 4ih5_219 3.92 4ih5_5 4.06
72 4ih7 4ih7_219 0.54 4ih7_241 0.44 4ih7_529 2.06
73 3cj4 3cj4_166 0.50 3cj4_164 0.70 3cj4_338 0.64
74 4eo8 4eo8_12 0.39 4eo8_318 0.54 4eo8_148 0.73
75 3gnw 3gnw_294 0.71 3gnw_712 0.63 3gnw_718 0.96
76 1gpk 1gpk_407 0.79 1gpk_463 1.44 1gpk_328 0.52
77 1gpn 1gpn_299 0.35 1gpn_252 0.37 1gpn_451 1.40
78 1h23 1h23_728 1.77 1h23_913 2.33 1h23_818 2.65
79 1h22 1h22_393 2.01 1h22_852 1.55 1h22_197 1.28
80 1e66 1e66_412 1.29 1e66_404 0.97 1e66_247 0.71
81 3f3a 3f3a_203 0.20 3f3a_57 0.37 3f3a_3 0.60
82 3f3c 3f3c_401 0.77 3f3c_256 0.53 3f3c_326 0.45
83 4mme 4mme_71 0.55 4mme_133 0.65 4mme_16 0.59
84 3f3d 3f3d_287 1.55 3f3d_255 0.86 3f3d_363 0.40
85 3f3e 3f3e_174 0.83 3f3e_451 1.15 3f3e_197 0.22
86 2wbg 2wbg_331 0.93 2wbg_75 0.33 2wbg_270 0.74
87 2cbv 2cbv_945 0.99 2cbv_291 0.47 2cbv_719 0.38
88 2j78 2j78_311 0.37 2j78_31 0.74 2j78_406 0.92
89 2j7h 2j7h_721 0.75 2j7h_48 0.17 2j7h_720 0.45
90 2cet 2cet_734 0.98 2cet_359 0.88 2cet_313 0.85
91 3udh 3udh_30 0.50 3udh_347 0.32 3udh_121 0.51
92 3rsx 3rsx_249 0.76 3rsx_131 0.41 3rsx_266 0.77
93 4djv 4djv_399 0.64 4djv_349 0.40 4djv_212 0.43
94 2vkm 2vkm_237 1.46 2vkm_296 1.05 2vkm_890 0.71
95 4gid 4gid_201 0.38 4gid_197 0.77 4gid_269 0.94
96 4jfs 4jfs_818 0.92 4jfs_139 0.69 4jfs_899 0.85
97 4j28 4j28_303 1.00 4j28_154 0.98 4j28_794 0.34
98 2wvt 2wvt_415 0.95 2wvt_217 0.34 2wvt_859 0.79
99 2xii 2xii_253 1.25 2xii_293 0.41 2xii_237 0.53
100 4pcs 4pcs_165 0.90 4pcs_812 0.56 4pcs_164 0.54
101 3rr4 3rr4_369 0.22 3rr4_401 0.81 3rr4_85 0.27
102 1s38 1s38_305 0.47 1s38_75 0.30 1s38_117 0.19
103 1r5y 1r5y_167 0.16 1r5y_341 0.31 1r5y_270 0.34
104 3gc5 3gc5_402 0.72 3gc5_464 0.92 3gc5_426 0.97
105 3ge7 3ge7_140 0.61 3ge7_262 0.89 3ge7_24 0.78
106 4dli 4dli_96 0.44 4dli_42 0.41 4dli_154 0.56
107 2zb1 2zb1_206 0.78 2zb1_55 0.41 2zb1_203 0.78
108 4f9w 4f9w_96 0.53 4f9w_36 0.30 4f9w_25 0.53
109 3e92 3e92_940 0.95 3e92_907 0.39 3e92_332 0.35
110 3e93 3e93_240 0.34 3e93_141 0.46 3e93_7 0.68
111 4owm 4owm_232 0.61 4owm_230 0.75 4owm_629 0.83
112 3twp 3twp_381 2.79 3twp_608 1.94 3twp_198 2.60
113 3r88 3r88_513 1.14 3r88_802 0.93 3r88_931 2.07
114 4gkm 4gkm_348 0.68 4gkm_180 0.50 4gkm_111 0.46
115 3qqs 3qqs_100 0.48 3qqs_35 0.61 3qqs_715 0.76
116 3gv9 3gv9_30 0.64 3gv9_370 0.84 3gv9_6 0.60
117 3gr2 3gr2_337 1.94 3gr2_474 1.22 3gr2_410 1.30
118 4kz6 4kz6_143 0.71 4kz6_160 0.69 4kz6_112 0.88
119 4jxs 4jxs_218 0.97 4jxs_153 0.47 4jxs_306 0.81
120 2r9w 2r9w_710 0.95 2r9w_950 0.96 2r9w_774 1.10
121 2hb1 2hb1_148 0.81 2hb1_167 0.53 2hb1_175 0.41
122 1bzc 1bzc_375 0.69 1bzc_225 1.01 1bzc_323 0.74
123 2qbr 2qbr_385 0.80 2qbr_61 0.97 2qbr_201 0.65
124 2qbq 2qbq_235 0.92 2qbq_97 0.91 2qbq_276 0.55
125 2qbp 2qbp_259 0.84 2qbp_29 0.90 2qbp_272 0.59
126 1q8t 1q8t_879 0.79 1q8t_262 0.87 1q8t_60 0.92
127 1ydr 1ydr_63 0.92 1ydr_46 0.88 1ydr_369 0.95
128 1q8u 1q8u_796 0.54 1q8u_902 0.69 1q8u_909 0.70
129 1ydt 1ydt_386 0.73 1ydt_817 1.01 1ydt_251 0.54
130 3ag9 3ag9_934 2.78 3ag9_28 3.10 3ag9_1010 2.00
131 3fcq 3fcq_493 3.31 3fcq_135 3.09 3fcq_169 3.40
132 1z9g 1z9g_229 1.89 1z9g_219 2.15 1z9g_722 0.57
133 1qf1 1qf1_895 0.44 1qf1_198 0.99 1qf1_727 0.56
134 5tmn 5tmn_219 1.89 5tmn_717 1.71 5tmn_858 1.71
135 4tmn 4tmn_260 1.49 4tmn_278 0.92 4tmn_248 1.09
136 4ddk 4ddk_467 4.28 4ddk_445 4.35 4ddk_345 0.46
137 4ddh 4ddh_734 0.95 4ddh_1 0.45 4ddh_911 0.30
138 3ivg 3ivg_252 0.80 3ivg_209 0.97 3ivg_250 0.82
139 3coz 3coz_214 1.91 3coz_393 0.59 3coz_388 0.83
140 3coy 3coy_258 2.90 3coy_66 1.77 3coy_291 2.65
141 3pxf 3pxf_539 1.98 3pxf_767 2.01 3pxf_761 1.37
142 4eor 4eor_701 0.53 4eor_257 0.79 4eor_121 0.52
143 2xnb 2xnb_911 1.96 2xnb_291 0.89 2xnb_841 1.28
144 1pxn 1pxn_86 0.83 1pxn_96 0.51 1pxn_197 0.77
145 2fvd 2fvd_809 2.00 2fvd_275 0.92 2fvd_34 0.83
146 4k77 4k77_328 0.37 4k77_167 0.22 4k77_257 0.54
147 4e5w 4e5w_121 0.15 4e5w_779 0.86 4e5w_883 0.39
148 4ivb 4ivb_85 0.23 4ivb_142 0.45 4ivb_253 0.22
149 4ivd 4ivd_771 0.83 4ivd_231 0.77 4ivd_260 0.77
150 4ivc 4ivc_841 0.90 4ivc_740 0.76 4ivc_232 0.28
151 4f09 4f09_903 0.51 4f09_893 0.30 4f09_270 0.27
152 4gfm 4gfm_225 0.43 4gfm_214 0.46 4gfm_249 0.27
153 4hge 4hge_602 3.95 4hge_617 4.13 4hge_797 2.55
154 4e6q 4e6q_936 0.96 4e6q_120 0.39 4e6q_249 0.65
155 4jia 4jia_66 0.82 4jia_60 0.65 4jia_923 0.85
156 2brb 2brb_318 0.71 2brb_302 0.71 2brb_383 0.62
157 2br1 2br1_349 1.00 2br1_392 0.83 2br1_315 0.70
158 3jvr 3jvr_484 4.51 3jvr_470 4.09 3jvr_703 0.43
159 3jvs 3jvs_884 0.98 3jvs_897 0.84 3jvs_904 1.30
160 1nvq 1nvq_710 0.21 1nvq_927 0.40 1nvq_940 0.24
161 3acw 3acw_281 1.61 3acw_99 0.79 3acw_411 0.85
162 4ea2 4ea2_722 1.99 4ea2_961 2.54 4ea2_819 2.38
163 2zcr 2zcr_37 0.35 2zcr_187 0.76 2zcr_318 0.91
164 2zy1 2zy1_396 1.90 2zy1_113 2.40 2zy1_172 0.95
165 2zcq 2zcq_274 3.57 2zcq_254 0.84 2zcq_259 0.55
166 1bcu 1bcu_362 0.59 1bcu_397 0.66 1bcu_903 1.58
167 3bv9 3bv9_345 1.52 3bv9_325 0.88 3bv9_317 1.49
168 1oyt 1oyt_59 0.51 1oyt_64 0.53 1oyt_711 0.40
169 2zda 2zda_170 0.93 2zda_835 0.93 2zda_264 0.76
170 3utu 3utu_374 0.45 3utu_701 1.31 3utu_211 0.98
171 3u9q 3u9q_133 0.93 3u9q_252 1.27 3u9q_291 0.89
172 2yfe 2yfe_81 0.72 2yfe_53 0.73 2yfe_286 0.90
173 3fur 3fur_323 0.53 3fur_138 0.91 3fur_153 1.00
174 3b1m 3b1m_949 1.94 3b1m_221 0.81 3b1m_215 1.33
175 2p4y 2p4y_275 5.23 2p4y_25 4.44 2p4y_908 5.94
176 3uo4 3uo4_343 0.54 3uo4_388 0.96 3uo4_329 0.43
177 3up2 3up2_29 0.80 3up2_40 0.30 3up2_231 0.82
178 3e5a 3e5a_49 0.94 3e5a_398 1.00 3e5a_372 0.76
179 2wtv 2wtv_320 0.40 2wtv_108 0.49 2wtv_174 0.53
180 3myg 3myg_333 0.91 3myg_965 2.04 3myg_905 0.82
181 3kgp 3kgp_5 1.00 3kgp_420 4.70 3kgp_31 1.00
182 1c5z 1c5z_3 0.40 1c5z_8 0.45 1c5z_247 0.61
183 1o5b 1o5b_400 0.41 1o5b_421 0.75 1o5b_396 0.56
184 1owh 1owh_3 0.66 1owh_307 0.53 1owh_391 0.26
185 1sqa 1sqa_747 0.72 1sqa_761 0.95 1sqa_787 0.64
186 4jsz 4jsz_672 2.00 4jsz_571 1.66 4jsz_815 1.73
187 3kwa 3kwa_810 3.44 3kwa_823 4.35 3kwa_876 3.50
188 2weg 2weg_218 0.84 2weg_204 0.98 2weg_407 1.79
189 3ryj 3ryj_387 0.81 3ryj_395 0.74 3ryj_229 0.40
190 3dd0 3dd0_645 0.76 3dd0_479 0.92 3dd0_298 0.83
191 2xdl 2xdl_615 4.84 2xdl_192 1.74 2xdl_692 2.17
192 3b27 3b27_307 0.24 3b27_106 0.64 3b27_139 0.45
193 1yc1 1yc1_214 1.24 1yc1_205 0.51 1yc1_220 1.05
194 3rlr 3rlr_197 0.43 3rlr_365 0.54 3rlr_347 0.42
195 2yki 2yki_701 0.49 2yki_57 0.81 2yki_239 0.64
196 1z95 1z95_169 0.45 1z95_249 0.96 1z95_313 0.57
197 3b68 3b68_304 0.87 3b68_126 1.00 3b68_753 0.98
198 3b5r 3b5r_228 1.28 3b5r_443 4.99 3b5r_354 0.53
199 3b65 3b65_488 3.29 3b65_533 3.17 3b65_288 0.62
200 3g0w 3g0w_196 0.33 3g0w_791 0.43 3g0w_27 0.45
201 4u4s 4u4s_787 0.72 4u4s_430 0.90 4u4s_71 0.37
202 1p1q 1p1q_394 0.86 1p1q_121 0.71 1p1q_357 0.60
203 1syi 1syi_234 0.38 1syi_353 0.21 1syi_433 0.91
204 1p1n 1p1n_712 0.93 1p1n_73 0.61 1p1n_216 1.13
205 2al5 2al5_436 0.76 2al5_323 0.23 2al5_243 0.46
206 3g2z 3g2z_1008 1.26 3g2z_1012 0.93 3g2z_631 3.16
207 3g31 3g31_362 0.98 3g31_109 0.92 3g31_442 3.06
208 4de2 4de2_385 0.74 4de2_290 0.74 4de2_371 0.85
209 4de3 4de3_260 0.98 4de3_254 0.62 4de3_371 0.56
210 4de1 4de1_277 0.83 4de1_237 0.77 4de1_215 0.47
211 1vso 1vso_243 0.30 1vso_254 0.99 1vso_273 0.96
212 4dld 4dld_285 0.87 4dld_826 1.36 4dld_262 1.00
213 3gbb 3gbb_693 4.02 3gbb_678 5.52 3gbb_728 4.01
214 3fv2 3fv2_177 0.59 3fv2_42 0.66 3fv2_302 0.36
215 3fv1 3fv1_407 1.52 3fv1_436 1.51 3fv1_333 0.31
216 4mgd 4mgd_272 0.37 4mgd_829 0.98 4mgd_24 0.43
217 2qe4 2qe4_249 0.44 2qe4_83 0.54 2qe4_79 2.15
218 1qkt 1qkt_718 0.58 1qkt_834 0.64 1qkt_150 0.92
219 2pog 2pog_540 0.96 2pog_967 0.44 2pog_457 1.05
220 2p15 2p15_331 0.27 2p15_591 3.75 2p15_87 0.41
221 2y5h 2y5h_284 0.82 2y5h_703 0.82 2y5h_710 0.73
222 1lpg 1lpg_254 0.74 1lpg_244 0.75 1lpg_286 0.66
223 2xbv 2xbv_317 0.69 2xbv_378 0.90 2xbv_267 0.74
224 1z6e 1z6e_371 0.99 1z6e_350 0.85 1z6e_31 0.84
225 1mq6 1mq6_129 0.70 1mq6_750 1.80 1mq6_416 1.99
226 1nc3 1nc3_471 5.45 1nc3_524 5.73 1nc3_665 5.75
227 1nc1 1nc1_6 0.51 1nc1_865 0.57 1nc1_763 0.93
228 1y6r 1y6r_809 0.67 1y6r_925 0.88 1y6r_947 0.54
229 4f2w 4f2w_264 0.60 4f2w_105 0.60 4f2w_825 0.90
230 4f3c 4f3c_755 0.32 4f3c_744 0.73 4f3c_362 0.53
231 1uto 1uto_972 0.70 1uto_203 1.76 1uto_834 0.67
232 4abg 4abg_149 0.93 4abg_800 0.55 4abg_835 0.91
233 3gy4 3gy4_39 0.50 3gy4_14 0.64 3gy4_184 0.34
234 1k1i 1k1i_896 1.65 1k1i_71 1.97 1k1i_79 1.99
235 1o3f 1o3f_372 0.49 1o3f_931 0.83 1o3f_538 0.75
236 2yge 2yge_74 0.68 2yge_198 1.01 2yge_267 0.62
237 2fxs 2fxs_941 0.65 2fxs_953 0.54 2fxs_67 0.63
238 2iwx 2iwx_366 0.54 2iwx_394 0.36 2iwx_351 0.33
239 2wer 2wer_888 0.72 2wer_73 0.27 2wer_390 0.41
240 2vw5 2vw5_115 0.84 2vw5_381 0.43 2vw5_249 0.28
241 4kzq 4kzq_311 0.53 4kzq_5 0.66 4kzq_133 0.74
242 4kzu 4kzu_601 0.83 4kzu_286 0.64 4kzu_112 0.50
243 4j21 4j21_535 5.79 4j21_18 0.91 4j21_428 5.93
244 4j3l 4j3l_561 2.00 4j3l_348 1.67 4j3l_115 2.17
245 3kr8 3kr8_896 0.93 3kr8_894 0.62 3kr8_787 1.17
246 2ymd 2ymd_496 4.49 2ymd_489 2.72 2ymd_626 2.66
247 2wnc 2wnc_891 0.32 2wnc_370 0.91 2wnc_796 0.56
248 2xys 2xys_398 0.44 2xys_420 0.96 2xys_782 0.42
249 2wn9 2wn9_329 3.65 2wn9_232 0.52 2wn9_172 0.41
250 2x00 2x00_234 0.58 2x00_611 2.59 2x00_174 0.76
251 3ozt 3ozt_70 0.26 3ozt_202 0.52 3ozt_267 0.96
252 3ozs 3ozs_201 0.79 3ozs_307 0.55 3ozs_255 0.69
253 3oe5 3oe5_236 0.42 3oe5_356 0.51 3oe5_329 0.85
254 3oe4 3oe4_292 0.34 3oe4_328 0.42 3oe4_237 0.83
255 3nw9 3nw9_210 0.81 3nw9_945 0.53 3nw9_70 0.54
256 3ao4 3ao4_165 0.61 3ao4_195 0.68 3ao4_456 0.80
257 3zt2 3zt2_195 0.68 3zt2_323 0.82 3zt2_250 0.35
258 3zsx 3zsx_955 1.91 3zsx_293 1.23 3zsx_961 1.78
259 4cig 4cig_809 1.14 4cig_784 2.57 4cig_298 3.84
260 3zso 3zso_898 2.08 3zso_899 1.99 3zso_866 3.71
261 3n7a 3n7a_831 0.47 3n7a_955 0.21 3n7a_36 0.49
262 4ciw 4ciw_267 0.46 4ciw_270 0.64 4ciw_482 1.12
263 3n86 3n86_870 0.87 3n86_859 0.31 3n86_54 0.25
264 3n76 3n76_760 0.78 3n76_758 1.26 3n76_737 1.01
265 2xb8 2xb8_133 0.62 2xb8_35 0.74 2xb8_28 0.66
266 4bkt 4bkt_32 0.95 4bkt_283 0.56 4bkt_713 0.65
267 4w9c 4w9c_326 1.12 4w9c_719 0.92 4w9c_720 0.95
268 4w9l 4w9l_35 0.79 4w9l_264 0.88 4w9l_204 0.96
269 4w9i 4w9i_204 0.42 4w9i_110 0.89 4w9i_795 0.74
270 4w9h 4w9h_368 0.40 4w9h_307 0.38 4w9h_326 0.58
271 3nq9 3nq9_473 1.47 3nq9_35 1.54 3nq9_507 1.49
272 3ueu 3ueu_280 5.03 3ueu_8 1.17 3ueu_36 0.93
273 3uev 3uev_194 1.98 3uev_178 1.73 3uev_290 1.47
274 3uew 3uew_65 1.64 3uew_161 2.04 3uew_255 1.34
275 3uex 3uex_90 1.27 3uex_227 0.73 3uex_56 0.94
276 3lka 3lka_685 1.70 3lka_556 0.94 3lka_431 0.94
277 3ehy 3ehy_376 0.97 3ehy_232 0.82 3ehy_395 0.82
278 3tsk 3tsk_337 4.22 3tsk_713 3.96 3tsk_358 1.75
279 3nx7 3nx7_74 0.60 3nx7_36 0.73 3nx7_239 0.70
280 4gr0 4gr0_268 0.98 4gr0_326 0.91 4gr0_281 0.94
281 3dxg 3dxg_1002 1.70 3dxg_458 3.95 3dxg_310 3.88
282 3d6q 3d6q_312 1.87 3d6q_320 1.98 3d6q_50 2.46
283 1w4o 1w4o_246 0.91 1w4o_313 0.81 1w4o_521 1.39
284 1o0h 1o0h_272 0.77 1o0h_603 2.49 1o0h_821 0.67
285 1u1b 1u1b_26 2.85 1u1b_214 2.42 1u1b_826 2.53
Summary of the docking power: ========================================
Among the top1 binding pose ranked by the given scoring function:
Number of correct binding poses = 248, success rate = 87.0%
Among the top2 binding pose ranked by the given scoring function:
Number of correct binding poses = 262, success rate = 91.9%
Among the top3 binding pose ranked by the given scoring function:
Number of correct binding poses = 270, success rate = 94.7%
Spearman correlation coefficient in rmsd range [0-2]: 0.610
Spearman correlation coefficient in rmsd range [0-3]: 0.715
Spearman correlation coefficient in rmsd range [0-4]: 0.774
Spearman correlation coefficient in rmsd range [0-5]: 0.798
Spearman correlation coefficient in rmsd range [0-6]: 0.816
Spearman correlation coefficient in rmsd range [0-7]: 0.824
Spearman correlation coefficient in rmsd range [0-8]: 0.827
Spearman correlation coefficient in rmsd range [0-9]: 0.829
Spearman correlation coefficient in rmsd range [0-10]: 0.830
======================================================================
Template command for running the bootstrap in R program===============
rm(list=ls());
require(boot);
data_all<-read.table("DeepDock_Top1.results",header=TRUE);
data<-as.matrix(data_all[,2]);
mymean<-function(x,indices) sum(x[indices])/285;
data.boot<-boot(aa,mymean,R=10000,stype="i",sim="ordinary");
sink("DeepDock_Top1-ci.results");
a<-boot.ci(data.boot,conf=0.9,type=c("bca"));
print(a);
sink();
========================================================================
================================================
FILE: Validation_CASF2016/DockingPower_DeepDock_5A/DockingPower_DeepDock_5A.out
================================================
code Rank1 RMSD1 Rank2 RMSD2 Rank3 RMSD3
1 4llx 4llx_276 0.96 4llx_208 1.53 4llx_213 0.45
2 5c28 5c28_382 2.10 5c28_97 2.25 5c28_47 0.56
3 3uuo 3uuo_181 0.98 3uuo_709 0.60 3uuo_187 0.77
4 3ui7 3ui7_282 0.50 3ui7_16 0.63 3ui7_227 0.53
5 5c2h 5c2h_247 0.94 5c2h_240 0.46 5c2h_244 0.59
6 2v00 2v00_79 0.74 2v00_605 1.16 2v00_856 1.33
7 3wz8 3wz8_376 0.84 3wz8_371 0.83 3wz8_484 4.24
8 3pww 3pww_256 0.88 3pww_888 1.50 3pww_889 1.50
9 3prs 3prs_213 2.45 3prs_781 1.94 3prs_701 1.22
10 3uri 3uri_1000 0.94 3uri_1002 0.93 3uri_1001 0.91
11 4m0z 4m0z_686 1.31 4m0z_41 0.68 4m0z_516 0.59
12 4m0y 4m0y_920 1.74 4m0y_814 1.81 4m0y_952 1.65
13 3qgy 3qgy_306 1.03 3qgy_20 1.13 3qgy_318 0.90
14 4qd6 4qd6_978 0.70 4qd6_975 0.79 4qd6_790 0.96
15 4rfm 4rfm_85 0.43 4rfm_113 1.01 4rfm_354 0.71
16 4cr9 4cr9_423 1.85 4cr9_358 0.95 4cr9_411 1.77
17 4cra 4cra_258 0.87 4cra_283 0.60 4cra_155 0.53
18 4x6p 4x6p_312 0.67 4x6p_201 0.86 4x6p_204 0.65
19 4crc 4crc_291 0.85 4crc_217 0.88 4crc_215 0.79
20 4ty7 4ty7_808 0.91 4ty7_254 0.79 4ty7_106 0.56
21 5aba 5aba_966 2.47 5aba_786 1.79 5aba_712 1.21
22 5a7b 5a7b_302 2.91 5a7b_553 5.42 5a7b_660 4.68
23 4agn 4agn_290 1.28 4agn_251 0.99 4agn_228 1.07
24 4agp 4agp_350 0.97 4agp_261 1.37 4agp_612 2.14
25 4agq 4agq_339 0.95 4agq_205 1.39 4agq_151 1.96
26 3bgz 3bgz_621 1.05 3bgz_97 0.23 3bgz_81 0.44
27 3jya 3jya_490 0.85 3jya_471 1.04 3jya_544 0.57
28 2c3i 2c3i_799 0.90 2c3i_387 0.98 2c3i_729 0.68
29 4k18 4k18_85 0.53 4k18_151 1.76 4k18_23 0.87
30 5dwr 5dwr_289 0.54 5dwr_382 0.46 5dwr_200 0.42
31 3mss 3mss_96 2.00 3mss_475 3.21 3mss_856 1.97
32 3k5v 3k5v_330 1.91 3k5v_664 1.01 3k5v_386 0.64
33 3pyy 3pyy_822 1.59 3pyy_812 1.63 3pyy_363 0.88
34 2v7a 2v7a_859 0.95 2v7a_338 0.60 2v7a_983 1.29
35 4twp 4twp_379 0.76 4twp_358 0.80 4twp_352 0.95
36 3wtj 3wtj_4 6.27 3wtj_258 0.93 3wtj_222 1.05
37 3zdg 3zdg_765 3.66 3zdg_266 1.48 3zdg_32 0.88
38 3u8k 3u8k_448 0.91 3u8k_830 0.52 3u8k_412 2.67
39 4qac 4qac_116 0.92 4qac_115 0.90 4qac_232 0.78
40 3u8n 3u8n_928 0.84 3u8n_981 1.32 3u8n_869 2.66
41 1a30 1a30_221 4.59 1a30_140 2.70 1a30_170 2.97
42 2qnq 2qnq_207 0.98 2qnq_287 1.42 2qnq_209 0.60
43 1g2k 1g2k_74 0.60 1g2k_52 0.75 1g2k_328 0.54
44 1eby 1eby_295 0.87 1eby_262 1.01 1eby_299 0.81
45 3o9i 3o9i_227 0.79 3o9i_38 0.68 3o9i_222 0.93
46 4lzs 4lzs_70 0.69 4lzs_1 0.75 4lzs_61 0.87
47 3u5j 3u5j_65 0.41 3u5j_24 1.02 3u5j_475 1.57
48 4wiv 4wiv_486 3.84 4wiv_621 2.13 4wiv_742 3.83
49 4ogj 4ogj_999 1.36 4ogj_993 1.16 4ogj_795 3.64
50 3p5o 3p5o_818 0.83 3p5o_759 0.69 3p5o_238 0.69
51 1ps3 1ps3_734 0.86 1ps3_161 0.28 1ps3_707 0.98
52 3dx1 3dx1_25 0.33 3dx1_304 0.47 3dx1_382 0.79
53 3d4z 3d4z_376 0.31 3d4z_340 0.47 3d4z_331 1.06
54 3dx2 3dx2_820 0.37 3dx2_380 0.41 3dx2_836 0.99
55 3ejr 3ejr_964 0.65 3ejr_360 0.41 3ejr_459 0.89
56 3l7b 3l7b_38 0.42 3l7b_163 0.69 3l7b_9 0.40
57 4eky 4eky_38 0.63 4eky_899 0.67 4eky_32 0.89
58 3g2n 3g2n_49 0.48 3g2n_35 0.79 3g2n_197 0.65
59 3syr 3syr_862 0.78 3syr_477 1.35 3syr_408 0.85
60 3ebp 3ebp_476 3.98 3ebp_251 2.88 3ebp_691 2.91
61 2w66 2w66_186 0.83 2w66_179 0.91 2w66_190 0.53
62 2w4x 2w4x_306 1.13 2w4x_67 0.54 2w4x_61 0.69
63 2wca 2wca_161 3.26 2wca_20 2.86 2wca_773 5.59
64 2xj7 2xj7_812 0.40 2xj7_914 0.27 2xj7_118 0.52
65 2vvn 2vvn_205 0.61 2vvn_780 0.83 2vvn_802 0.44
66 3aru 3aru_39 4.79 3aru_715 1.97 3aru_725 1.83
67 3arv 3arv_324 0.85 3arv_386 1.27 3arv_374 1.27
68 3ary 3ary_222 6.00 3ary_561 4.87 3ary_687 5.72
69 3arq 3arq_422 5.21 3arq_445 4.86 3arq_760 2.59
70 3arp 3arp_37 0.97 3arp_394 2.82 3arp_254 3.03
71 4ih5 4ih5_227 3.85 4ih5_219 3.92 4ih5_5 4.06
72 4ih7 4ih7_12 0.69 4ih7_219 0.54 4ih7_241 0.44
73 3cj4 3cj4_166 0.50 3cj4_179 0.76 3cj4_338 0.64
74 4eo8 4eo8_12 0.39 4eo8_318 0.54 4eo8_30 0.50
75 3gnw 3gnw_712 0.63 3gnw_294 0.71 3gnw_708 0.71
76 1gpk 1gpk_407 0.79 1gpk_328 0.52 1gpk_463 1.44
77 1gpn 1gpn_299 0.35 1gpn_435 1.00 1gpn_451 1.40
78 1h23 1h23_815 1.15 1h23_728 1.77 1h23_347 0.89
79 1h22 1h22_346 0.64 1h22_187 1.33 1h22_393 2.01
80 1e66 1e66_412 1.29 1e66_404 0.97 1e66_787 0.60
81 3f3a 3f3a_649 1.33 3f3a_612 1.97 3f3a_3 0.60
82 3f3c 3f3c_466 1.55 3f3c_508 0.97 3f3c_326 0.45
83 4mme 4mme_16 0.59 4mme_71 0.55 4mme_324 0.60
84 3f3d 3f3d_287 1.55 3f3d_516 1.38 3f3d_692 0.91
85 3f3e 3f3e_844 0.93 3f3e_451 1.15 3f3e_457 0.79
86 2wbg 2wbg_331 0.93 2wbg_75 0.33 2wbg_734 1.53
87 2cbv 2cbv_945 0.99 2cbv_719 0.38 2cbv_712 0.58
88 2j78 2j78_311 0.37 2j78_326 0.70 2j78_31 0.74
89 2j7h 2j7h_48 0.17 2j7h_720 0.45 2j7h_721 0.75
90 2cet 2cet_734 0.98 2cet_313 0.85 2cet_359 0.88
91 3udh 3udh_30 0.50 3udh_347 0.32 3udh_359 0.14
92 3rsx 3rsx_131 0.41 3rsx_249 0.76 3rsx_515 0.79
93 4djv 4djv_314 1.09 4djv_335 0.61 4djv_399 0.64
94 2vkm 2vkm_296 1.05 2vkm_707 0.95 2vkm_806 0.93
95 4gid 4gid_197 0.77 4gid_201 0.38 4gid_269 0.94
96 4jfs 4jfs_818 0.92 4jfs_982 0.82 4jfs_362 0.66
97 4j28 4j28_110 0.90 4j28_183 0.93 4j28_196 0.87
98 2wvt 2wvt_266 0.93 2wvt_217 0.34 2wvt_415 0.95
99 2xii 2xii_237 0.53 2xii_185 1.01 2xii_238 0.45
100 4pcs 4pcs_165 0.90 4pcs_164 0.54 4pcs_812 0.56
101 3rr4 3rr4_401 0.81 3rr4_369 0.22 3rr4_701 0.44
102 1s38 1s38_305 0.47 1s38_75 0.30 1s38_117 0.19
103 1r5y 1r5y_167 0.16 1r5y_181 0.61 1r5y_270 0.34
104 3gc5 3gc5_402 0.72 3gc5_464 0.92 3gc5_426 0.97
105 3ge7 3ge7_795 0.99 3ge7_140 0.61 3ge7_191 0.68
106 4dli 4dli_96 0.44 4dli_948 1.19 4dli_769 0.95
107 2zb1 2zb1_510 0.99 2zb1_98 0.63 2zb1_55 0.41
108 4f9w 4f9w_96 0.53 4f9w_36 0.30 4f9w_25 0.53
109 3e92 3e92_17 0.59 3e92_907 0.39 3e92_940 0.95
110 3e93 3e93_240 0.34 3e93_141 0.46 3e93_7 0.68
111 4owm 4owm_230 0.75 4owm_232 0.61 4owm_366 1.41
112 3twp 3twp_608 1.94 3twp_381 2.79 3twp_321 2.56
113 3r88 3r88_255 0.58 3r88_291 0.76 3r88_632 1.17
114 4gkm 4gkm_189 0.94 4gkm_375 0.56 4gkm_180 0.50
115 3qqs 3qqs_100 0.48 3qqs_35 0.61 3qqs_715 0.76
116 3gv9 3gv9_30 0.64 3gv9_7 0.59 3gv9_462 0.92
117 3gr2 3gr2_701 1.32 3gr2_835 1.92 3gr2_337 1.94
118 4kz6 4kz6_365 0.66 4kz6_143 0.71 4kz6_160 0.69
119 4jxs 4jxs_334 1.00 4jxs_147 0.77 4jxs_153 0.47
120 2r9w 2r9w_950 0.96 2r9w_710 0.95 2r9w_774 1.10
121 2hb1 2hb1_148 0.81 2hb1_167 0.53 2hb1_313 0.32
122 1bzc 1bzc_375 0.69 1bzc_323 0.74 1bzc_43 0.83
123 2qbr 2qbr_778 0.87 2qbr_201 0.65 2qbr_385 0.80
124 2qbq 2qbq_235 0.92 2qbq_97 0.91 2qbq_843 0.90
125 2qbp 2qbp_272 0.59 2qbp_29 0.90 2qbp_259 0.84
126 1q8t 1q8t_879 0.79 1q8t_60 0.92 1q8t_152 0.82
127 1ydr 1ydr_801 1.00 1ydr_63 0.92 1ydr_80 0.79
128 1q8u 1q8u_796 0.54 1q8u_901 0.63 1q8u_909 0.70
129 1ydt 1ydt_207 1.02 1ydt_817 1.01 1ydt_251 0.54
130 3ag9 3ag9_934 2.78 3ag9_1008 1.62 3ag9_712 3.32
131 3fcq 3fcq_135 3.09 3fcq_110 0.87 3fcq_352 0.57
132 1z9g 1z9g_229 1.89 1z9g_849 0.89 1z9g_219 2.15
133 1qf1 1qf1_198 0.99 1qf1_814 0.64 1qf1_727 0.56
134 5tmn 5tmn_717 1.71 5tmn_725 1.92 5tmn_284 2.45
135 4tmn 4tmn_324 0.74 4tmn_710 1.15 4tmn_248 1.09
136 4ddk 4ddk_445 4.35 4ddk_467 4.28 4ddk_361 0.57
137 4ddh 4ddh_427 1.25 4ddh_911 0.30 4ddh_519 0.57
138 3ivg 3ivg_713 0.71 3ivg_250 0.82 3ivg_252 0.80
139 3coz 3coz_212 2.17 3coz_214 1.91 3coz_247 0.62
140 3coy 3coy_258 2.90 3coy_66 1.77 3coy_291 2.65
141 3pxf 3pxf_743 1.39 3pxf_761 1.37 3pxf_724 2.89
142 4eor 4eor_121 0.52 4eor_701 0.53 4eor_790 0.97
143 2xnb 2xnb_911 1.96 2xnb_291 0.89 2xnb_770 1.55
144 1pxn 1pxn_96 0.51 1pxn_86 0.83 1pxn_197 0.77
145 2fvd 2fvd_809 2.00 2fvd_34 0.83 2fvd_288 0.54
146 4k77 4k77_167 0.22 4k77_328 0.37 4k77_951 0.87
147 4e5w 4e5w_121 0.15 4e5w_779 0.86 4e5w_883 0.39
148 4ivb 4ivb_253 0.22 4ivb_142 0.45 4ivb_875 0.84
149 4ivd 4ivd_771 0.83 4ivd_886 1.57 4ivd_231 0.77
150 4ivc 4ivc_864 1.36 4ivc_232 0.28 4ivc_831 1.33
151 4f09 4f09_903 0.51 4f09_893 0.30 4f09_270 0.27
152 4gfm 4gfm_225 0.43 4gfm_913 0.44 4gfm_214 0.46
153 4hge 4hge_617 4.13 4hge_471 1.72 4hge_797 2.55
154 4e6q 4e6q_120 0.39 4e6q_936 0.96 4e6q_132 0.96
155 4jia 4jia_66 0.82 4jia_85 0.80 4jia_60 0.65
156 2brb 2brb_302 0.71 2brb_318 0.71 2brb_383 0.62
157 2br1 2br1_354 0.83 2br1_349 1.00 2br1_392 0.83
158 3jvr 3jvr_470 4.09 3jvr_471 5.37 3jvr_580 2.06
159 3jvs 3jvs_884 0.98 3jvs_897 0.84 3jvs_904 1.30
160 1nvq 1nvq_927 0.40 1nvq_710 0.21 1nvq_871 0.85
161 3acw 3acw_411 0.85 3acw_455 0.89 3acw_809 0.58
162 4ea2 4ea2_722 1.99 4ea2_819 2.38 4ea2_961 2.54
163 2zcr 2zcr_187 0.76 2zcr_351 0.85 2zcr_75 0.70
164 2zy1 2zy1_175 1.13 2zy1_172 0.95 2zy1_119 1.42
165 2zcq 2zcq_263 2.07 2zcq_168 1.98 2zcq_274 3.57
166 1bcu 1bcu_703 0.61 1bcu_397 0.66 1bcu_903 1.58
167 3bv9 3bv9_195 0.70 3bv9_325 0.88 3bv9_71 0.87
168 1oyt 1oyt_711 0.40 1oyt_59 0.51 1oyt_947 0.62
169 2zda 2zda_264 0.76 2zda_835 0.93 2zda_23 0.75
170 3utu 3utu_701 1.31 3utu_374 0.45 3utu_713 1.97
171 3u9q 3u9q_416 0.88 3u9q_291 0.89 3u9q_91 0.91
172 2yfe 2yfe_257 0.83 2yfe_81 0.72 2yfe_286 0.90
173 3fur 3fur_960 0.93 3fur_138 0.91 3fur_215 0.57
174 3b1m 3b1m_949 1.94 3b1m_215 1.33 3b1m_423 2.79
175 2p4y 2p4y_275 5.23 2p4y_25 4.44 2p4y_762 5.27
176 3uo4 3uo4_388 0.96 3uo4_343 0.54 3uo4_329 0.43
177 3up2 3up2_4 0.67 3up2_29 0.80 3up2_231 0.82
178 3e5a 3e5a_129 0.64 3e5a_398 1.00 3e5a_49 0.94
179 2wtv 2wtv_108 0.49 2wtv_174 0.53 2wtv_320 0.40
180 3myg 3myg_965 2.04 3myg_333 0.91 3myg_840 1.28
181 3kgp 3kgp_715 1.60 3kgp_5 1.00 3kgp_31 1.00
182 1c5z 1c5z_8 0.45 1c5z_3 0.40 1c5z_452 0.77
183 1o5b 1o5b_400 0.41 1o5b_396 0.56 1o5b_329 0.60
184 1owh 1owh_3 0.66 1owh_307 0.53 1owh_391 0.26
185 1sqa 1sqa_747 0.72 1sqa_357 0.62 1sqa_322 0.79
186 4jsz 4jsz_672 2.00 4jsz_506 3.36 4jsz_491 2.64
187 3kwa 3kwa_541 1.91 3kwa_130 1.76 3kwa_872 1.96
188 2weg 2weg_335 1.54 2weg_204 0.98 2weg_407 1.79
189 3ryj 3ryj_387 0.81 3ryj_229 0.40 3ryj_395 0.74
190 3dd0 3dd0_645 0.76 3dd0_479 0.92 3dd0_283 0.99
191 2xdl 2xdl_176 0.96 2xdl_692 2.17 2xdl_265 0.47
192 3b27 3b27_307 0.24 3b27_106 0.64 3b27_139 0.45
193 1yc1 1yc1_205 0.51 1yc1_246 0.96 1yc1_214 1.24
194 3rlr 3rlr_197 0.43 3rlr_365 0.54 3rlr_341 0.42
195 2yki 2yki_363 0.28 2yki_701 0.49 2yki_893 0.94
196 1z95 1z95_313 0.57 1z95_169 0.45 1z95_337 0.51
197 3b68 3b68_304 0.87 3b68_884 2.09 3b68_126 1.00
198 3b5r 3b5r_443 4.99 3b5r_557 1.73 3b5r_468 5.29
199 3b65 3b65_533 3.17 3b65_488 3.29 3b65_596 3.12
200 3g0w 3g0w_27 0.45 3g0w_874 1.83 3g0w_196 0.33
201 4u4s 4u4s_927 0.73 4u4s_787 0.72 4u4s_71 0.37
202 1p1q 1p1q_394 0.86 1p1q_357 0.60 1p1q_121 0.71
203 1syi 1syi_234 0.38 1syi_353 0.21 1syi_259 0.63
204 1p1n 1p1n_333 0.64 1p1n_216 1.13 1p1n_825 0.77
205 2al5 2al5_436 0.76 2al5_323 0.23 2al5_339 0.41
206 3g2z 3g2z_132 2.47 3g2z_166 2.93 3g2z_1005 1.01
207 3g31 3g31_596 1.96 3g31_109 0.92 3g31_534 1.91
208 4de2 4de2_290 0.74 4de2_108 0.72 4de2_214 0.73
209 4de3 4de3_358 0.53 4de3_322 0.54 4de3_375 0.40
210 4de1 4de1_190 0.24 4de1_215 0.47 4de1_277 0.83
211 1vso 1vso_358 1.65 1vso_978 1.35 1vso_115 0.67
212 4dld 4dld_826 1.36 4dld_285 0.87 4dld_262 1.00
213 3gbb 3gbb_625 3.94 3gbb_421 4.13 3gbb_489 4.02
214 3fv2 3fv2_302 0.36 3fv2_177 0.59 3fv2_172 0.12
215 3fv1 3fv1_436 1.51 3fv1_467 2.37 3fv1_452 1.82
216 4mgd 4mgd_132 0.93 4mgd_24 0.43 4mgd_96 0.52
217 2qe4 2qe4_83 0.54 2qe4_841 0.75 2qe4_309 0.49
218 1qkt 1qkt_766 4.54 1qkt_86 0.47 1qkt_718 0.58
219 2pog 2pog_457 1.05 2pog_876 0.85 2pog_734 0.87
220 2p15 2p15_496 4.30 2p15_414 3.90 2p15_591 3.75
221 2y5h 2y5h_284 0.82 2y5h_364 0.82 2y5h_703 0.82
222 1lpg 1lpg_254 0.74 1lpg_244 0.75 1lpg_253 0.78
223 2xbv 2xbv_317 0.69 2xbv_378 0.90 2xbv_392 1.22
224 1z6e 1z6e_350 0.85 1z6e_160 0.96 1z6e_31 0.84
225 1mq6 1mq6_750 1.80 1mq6_416 1.99 1mq6_129 0.70
226 1nc3 1nc3_665 5.75 1nc3_471 5.45 1nc3_551 5.66
227 1nc1 1nc1_6 0.51 1nc1_18 0.58 1nc1_739 0.59
228 1y6r 1y6r_809 0.67 1y6r_947 0.54 1y6r_232 0.46
229 4f2w 4f2w_142 0.57 4f2w_105 0.60 4f2w_907 0.44
230 4f3c 4f3c_755 0.32 4f3c_744 0.73 4f3c_871 1.05
231 1uto 1uto_834 0.67 1uto_972 0.70 1uto_428 0.71
232 4abg 4abg_800 0.55 4abg_824 0.96 4abg_149 0.93
233 3gy4 3gy4_39 0.50 3gy4_184 0.34 3gy4_14 0.64
234 1k1i 1k1i_71 1.97 1k1i_79 1.99 1k1i_896 1.65
235 1o3f 1o3f_372 0.49 1o3f_931 0.83 1o3f_910 0.58
236 2yge 2yge_74 0.68 2yge_267 0.62 2yge_966 1.08
237 2fxs 2fxs_927 0.79 2fxs_941 0.65 2fxs_827 0.87
238 2iwx 2iwx_366 0.54 2iwx_103 0.97 2iwx_351 0.33
239 2wer 2wer_888 0.72 2wer_73 0.27 2wer_390 0.41
240 2vw5 2vw5_381 0.43 2vw5_249 0.28 2vw5_227 0.60
241 4kzq 4kzq_5 0.66 4kzq_133 0.74 4kzq_14 0.45
242 4kzu 4kzu_601 0.83 4kzu_411 0.89 4kzu_206 0.45
243 4j21 4j21_50 0.34 4j21_190 0.95 4j21_119 1.35
244 4j3l 4j3l_348 1.67 4j3l_202 2.07 4j3l_761 0.60
245 3kr8 3kr8_896 0.93 3kr8_894 0.62 3kr8_884 0.56
246 2ymd 2ymd_439 3.97 2ymd_496 4.49 2ymd_492 4.07
247 2wnc 2wnc_969 1.15 2wnc_302 0.70 2wnc_860 2.01
248 2xys 2xys_782 0.42 2xys_398 0.44 2xys_127 0.46
249 2wn9 2wn9_232 0.52 2wn9_194 0.69 2wn9_445 4.06
250 2x00 2x00_767 2.59 2x00_701 0.97 2x00_705 1.23
251 3ozt 3ozt_70 0.26 3ozt_202 0.52 3ozt_267 0.96
252 3ozs 3ozs_201 0.79 3ozs_255 0.69 3ozs_307 0.55
253 3oe5 3oe5_236 0.42 3oe5_356 0.51 3oe5_30 0.57
254 3oe4 3oe4_292 0.34 3oe4_203 0.47 3oe4_328 0.42
255 3nw9 3nw9_210 0.81 3nw9_945 0.53 3nw9_70 0.54
256 3ao4 3ao4_165 0.61 3ao4_115 0.50 3ao4_195 0.68
257 3zt2 3zt2_195 0.68 3zt2_323 0.82 3zt2_173 0.32
258 3zsx 3zsx_714 1.13 3zsx_955 1.91 3zsx_961 1.78
259 4cig 4cig_809 1.14 4cig_600 1.75 4cig_787 1.79
260 3zso 3zso_902 1.59 3zso_799 1.59 3zso_910 2.05
261 3n7a 3n7a_495 1.11 3n7a_955 0.21 3n7a_428 0.71
262 4ciw 4ciw_482 1.12 4ciw_451 0.78 4ciw_267 0.46
263 3n86 3n86_870 0.87 3n86_531 1.45 3n86_861 0.90
264 3n76 3n76_758 1.26 3n76_760 0.78 3n76_407 0.89
265 2xb8 2xb8_35 0.74 2xb8_133 0.62 2xb8_28 0.66
266 4bkt 4bkt_283 0.56 4bkt_953 1.07 4bkt_713 0.65
267 4w9c 4w9c_719 0.92 4w9c_379 0.53 4w9c_326 1.12
268 4w9l 4w9l_368 0.71 4w9l_215 0.71 4w9l_264 0.88
269 4w9i 4w9i_204 0.42 4w9i_172 0.71 4w9i_110 0.89
270 4w9h 4w9h_368 0.40 4w9h_307 0.38 4w9h_326 0.58
271 3nq9 3nq9_470 1.37 3nq9_507 1.49 3nq9_605 1.35
272 3ueu 3ueu_91 0.93 3ueu_52 0.87 3ueu_37 0.86
273 3uev 3uev_178 1.73 3uev_290 1.47 3uev_957 1.54
274 3uew 3uew_65 1.64 3uew_243 2.65 3uew_161 2.04
275 3uex 3uex_227 0.73 3uex_247 0.93 3uex_276 0.64
276 3lka 3lka_685 1.70 3lka_495 1.55 3lka_161 1.56
277 3ehy 3ehy_376 0.97 3ehy_395 0.82 3ehy_858 0.83
278 3tsk 3tsk_713 3.96 3tsk_358 1.75 3tsk_344 2.04
279 3nx7 3nx7_36 0.73 3nx7_74 0.60 3nx7_46 1.11
280 4gr0 4gr0_326 0.91 4gr0_279 0.95 4gr0_206 0.97
281 3dxg 3dxg_458 3.95 3dxg_450 3.06 3dxg_1002 1.70
282 3d6q 3d6q_50 2.46 3d6q_313 2.04 3d6q_23 2.26
283 1w4o 1w4o_521 1.39 1w4o_367 0.69 1w4o_246 0.91
284 1o0h 1o0h_272 0.77 1o0h_78 0.60 1o0h_603 2.49
285 1u1b 1u1b_26 2.85 1u1b_214 2.42 1u1b_826 2.53
Summary of the docking power: ========================================
Among the top1 binding pose ranked by the given scoring function:
Number of correct binding poses = 248, success rate = 87.0%
Among the top2 binding pose ranked by the given scoring function:
Number of correct binding poses = 264, success rate = 92.6%
Among the top3 binding pose ranked by the given scoring function:
Number of correct binding poses = 268, success rate = 94.0%
Spearman correlation coefficient in rmsd range [0-2]: 0.602
Spearman correlation coefficient in rmsd range [0-3]: 0.720
Spearman correlation coefficient in rmsd range [0-4]: 0.786
Spearman correlation coefficient in rmsd range [0-5]: 0.810
Spearman correlation coefficient in rmsd range [0-6]: 0.831
Spearman correlation coefficient in rmsd range [0-7]: 0.846
Spearman correlation coefficient in rmsd range [0-8]: 0.854
Spearman correlation coefficient in rmsd range [0-9]: 0.859
Spearman correlation coefficient in rmsd range [0-10]: 0.863
======================================================================
Template command for running the bootstrap in R program===============
rm(list=ls());
require(boot);
data_all<-read.table("DeepDock_Top1.results",header=TRUE);
data<-as.matrix(data_all[,2]);
mymean<-function(x,indices) sum(x[indices])/285;
data.boot<-boot(aa,mymean,R=10000,stype="i",sim="ordinary");
sink("DeepDock_Top1-ci.results");
a<-boot.ci(data.boot,conf=0.9,type=c("bca"));
print(a);
sink();
========================================================================
================================================
FILE: Validation_CASF2016/DockingPower_DeepDock_7A/DockingPower_DeepDock_7A.out
================================================
code Rank1 RMSD1 Rank2 RMSD2 Rank3 RMSD3
1 4llx 4llx_386 0.33 4llx_302 0.27 4llx_236 0.43
2 5c28 5c28_382 2.10 5c28_97 2.25 5c28_47 0.56
3 3uuo 3uuo_709 0.60 3uuo_712 0.60 3uuo_187 0.77
4 3ui7 3ui7_333 0.45 3ui7_16 0.63 3ui7_335 0.90
5 5c2h 5c2h_244 0.59 5c2h_240 0.46 5c2h_247 0.94
6 2v00 2v00_79 0.74 2v00_605 1.16 2v00_544 1.87
7 3wz8 3wz8_376 0.84 3wz8_371 0.83 3wz8_391 0.96
8 3pww 3pww_889 1.50 3pww_394 1.13 3pww_888 1.50
9 3prs 3prs_711 1.08 3prs_701 1.22 3prs_706 1.74
10 3uri 3uri_1002 0.93 3uri_1000 0.94 3uri_1003 0.91
11 4m0z 4m0z_516 0.59 4m0z_686 1.31 4m0z_41 0.68
12 4m0y 4m0y_814 1.81 4m0y_920 1.74 4m0y_952 1.65
13 3qgy 3qgy_306 1.03 3qgy_20 1.13 3qgy_318 0.90
14 4qd6 4qd6_954 1.59 4qd6_978 0.70 4qd6_939 1.27
15 4rfm 4rfm_85 0.43 4rfm_354 0.71 4rfm_9 0.37
16 4cr9 4cr9_423 1.85 4cr9_358 0.95 4cr9_690 1.66
17 4cra 4cra_258 0.87 4cra_281 0.71 4cra_364 0.45
18 4x6p 4x6p_312 0.67 4x6p_201 0.86 4x6p_324 0.63
19 4crc 4crc_291 0.85 4crc_368 0.73 4crc_217 0.88
20 4ty7 4ty7_925 0.95 4ty7_106 0.56 4ty7_254 0.79
21 5aba 5aba_772 1.67 5aba_525 9.03 5aba_898 1.74
22 5a7b 5a7b_243 1.60 5a7b_623 1.71 5a7b_284 0.88
23 4agn 4agn_228 1.07 4agn_280 0.89 4agn_603 1.71
24 4agp 4agp_656 1.92 4agp_612 2.14 4agp_350 0.97
25 4agq 4agq_272 2.01 4agq_529 1.95 4agq_234 1.94
26 3bgz 3bgz_653 1.48 3bgz_621 1.05 3bgz_472 1.94
27 3jya 3jya_471 1.04 3jya_179 0.94 3jya_490 0.85
28 2c3i 2c3i_818 1.74 2c3i_799 0.90 2c3i_556 2.88
29 4k18 4k18_85 0.53 4k18_225 0.31 4k18_254 0.36
30 5dwr 5dwr_382 0.46 5dwr_289 0.54 5dwr_193 0.94
31 3mss 3mss_948 2.58 3mss_873 2.89 3mss_836 3.05
32 3k5v 3k5v_152 8.28 3k5v_206 8.42 3k5v_810 3.09
33 3pyy 3pyy_812 1.63 3pyy_822 1.59 3pyy_832 2.38
34 2v7a 2v7a_338 0.60 2v7a_859 0.95 2v7a_784 1.76
35 4twp 4twp_379 0.76 4twp_358 0.80 4twp_352 0.95
36 3wtj 3wtj_307 0.38 3wtj_328 0.31 3wtj_184 0.94
37 3zdg 3zdg_286 2.01 3zdg_300 1.85 3zdg_792 1.53
38 3u8k 3u8k_564 4.38 3u8k_557 5.05 3u8k_612 0.97
39 4qac 4qac_476 2.48 4qac_581 2.49 4qac_484 2.47
40 3u8n 3u8n_928 0.84 3u8n_869 2.66 3u8n_531 1.17
41 1a30 1a30_140 2.70 1a30_170 2.97 1a30_353 3.15
42 2qnq 2qnq_277 2.31 2qnq_207 0.98 2qnq_287 1.42
43 1g2k 1g2k_265 0.69 1g2k_242 1.13 1g2k_74 0.60
44 1eby 1eby_262 1.01 1eby_295 0.87 1eby_238 0.95
45 3o9i 3o9i_227 0.79 3o9i_38 0.68 3o9i_222 0.93
46 4lzs 4lzs_70 0.69 4lzs_1 0.75 4lzs_61 0.87
47 3u5j 3u5j_65 0.41 3u5j_294 0.65 3u5j_251 0.79
48 4wiv 4wiv_829 0.86 4wiv_486 3.84 4wiv_838 0.87
49 4ogj 4ogj_795 3.64 4ogj_297 4.94 4ogj_661 5.40
50 3p5o 3p5o_759 0.69 3p5o_108 0.92 3p5o_238 0.69
51 1ps3 1ps3_707 0.98 1ps3_734 0.86 1ps3_161 0.28
52 3dx1 3dx1_382 0.79 3dx1_25 0.33 3dx1_304 0.47
53 3d4z 3d4z_340 0.47 3d4z_376 0.31 3d4z_132 0.34
54 3dx2 3dx2_990 0.58 3dx2_820 0.37 3dx2_380 0.41
55 3ejr 3ejr_87 1.26 3ejr_964 0.65 3ejr_360 0.41
56 3l7b 3l7b_38 0.42 3l7b_9 0.40 3l7b_47 0.77
57 4eky 4eky_899 0.67 4eky_38 0.63 4eky_32 0.89
58 3g2n 3g2n_35 0.79 3g2n_49 0.48 3g2n_55 0.78
59 3syr 3syr_862 0.78 3syr_477 1.35 3syr_408 0.85
60 3ebp 3ebp_476 3.98 3ebp_691 2.91 3ebp_251 2.88
61 2w66 2w66_534 3.26 2w66_453 2.28 2w66_773 3.27
62 2w4x 2w4x_407 0.70 2w4x_417 0.86 2w4x_67 0.54
63 2wca 2wca_773 5.59 2wca_731 1.88 2wca_951 3.72
64 2xj7 2xj7_812 0.40 2xj7_871 1.64 2xj7_914 0.27
65 2vvn 2vvn_406 1.13 2vvn_401 1.83 2vvn_205 0.61
66 3aru 3aru_573 2.65 3aru_311 0.67 3aru_450 2.24
67 3arv 3arv_345 0.70 3arv_515 3.27 3arv_324 0.85
68 3ary 3ary_858 6.03 3ary_762 1.40 3ary_844 1.23
69 3arq 3arq_639 9.10 3arq_575 3.46 3arq_515 6.04
70 3arp 3arp_17 1.39 3arp_67 0.98 3arp_284 1.83
71 4ih5 4ih5_867 1.96 4ih5_192 3.09 4ih5_159 2.57
72 4ih7 4ih7_461 2.23 4ih7_12 0.69 4ih7_438 1.86
73 3cj4 3cj4_166 0.50 3cj4_338 0.64 3cj4_179 0.76
74 4eo8 4eo8_30 0.50 4eo8_12 0.39 4eo8_318 0.54
75 3gnw 3gnw_729 2.21 3gnw_722 1.53 3gnw_155 5.15
76 1gpk 1gpk_159 0.76 1gpk_415 1.62 1gpk_328 0.52
77 1gpn 1gpn_600 1.09 1gpn_781 4.18 1gpn_658 2.56
78 1h23 1h23_815 1.15 1h23_347 0.89 1h23_335 1.86
79 1h22 1h22_187 1.33 1h22_346 0.64 1h22_384 1.95
80 1e66 1e66_661 2.74 1e66_712 1.76 1e66_404 0.97
81 3f3a 3f3a_612 1.97 3f3a_65 0.32 3f3a_552 2.20
82 3f3c 3f3c_508 0.97 3f3c_686 2.83 3f3c_466 1.55
83 4mme 4mme_16 0.59 4mme_71 0.55 4mme_324 0.60
84 3f3d 3f3d_287 1.55 3f3d_666 2.02 3f3d_516 1.38
85 3f3e 3f3e_844 0.93 3f3e_451 1.15 3f3e_457 0.79
86 2wbg 2wbg_331 0.93 2wbg_719 0.68 2wbg_734 1.53
87 2cbv 2cbv_945 0.99 2cbv_453 2.15 2cbv_872 3.09
88 2j78 2j78_311 0.37 2j78_458 1.14 2j78_326 0.70
89 2j7h 2j7h_720 0.45 2j7h_912 0.48 2j7h_48 0.17
90 2cet 2cet_313 0.85 2cet_734 0.98 2cet_756 0.77
91 3udh 3udh_477 1.40 3udh_523 2.02 3udh_72 0.43
92 3rsx 3rsx_249 0.76 3rsx_388 0.91 3rsx_131 0.41
93 4djv 4djv_105 1.84 4djv_314 1.09 4djv_335 0.61
94 2vkm 2vkm_296 1.05 2vkm_955 1.47 2vkm_707 0.95
95 4gid 4gid_197 0.77 4gid_291 0.81 4gid_201 0.38
96 4jfs 4jfs_899 0.85 4jfs_362 0.66 4jfs_139 0.69
97 4j28 4j28_110 0.90 4j28_794 0.34 4j28_196 0.87
98 2wvt 2wvt_736 0.85 2wvt_217 0.34 2wvt_922 0.61
99 2xii 2xii_763 0.88 2xii_237 0.53 2xii_146 0.53
100 4pcs 4pcs_196 0.41 4pcs_173 0.77 4pcs_812 0.56
101 3rr4 3rr4_369 0.22 3rr4_701 0.44 3rr4_529 0.89
102 1s38 1s38_305 0.47 1s38_432 0.84 1s38_485 0.99
103 1r5y 1r5y_167 0.16 1r5y_341 0.31 1r5y_90 0.48
104 3gc5 3gc5_402 0.72 3gc5_426 0.97 3gc5_464 0.92
105 3ge7 3ge7_794 1.03 3ge7_795 0.99 3ge7_895 0.91
106 4dli 4dli_96 0.44 4dli_948 1.19 4dli_524 1.15
107 2zb1 2zb1_510 0.99 2zb1_726 0.89 2zb1_329 0.43
108 4f9w 4f9w_25 0.53 4f9w_96 0.53 4f9w_86 0.49
109 3e92 3e92_106 0.32 3e92_907 0.39 3e92_482 0.91
110 3e93 3e93_7 0.68 3e93_51 0.34 3e93_390 2.73
111 4owm 4owm_362 0.56 4owm_232 0.61 4owm_230 0.75
112 3twp 3twp_510 0.46 3twp_608 1.94 3twp_613 0.89
113 3r88 3r88_628 4.18 3r88_533 0.98 3r88_632 1.17
114 4gkm 4gkm_375 0.56 4gkm_111 0.46 4gkm_348 0.68
115 3qqs 3qqs_35 0.61 3qqs_105 0.81 3qqs_511 1.32
116 3gv9 3gv9_30 0.64 3gv9_7 0.59 3gv9_59 0.59
117 3gr2 3gr2_337 1.94 3gr2_973 1.50 3gr2_872 2.26
118 4kz6 4kz6_723 1.76 4kz6_315 0.54 4kz6_389 0.56
119 4jxs 4jxs_334 1.00 4jxs_306 0.81 4jxs_147 0.77
120 2r9w 2r9w_910 0.78 2r9w_911 0.66 2r9w_741 1.36
121 2hb1 2hb1_167 0.53 2hb1_148 0.81 2hb1_981 0.79
122 1bzc 1bzc_225 1.01 1bzc_265 0.70 1bzc_375 0.69
123 2qbr 2qbr_385 0.80 2qbr_778 0.87 2qbr_301 1.10
124 2qbq 2qbq_235 0.92 2qbq_752 0.38 2qbq_276 0.55
125 2qbp 2qbp_272 0.59 2qbp_288 0.77 2qbp_259 0.84
126 1q8t 1q8t_706 1.15 1q8t_879 0.79 1q8t_60 0.92
127 1ydr 1ydr_801 1.00 1ydr_701 0.98 1ydr_80 0.79
128 1q8u 1q8u_796 0.54 1q8u_901 0.63 1q8u_902 0.69
129 1ydt 1ydt_817 1.01 1ydt_251 0.54 1ydt_283 0.93
130 3ag9 3ag9_891 2.82 3ag9_712 3.32 3ag9_934 2.78
131 3fcq 3fcq_107 1.83 3fcq_135 3.09 3fcq_365 0.79
132 1z9g 1z9g_849 0.89 1z9g_789 0.75 1z9g_790 0.64
133 1qf1 1qf1_198 0.99 1qf1_814 0.64 1qf1_397 0.95
134 5tmn 5tmn_950 0.93 5tmn_717 1.71 5tmn_996 1.96
135 4tmn 4tmn_746 2.14 4tmn_710 1.15 4tmn_209 1.03
136 4ddk 4ddk_568 3.45 4ddk_445 4.35 4ddk_496 4.33
137 4ddh 4ddh_462 3.71 4ddh_427 1.25 4ddh_882 3.96
138 3ivg 3ivg_209 0.97 3ivg_713 0.71 3ivg_250 0.82
139 3coz 3coz_7 3.03 3coz_401 3.19 3coz_263 0.83
140 3coy 3coy_258 2.90 3coy_291 2.65 3coy_695 8.03
141 3pxf 3pxf_539 1.98 3pxf_631 1.93 3pxf_743 1.39
142 4eor 4eor_701 0.53 4eor_121 0.52 4eor_850 0.62
143 2xnb 2xnb_770 1.55 2xnb_291 0.89 2xnb_751 0.63
144 1pxn 1pxn_96 0.51 1pxn_197 0.77 1pxn_86 0.83
145 2fvd 2fvd_286 1.08 2fvd_288 0.54 2fvd_345 1.41
146 4k77 4k77_328 0.37 4k77_167 0.22 4k77_257 0.54
147 4e5w 4e5w_286 0.40 4e5w_121 0.15 4e5w_247 0.23
148 4ivb 4ivb_875 0.84 4ivb_253 0.22 4ivb_142 0.45
149 4ivd 4ivd_771 0.83 4ivd_823 0.37 4ivd_962 1.03
150 4ivc 4ivc_232 0.28 4ivc_831 1.33 4ivc_841 0.90
151 4f09 4f09_903 0.51 4f09_270 0.27 4f09_893 0.30
152 4gfm 4gfm_225 0.43 4gfm_214 0.46 4gfm_913 0.44
153 4hge 4hge_797 2.55 4hge_617 4.13 4hge_471 1.72
154 4e6q 4e6q_120 0.39 4e6q_936 0.96 4e6q_132 0.96
155 4jia 4jia_60 0.65 4jia_85 0.80 4jia_66 0.82
156 2brb 2brb_302 0.71 2brb_318 0.71 2brb_383 0.62
157 2br1 2br1_354 0.83 2br1_349 1.00 2br1_315 0.70
158 3jvr 3jvr_580 2.06 3jvr_484 4.51 3jvr_471 5.37
159 3jvs 3jvs_884 0.98 3jvs_904 1.30 3jvs_897 0.84
160 1nvq 1nvq_927 0.40 1nvq_710 0.21 1nvq_871 0.85
161 3acw 3acw_281 1.61 3acw_809 0.58 3acw_411 0.85
162 4ea2 4ea2_819 2.38 4ea2_722 1.99 4ea2_565 4.95
163 2zcr 2zcr_495 1.68 2zcr_383 1.39 2zcr_187 0.76
164 2zy1 2zy1_175 1.13 2zy1_119 1.42 2zy1_172 0.95
165 2zcq 2zcq_593 8.22 2zcq_168 1.98 2zcq_471 8.00
166 1bcu 1bcu_684 0.92 1bcu_397 0.66 1bcu_852 2.22
167 3bv9 3bv9_124 1.88 3bv9_325 0.88 3bv9_35 2.28
168 1oyt 1oyt_711 0.40 1oyt_59 0.51 1oyt_947 0.62
169 2zda 2zda_264 0.76 2zda_277 0.43 2zda_299 0.46
170 3utu 3utu_701 1.31 3utu_374 0.45 3utu_237 0.90
171 3u9q 3u9q_621 1.91 3u9q_539 2.60 3u9q_416 0.88
172 2yfe 2yfe_784 1.90 2yfe_286 0.90 2yfe_257 0.83
173 3fur 3fur_960 0.93 3fur_243 0.46 3fur_234 1.11
174 3b1m 3b1m_778 2.92 3b1m_657 7.09 3b1m_215 1.33
175 2p4y 2p4y_314 2.87 2p4y_765 2.77 2p4y_472 3.48
176 3uo4 3uo4_388 0.96 3uo4_343 0.54 3uo4_329 0.43
177 3up2 3up2_231 0.82 3up2_29 0.80 3up2_4 0.67
178 3e5a 3e5a_129 0.64 3e5a_313 0.83 3e5a_398 1.00
179 2wtv 2wtv_108 0.49 2wtv_174 0.53 2wtv_320 0.40
180 3myg 3myg_965 2.04 3myg_333 0.91 3myg_925 1.93
181 3kgp 3kgp_31 1.00 3kgp_5 1.00 3kgp_20 0.99
182 1c5z 1c5z_3 0.40 1c5z_8 0.45 1c5z_452 0.77
183 1o5b 1o5b_400 0.41 1o5b_329 0.60 1o5b_396 0.56
184 1owh 1owh_3 0.66 1owh_391 0.26 1owh_307 0.53
185 1sqa 1sqa_776 0.65 1sqa_787 0.64 1sqa_747 0.72
186 4jsz 4jsz_672 2.00 4jsz_491 2.64 4jsz_815 1.73
187 3kwa 3kwa_758 2.38 3kwa_541 1.91 3kwa_130 1.76
188 2weg 2weg_838 0.90 2weg_335 1.54 2weg_515 1.33
189 3ryj 3ryj_387 0.81 3ryj_229 0.40 3ryj_395 0.74
190 3dd0 3dd0_645 0.76 3dd0_479 0.92 3dd0_570 1.32
191 2xdl 2xdl_549 5.58 2xdl_559 5.12 2xdl_495 5.33
192 3b27 3b27_307 0.24 3b27_106 0.64 3b27_366 0.38
193 1yc1 1yc1_246 0.96 1yc1_38 0.59 1yc1_214 1.24
194 3rlr 3rlr_365 0.54 3rlr_197 0.43 3rlr_192 0.43
195 2yki 2yki_893 0.94 2yki_841 1.13 2yki_830 0.98
196 1z95 1z95_313 0.57 1z95_337 0.51 1z95_303 0.38
197 3b68 3b68_304 0.87 3b68_475 4.89 3b68_291 0.90
198 3b5r 3b5r_443 4.99 3b5r_575 5.00 3b5r_557 1.73
199 3b65 3b65_533 3.17 3b65_639 3.80 3b65_542 3.38
200 3g0w 3g0w_413 0.93 3g0w_874 1.83 3g0w_196 0.33
201 4u4s 4u4s_927 0.73 4u4s_787 0.72 4u4s_71 0.37
202 1p1q 1p1q_394 0.86 1p1q_357 0.60 1p1q_121 0.71
203 1syi 1syi_234 0.38 1syi_411 0.89 1syi_259 0.63
204 1p1n 1p1n_216 1.13 1p1n_825 0.77 1p1n_333 0.64
205 2al5 2al5_436 0.76 2al5_339 0.41 2al5_323 0.23
206 3g2z 3g2z_132 2.47 3g2z_1008 1.26 3g2z_1005 1.01
207 3g31 3g31_596 1.96 3g31_534 1.91 3g31_109 0.92
208 4de2 4de2_567 2.06 4de2_108 0.72 4de2_333 0.43
209 4de3 4de3_358 0.53 4de3_322 0.54 4de3_375 0.40
210 4de1 4de1_190 0.24 4de1_366 0.56 4de1_195 0.77
211 1vso 1vso_115 0.67 1vso_198 0.58 1vso_301 0.60
212 4dld 4dld_211 0.34 4dld_207 0.53 4dld_410 0.80
213 3gbb 3gbb_616 4.30 3gbb_796 4.04 3gbb_503 1.27
214 3fv2 3fv2_177 0.59 3fv2_302 0.36 3fv2_298 0.33
215 3fv1 3fv1_436 1.51 3fv1_491 2.03 3fv1_407 1.52
216 4mgd 4mgd_24 0.43 4mgd_96 0.52 4mgd_425 0.98
217 2qe4 2qe4_629 1.55 2qe4_689 2.03 2qe4_693 1.73
218 1qkt 1qkt_766 4.54 1qkt_776 2.15 1qkt_86 0.47
219 2pog 2pog_552 1.52 2pog_876 0.85 2pog_853 1.20
220 2p15 2p15_550 3.78 2p15_331 0.27 2p15_262 0.40
221 2y5h 2y5h_284 0.82 2y5h_364 0.82 2y5h_703 0.82
222 1lpg 1lpg_244 0.75 1lpg_266 1.02 1lpg_254 0.74
223 2xbv 2xbv_317 0.69 2xbv_392 1.22 2xbv_267 0.74
224 1z6e 1z6e_160 0.96 1z6e_350 0.85 1z6e_31 0.84
225 1mq6 1mq6_750 1.80 1mq6_416 1.99 1mq6_129 0.70
226 1nc3 1nc3_524 5.73 1nc3_665 5.75 1nc3_669 4.51
227 1nc1 1nc1_6 0.51 1nc1_739 0.59 1nc1_18 0.58
228 1y6r 1y6r_809 0.67 1y6r_947 0.54 1y6r_232 0.46
229 4f2w 4f2w_142 0.57 4f2w_105 0.60 4f2w_907 0.44
230 4f3c 4f3c_744 0.73 4f3c_251 0.78 4f3c_755 0.32
231 1uto 1uto_428 0.71 1uto_834 0.67 1uto_972 0.70
232 4abg 4abg_824 0.96 4abg_149 0.93 4abg_800 0.55
233 3gy4 3gy4_39 0.50 3gy4_184 0.34 3gy4_14 0.64
234 1k1i 1k1i_79 1.99 1k1i_896 1.65 1k1i_71 1.97
235 1o3f 1o3f_372 0.49 1o3f_752 0.66 1o3f_910 0.58
236 2yge 2yge_35 0.83 2yge_74 0.68 2yge_437 0.89
237 2fxs 2fxs_905 1.62 2fxs_927 0.79 2fxs_953 0.54
238 2iwx 2iwx_351 0.33 2iwx_394 0.36 2iwx_366 0.54
239 2wer 2wer_888 0.72 2wer_73 0.27 2wer_390 0.41
240 2vw5 2vw5_381 0.43 2vw5_227 0.60 2vw5_249 0.28
241 4kzq 4kzq_5 0.66 4kzq_14 0.45 4kzq_133 0.74
242 4kzu 4kzu_256 0.53 4kzu_206 0.45 4kzu_5 0.88
243 4j21 4j21_50 0.34 4j21_430 0.88 4j21_119 1.35
244 4j3l 4j3l_761 0.60 4j3l_858 0.90 4j3l_733 0.63
245 3kr8 3kr8_884 0.56 3kr8_894 0.62 3kr8_785 0.68
246 2ymd 2ymd_434 4.18 2ymd_484 1.53 2ymd_587 1.52
247 2wnc 2wnc_514 1.65 2wnc_532 1.79 2wnc_587 3.03
248 2xys 2xys_499 1.73 2xys_398 0.44 2xys_443 2.02
249 2wn9 2wn9_11 4.21 2wn9_448 2.49 2wn9_329 3.65
250 2x00 2x00_817 1.42 2x00_464 1.72 2x00_705 1.23
251 3ozt 3ozt_202 0.52 3ozt_70 0.26 3ozt_702 0.78
252 3ozs 3ozs_255 0.69 3ozs_201 0.79 3ozs_224 1.24
253 3oe5 3oe5_236 0.42 3oe5_266 0.67 3oe5_203 0.70
254 3oe4 3oe4_203 0.47 3oe4_292 0.34 3oe4_328 0.42
255 3nw9 3nw9_210 0.81 3nw9_945 0.53 3nw9_975 1.01
256 3ao4 3ao4_165 0.61 3ao4_195 0.68 3ao4_456 0.80
257 3zt2 3zt2_5 0.39 3zt2_8 0.49 3zt2_541 0.96
258 3zsx 3zsx_293 1.23 3zsx_955 1.91 3zsx_961 1.78
259 4cig 4cig_809 1.14 4cig_784 2.57 4cig_787 1.79
260 3zso 3zso_923 2.49 3zso_799 1.59 3zso_902 1.59
261 3n7a 3n7a_552 1.06 3n7a_495 1.11 3n7a_831 0.47
262 4ciw 4ciw_482 1.12 4ciw_451 0.78 4ciw_267 0.46
263 3n86 3n86_531 1.45 3n86_870 0.87 3n86_861 0.90
264 3n76 3n76_586 2.15 3n76_758 1.26 3n76_578 1.34
265 2xb8 2xb8_35 0.74 2xb8_133 0.62 2xb8_28 0.66
266 4bkt 4bkt_369 1.00 4bkt_17 0.99 4bkt_541 1.90
267 4w9c 4w9c_925 1.15 4w9c_719 0.92 4w9c_379 0.53
268 4w9l 4w9l_368 0.71 4w9l_204 0.96 4w9l_264 0.88
269 4w9i 4w9i_204 0.42 4w9i_795 0.74 4w9i_221 0.45
270 4w9h 4w9h_368 0.40 4w9h_307 0.38 4w9h_765 0.96
271 3nq9 3nq9_700 2.59 3nq9_470 1.37 3nq9_473 1.47
272 3ueu 3ueu_52 0.87 3ueu_438 1.14 3ueu_425 1.51
273 3uev 3uev_684 6.20 3uev_655 4.50 3uev_700 1.00
274 3uew 3uew_613 1.76 3uew_624 1.57 3uew_282 1.09
275 3uex 3uex_588 6.75 3uex_227 0.73 3uex_640 4.72
276 3lka 3lka_495 1.55 3lka_443 2.05 3lka_161 1.56
277 3ehy 3ehy_141 0.99 3ehy_706 0.66 3ehy_685 1.05
278 3tsk 3tsk_713 3.96 3tsk_263 3.98 3tsk_423 3.84
279 3nx7 3nx7_36 0.73 3nx7_854 1.92 3nx7_778 0.99
280 4gr0 4gr0_326 0.91 4gr0_279 0.95 4gr0_206 0.97
281 3dxg 3dxg_464 4.51 3dxg_450 3.06 3dxg_465 4.27
282 3d6q 3d6q_50 2.46 3d6q_23 2.26 3d6q_276 2.75
283 1w4o 1w4o_367 0.69 1w4o_376 0.67 1w4o_313 0.81
284 1o0h 1o0h_603 2.49 1o0h_272 0.77 1o0h_78 0.60
285 1u1b 1u1b_919 2.04 1u1b_929 1.97 1u1b_2 2.80
Summary of the docking power: ========================================
Among the top1 binding pose ranked by the given scoring function:
Number of correct binding poses = 230, success rate = 80.7%
Among the top2 binding pose ranked by the given scoring function:
Number of correct binding poses = 256, success rate = 89.8%
Among the top3 binding pose ranked by the given scoring function:
Number of correct binding poses = 265, success rate = 93.0%
Spearman correlation coefficient in rmsd range [0-2]: 0.519
Spearman correlation coefficient in rmsd range [0-3]: 0.624
Spearman correlation coefficient in rmsd range [0-4]: 0.683
Spearman correlation coefficient in rmsd range [0-5]: 0.711
Spearman correlation coefficient in rmsd range [0-6]: 0.730
Spearman correlation coefficient in rmsd range [0-7]: 0.739
Spearman correlation coefficient in rmsd range [0-8]: 0.745
Spearman correlation coefficient in rmsd range [0-9]: 0.749
Spearman correlation coefficient in rmsd range [0-10]: 0.756
======================================================================
Template command for running the bootstrap in R program===============
rm(list=ls());
require(boot);
data_all<-read.table("DeepDock_Top1.results",header=TRUE);
data<-as.matrix(data_all[,2]);
mymean<-function(x,indices) sum(x[indices])/285;
data.boot<-boot(aa,mymean,R=10000,stype="i",sim="ordinary");
sink("DeepDock_Top1-ci.results");
a<-boot.ci(data.boot,conf=0.9,type=c("bca"));
print(a);
sink();
========================================================================
================================================
FILE: Validation_CASF2016/DockingPower_DeepDock_all/DockingPower_DeepDock_all.out
================================================
code Rank1 RMSD1 Rank2 RMSD2 Rank3 RMSD3
1 4llx 4llx_386 0.33 4llx_302 0.27 4llx_519 1.25
2 5c28 5c28_47 0.56 5c28_531 2.52 5c28_382 2.10
3 3uuo 3uuo_709 0.60 3uuo_712 0.60 3uuo_711 0.55
4 3ui7 3ui7_333 0.45 3ui7_16 0.63 3ui7_335 0.90
5 5c2h 5c2h_244 0.59 5c2h_247 0.94 5c2h_240 0.46
6 2v00 2v00_79 0.74 2v00_544 1.87 2v00_605 1.16
7 3wz8 3wz8_376 0.84 3wz8_371 0.83 3wz8_391 0.96
8 3pww 3pww_889 1.50 3pww_394 1.13 3pww_816 1.65
9 3prs 3prs_711 1.08 3prs_701 1.22 3prs_706 1.74
10 3uri 3uri_1000 0.94 3uri_1002 0.93 3uri_1001 0.91
11 4m0z 4m0z_516 0.59 4m0z_686 1.31 4m0z_41 0.68
12 4m0y 4m0y_814 1.81 4m0y_920 1.74 4m0y_952 1.65
13 3qgy 3qgy_306 1.03 3qgy_20 1.13 3qgy_318 0.90
14 4qd6 4qd6_954 1.59 4qd6_978 0.70 4qd6_939 1.27
15 4rfm 4rfm_85 0.43 4rfm_354 0.71 4rfm_9 0.37
16 4cr9 4cr9_423 1.85 4cr9_358 0.95 4cr9_690 1.66
17 4cra 4cra_258 0.87 4cra_318 0.47 4cra_364 0.45
18 4x6p 4x6p_312 0.67 4x6p_201 0.86 4x6p_254 0.57
19 4crc 4crc_291 0.85 4crc_217 0.88 4crc_368 0.73
20 4ty7 4ty7_106 0.56 4ty7_925 0.95 4ty7_62 1.05
21 5aba 5aba_772 1.67 5aba_525 9.03 5aba_657 2.90
22 5a7b 5a7b_623 1.71 5a7b_243 1.60 5a7b_286 0.74
23 4agn 4agn_228 1.07 4agn_603 1.71 4agn_280 0.89
24 4agp 4agp_656 1.92 4agp_612 2.14 4agp_350 0.97
25 4agq 4agq_529 1.95 4agq_234 1.94 4agq_272 2.01
26 3bgz 3bgz_653 1.48 3bgz_621 1.05 3bgz_472 1.94
27 3jya 3jya_471 1.04 3jya_413 1.38 3jya_490 0.85
28 2c3i 2c3i_818 1.74 2c3i_556 2.88 2c3i_823 0.88
29 4k18 4k18_225 0.31 4k18_85 0.53 4k18_254 0.36
30 5dwr 5dwr_382 0.46 5dwr_289 0.54 5dwr_193 0.94
31 3mss 3mss_948 2.58 3mss_836 3.05 3mss_873 2.89
32 3k5v 3k5v_152 8.28 3k5v_626 6.68 3k5v_810 3.09
33 3pyy 3pyy_812 1.63 3pyy_822 1.59 3pyy_832 2.38
34 2v7a 2v7a_338 0.60 2v7a_859 0.95 2v7a_784 1.76
35 4twp 4twp_379 0.76 4twp_358 0.80 4twp_352 0.95
36 3wtj 3wtj_328 0.31 3wtj_307 0.38 3wtj_222 1.05
37 3zdg 3zdg_286 2.01 3zdg_758 2.33 3zdg_792 1.53
38 3u8k 3u8k_564 4.38 3u8k_557 5.05 3u8k_584 4.59
39 4qac 4qac_476 2.48 4qac_581 2.49 4qac_945 0.87
40 3u8n 3u8n_928 0.84 3u8n_531 1.17 3u8n_869 2.66
41 1a30 1a30_353 3.15 1a30_140 2.70 1a30_170 2.97
42 2qnq 2qnq_277 2.31 2qnq_207 0.98 2qnq_287 1.42
43 1g2k 1g2k_265 0.69 1g2k_242 1.13 1g2k_74 0.60
44 1eby 1eby_262 1.01 1eby_295 0.87 1eby_238 0.95
45 3o9i 3o9i_227 0.79 3o9i_38 0.68 3o9i_222 0.93
46 4lzs 4lzs_70 0.69 4lzs_1 0.75 4lzs_61 0.87
47 3u5j 3u5j_65 0.41 3u5j_54 0.63 3u5j_251 0.79
48 4wiv 4wiv_829 0.86 4wiv_486 3.84 4wiv_838 0.87
49 4ogj 4ogj_297 4.94 4ogj_795 3.64 4ogj_661 5.40
50 3p5o 3p5o_759 0.69 3p5o_818 0.83 3p5o_108 0.92
51 1ps3 1ps3_707 0.98 1ps3_734 0.86 1ps3_161 0.28
52 3dx1 3dx1_382 0.79 3dx1_457 2.00 3dx1_25 0.33
53 3d4z 3d4z_340 0.47 3d4z_376 0.31 3d4z_132 0.34
54 3dx2 3dx2_990 0.58 3dx2_820 0.37 3dx2_380 0.41
55 3ejr 3ejr_87 1.26 3ejr_360 0.41 3ejr_964 0.65
56 3l7b 3l7b_38 0.42 3l7b_9 0.40 3l7b_47 0.77
57 4eky 4eky_899 0.67 4eky_38 0.63 4eky_32 0.89
58 3g2n 3g2n_35 0.79 3g2n_49 0.48 3g2n_55 0.78
59 3syr 3syr_862 0.78 3syr_477 1.35 3syr_408 0.85
60 3ebp 3ebp_476 3.98 3ebp_691 2.91 3ebp_597 3.81
61 2w66 2w66_534 3.26 2w66_453 2.28 2w66_773 3.27
62 2w4x 2w4x_756 2.01 2w4x_407 0.70 2w4x_417 0.86
63 2wca 2wca_773 5.59 2wca_731 1.88 2wca_951 3.72
64 2xj7 2xj7_812 0.40 2xj7_871 1.64 2xj7_914 0.27
65 2vvn 2vvn_406 1.13 2vvn_401 1.83 2vvn_205 0.61
66 3aru 3aru_573 2.65 3aru_311 0.67 3aru_450 2.24
67 3arv 3arv_515 3.27 3arv_821 4.42 3arv_345 0.70
68 3ary 3ary_858 6.03 3ary_762 1.40 3ary_844 1.23
69 3arq 3arq_639 9.10 3arq_655 7.80 3arq_643 8.72
70 3arp 3arp_17 1.39 3arp_67 0.98 3arp_284 1.83
71 4ih5 4ih5_867 1.96 4ih5_192 3.09 4ih5_159 2.57
72 4ih7 4ih7_461 2.23 4ih7_12 0.69 4ih7_438 1.86
73 3cj4 3cj4_166 0.50 3cj4_179 0.76 3cj4_338 0.64
74 4eo8 4eo8_30 0.50 4eo8_318 0.54 4eo8_206 0.55
75 3gnw 3gnw_729 2.21 3gnw_155 5.15 3gnw_722 1.53
76 1gpk 1gpk_159 0.76 1gpk_415 1.62 1gpk_328 0.52
77 1gpn 1gpn_781 4.18 1gpn_600 1.09 1gpn_658 2.56
78 1h23 1h23_815 1.15 1h23_347 0.89 1h23_12 1.81
79 1h22 1h22_346 0.64 1h22_187 1.33 1h22_384 1.95
80 1e66 1e66_661 2.74 1e66_712 1.76 1e66_404 0.97
81 3f3a 3f3a_612 1.97 3f3a_552 2.20 3f3a_65 0.32
82 3f3c 3f3c_508 0.97 3f3c_686 2.83 3f3c_466 1.55
83 4mme 4mme_16 0.59 4mme_71 0.55 4mme_324 0.60
84 3f3d 3f3d_287 1.55 3f3d_666 2.02 3f3d_516 1.38
85 3f3e 3f3e_844 0.93 3f3e_451 1.15 3f3e_457 0.79
86 2wbg 2wbg_331 0.93 2wbg_719 0.68 2wbg_734 1.53
87 2cbv 2cbv_453 2.15 2cbv_945 0.99 2cbv_872 3.09
88 2j78 2j78_311 0.37 2j78_326 0.70 2j78_458 1.14
89 2j7h 2j7h_720 0.45 2j7h_912 0.48 2j7h_48 0.17
90 2cet 2cet_313 0.85 2cet_734 0.98 2cet_756 0.77
91 3udh 3udh_523 2.02 3udh_477 1.40 3udh_72 0.43
92 3rsx 3rsx_249 0.76 3rsx_388 0.91 3rsx_515 0.79
93 4djv 4djv_314 1.09 4djv_105 1.84 4djv_335 0.61
94 2vkm 2vkm_296 1.05 2vkm_955 1.47 2vkm_707 0.95
95 4gid 4gid_197 0.77 4gid_291 0.81 4gid_201 0.38
96 4jfs 4jfs_899 0.85 4jfs_139 0.69 4jfs_362 0.66
97 4j28 4j28_110 0.90 4j28_794 0.34 4j28_196 0.87
98 2wvt 2wvt_736 0.85 2wvt_217 0.34 2wvt_922 0.61
99 2xii 2xii_763 0.88 2xii_237 0.53 2xii_146 0.53
100 4pcs 4pcs_196 0.41 4pcs_173 0.77 4pcs_812 0.56
101 3rr4 3rr4_369 0.22 3rr4_701 0.44 3rr4_529 0.89
102 1s38 1s38_305 0.47 1s38_432 0.84 1s38_485 0.99
103 1r5y 1r5y_167 0.16 1r5y_341 0.31 1r5y_113 0.44
104 3gc5 3gc5_426 0.97 3gc5_402 0.72 3gc5_464 0.92
105 3ge7 3ge7_795 0.99 3ge7_794 1.03 3ge7_895 0.91
106 4dli 4dli_96 0.44 4dli_948 1.19 4dli_524 1.15
107 2zb1 2zb1_510 0.99 2zb1_726 0.89 2zb1_875 2.11
108 4f9w 4f9w_25 0.53 4f9w_96 0.53 4f9w_86 0.49
109 3e92 3e92_106 0.32 3e92_482 0.91 3e92_17 0.59
110 3e93 3e93_390 2.73 3e93_7 0.68 3e93_51 0.34
111 4owm 4owm_362 0.56 4owm_232 0.61 4owm_135 2.35
112 3twp 3twp_510 0.46 3twp_362 2.42 3twp_608 1.94
113 3r88 3r88_628 4.18 3r88_533 0.98 3r88_632 1.17
114 4gkm 4gkm_375 0.56 4gkm_301 2.32 4gkm_111 0.46
115 3qqs 3qqs_35 0.61 3qqs_105 0.81 3qqs_511 1.32
116 3gv9 3gv9_30 0.64 3gv9_7 0.59 3gv9_59 0.59
117 3gr2 3gr2_973 1.50 3gr2_337 1.94 3gr2_872 2.26
118 4kz6 4kz6_315 0.54 4kz6_723 1.76 4kz6_308 0.28
119 4jxs 4jxs_334 1.00 4jxs_306 0.81 4jxs_147 0.77
120 2r9w 2r9w_460 3.92 2r9w_910 0.78 2r9w_927 0.68
121 2hb1 2hb1_167 0.53 2hb1_313 0.32 2hb1_981 0.79
122 1bzc 1bzc_225 1.01 1bzc_375 0.69 1bzc_265 0.70
123 2qbr 2qbr_385 0.80 2qbr_301 1.10 2qbr_282 0.91
124 2qbq 2qbq_235 0.92 2qbq_276 0.55 2qbq_752 0.38
125 2qbp 2qbp_272 0.59 2qbp_288 0.77 2qbp_259 0.84
126 1q8t 1q8t_706 1.15 1q8t_879 0.79 1q8t_60 0.92
127 1ydr 1ydr_801 1.00 1ydr_701 0.98 1ydr_80 0.79
128 1q8u 1q8u_796 0.54 1q8u_902 0.69 1q8u_901 0.63
129 1ydt 1ydt_817 1.01 1ydt_251 0.54 1ydt_283 0.93
130 3ag9 3ag9_891 2.82 3ag9_712 3.32 3ag9_850 2.93
131 3fcq 3fcq_107 1.83 3fcq_365 0.79 3fcq_135 3.09
132 1z9g 1z9g_849 0.89 1z9g_789 0.75 1z9g_790 0.64
133 1qf1 1qf1_198 0.99 1qf1_814 0.64 1qf1_397 0.95
134 5tmn 5tmn_950 0.93 5tmn_717 1.71 5tmn_996 1.96
135 4tmn 4tmn_746 2.14 4tmn_710 1.15 4tmn_209 1.03
136 4ddk 4ddk_568 3.45 4ddk_632 3.88 4ddk_496 4.33
137 4ddh 4ddh_462 3.71 4ddh_882 3.96 4ddh_427 1.25
138 3ivg 3ivg_209 0.97 3ivg_250 0.82 3ivg_252 0.80
139 3coz 3coz_7 3.03 3coz_401 3.19 3coz_561 4.61
140 3coy 3coy_258 2.90 3coy_695 8.03 3coy_291 2.65
141 3pxf 3pxf_539 1.98 3pxf_631 1.93 3pxf_775 1.61
142 4eor 4eor_701 0.53 4eor_121 0.52 4eor_850 0.62
143 2xnb 2xnb_770 1.55 2xnb_291 0.89 2xnb_751 0.63
144 1pxn 1pxn_96 0.51 1pxn_197 0.77 1pxn_86 0.83
145 2fvd 2fvd_286 1.08 2fvd_288 0.54 2fvd_345 1.41
146 4k77 4k77_328 0.37 4k77_167 0.22 4k77_257 0.54
147 4e5w 4e5w_286 0.40 4e5w_247 0.23 4e5w_121 0.15
148 4ivb 4ivb_142 0.45 4ivb_253 0.22 4ivb_875 0.84
149 4ivd 4ivd_771 0.83 4ivd_962 1.03 4ivd_823 0.37
150 4ivc 4ivc_232 0.28 4ivc_831 1.33 4ivc_841 0.90
151 4f09 4f09_903 0.51 4f09_270 0.27 4f09_893 0.30
152 4gfm 4gfm_225 0.43 4gfm_214 0.46 4gfm_913 0.44
153 4hge 4hge_797 2.55 4hge_617 4.13 4hge_471 1.72
154 4e6q 4e6q_120 0.39 4e6q_936 0.96 4e6q_132 0.96
155 4jia 4jia_60 0.65 4jia_85 0.80 4jia_66 0.82
156 2brb 2brb_302 0.71 2brb_318 0.71 2brb_383 0.62
157 2br1 2br1_354 0.83 2br1_349 1.00 2br1_392 0.83
158 3jvr 3jvr_580 2.06 3jvr_484 4.51 3jvr_471 5.37
159 3jvs 3jvs_884 0.98 3jvs_904 1.30 3jvs_897 0.84
160 1nvq 1nvq_927 0.40 1nvq_710 0.21 1nvq_871 0.85
161 3acw 3acw_411 0.85 3acw_809 0.58 3acw_281 1.61
162 4ea2 4ea2_565 4.95 4ea2_819 2.38 4ea2_798 3.64
163 2zcr 2zcr_495 1.68 2zcr_383 1.39 2zcr_324 0.85
164 2zy1 2zy1_175 1.13 2zy1_119 1.42 2zy1_172 0.95
165 2zcq 2zcq_593 8.22 2zcq_168 1.98 2zcq_471 8.00
166 1bcu 1bcu_684 0.92 1bcu_397 0.66 1bcu_852 2.22
167 3bv9 3bv9_124 1.88 3bv9_35 2.28 3bv9_325 0.88
168 1oyt 1oyt_711 0.40 1oyt_947 0.62 1oyt_59 0.51
169 2zda 2zda_264 0.76 2zda_277 0.43 2zda_299 0.46
170 3utu 3utu_237 0.90 3utu_701 1.31 3utu_374 0.45
171 3u9q 3u9q_539 2.60 3u9q_621 1.91 3u9q_416 0.88
172 2yfe 2yfe_784 1.90 2yfe_232 5.16 2yfe_240 1.03
173 3fur 3fur_960 0.93 3fur_243 0.46 3fur_138 0.91
174 3b1m 3b1m_657 7.09 3b1m_778 2.92 3b1m_215 1.33
175 2p4y 2p4y_314 2.87 2p4y_765 2.77 2p4y_472 3.48
176 3uo4 3uo4_388 0.96 3uo4_343 0.54 3uo4_329 0.43
177 3up2 3up2_231 0.82 3up2_29 0.80 3up2_4 0.67
178 3e5a 3e5a_129 0.64 3e5a_313 0.83 3e5a_398 1.00
179 2wtv 2wtv_108 0.49 2wtv_174 0.53 2wtv_320 0.40
180 3myg 3myg_965 2.04 3myg_333 0.91 3myg_925 1.93
181 3kgp 3kgp_5 1.00 3kgp_31 1.00 3kgp_20 0.99
182 1c5z 1c5z_3 0.40 1c5z_8 0.45 1c5z_452 0.77
183 1o5b 1o5b_400 0.41 1o5b_329 0.60 1o5b_396 0.56
184 1owh 1owh_3 0.66 1owh_391 0.26 1owh_307 0.53
185 1sqa 1sqa_787 0.64 1sqa_776 0.65 1sqa_366 0.83
186 4jsz 4jsz_672 2.00 4jsz_491 2.64 4jsz_815 1.73
187 3kwa 3kwa_758 2.38 3kwa_541 1.91 3kwa_852 2.92
188 2weg 2weg_838 0.90 2weg_335 1.54 2weg_515 1.33
189 3ryj 3ryj_387 0.81 3ryj_229 0.40 3ryj_395 0.74
190 3dd0 3dd0_645 0.76 3dd0_479 0.92 3dd0_570 1.32
191 2xdl 2xdl_549 5.58 2xdl_559 5.12 2xdl_495 5.33
192 3b27 3b27_307 0.24 3b27_106 0.64 3b27_366 0.38
193 1yc1 1yc1_246 0.96 1yc1_214 1.24 1yc1_38 0.59
194 3rlr 3rlr_365 0.54 3rlr_197 0.43 3rlr_397 1.07
195 2yki 2yki_893 0.94 2yki_830 0.98 2yki_841 1.13
196 1z95 1z95_313 0.57 1z95_337 0.51 1z95_303 0.38
197 3b68 3b68_304 0.87 3b68_475 4.89 3b68_291 0.90
198 3b5r 3b5r_443 4.99 3b5r_575 5.00 3b5r_557 1.73
199 3b65 3b65_533 3.17 3b65_639 3.80 3b65_542 3.38
200 3g0w 3g0w_874 1.83 3g0w_413 0.93 3g0w_196 0.33
201 4u4s 4u4s_927 0.73 4u4s_787 0.72 4u4s_71 0.37
202 1p1q 1p1q_394 0.86 1p1q_357 0.60 1p1q_121 0.71
203 1syi 1syi_234 0.38 1syi_411 0.89 1syi_259 0.63
204 1p1n 1p1n_216 1.13 1p1n_825 0.77 1p1n_333 0.64
205 2al5 2al5_436 0.76 2al5_339 0.41 2al5_323 0.23
206 3g2z 3g2z_132 2.47 3g2z_1008 1.26 3g2z_1011 0.97
207 3g31 3g31_596 1.96 3g31_781 2.03 3g31_786 2.00
208 4de2 4de2_567 2.06 4de2_108 0.72 4de2_333 0.43
209 4de3 4de3_358 0.53 4de3_322 0.54 4de3_350 0.97
210 4de1 4de1_190 0.24 4de1_366 0.56 4de1_195 0.77
211 1vso 1vso_115 0.67 1vso_198 0.58 1vso_301 0.60
212 4dld 4dld_211 0.34 4dld_410 0.80 4dld_207 0.53
213 3gbb 3gbb_616 4.30 3gbb_783 4.11 3gbb_796 4.04
214 3fv2 3fv2_177 0.59 3fv2_302 0.36 3fv2_298 0.33
215 3fv1 3fv1_436 1.51 3fv1_491 2.03 3fv1_407 1.52
216 4mgd 4mgd_24 0.43 4mgd_96 0.52 4mgd_425 0.98
217 2qe4 2qe4_629 1.55 2qe4_689 2.03 2qe4_693 1.73
218 1qkt 1qkt_766 4.54 1qkt_776 2.15 1qkt_86 0.47
219 2pog 2pog_552 1.52 2pog_853 1.20 2pog_876 0.85
220 2p15 2p15_550 3.78 2p15_331 0.27 2p15_262 0.40
221 2y5h 2y5h_284 0.82 2y5h_364 0.82 2y5h_703 0.82
222 1lpg 1lpg_244 0.75 1lpg_266 1.02 1lpg_254 0.74
223 2xbv 2xbv_317 0.69 2xbv_392 1.22 2xbv_267 0.74
224 1z6e 1z6e_160 0.96 1z6e_350 0.85 1z6e_31 0.84
225 1mq6 1mq6_416 1.99 1mq6_750 1.80 1mq6_266 2.06
226 1nc3 1nc3_524 5.73 1nc3_622 4.76 1nc3_665 5.75
227 1nc1 1nc1_6 0.51 1nc1_739 0.59 1nc1_18 0.58
228 1y6r 1y6r_809 0.67 1y6r_947 0.54 1y6r_232 0.46
229 4f2w 4f2w_142 0.57 4f2w_105 0.60 4f2w_907 0.44
230 4f3c 4f3c_744 0.73 4f3c_251 0.78 4f3c_755 0.32
231 1uto 1uto_428 0.71 1uto_834 0.67 1uto_203 1.76
232 4abg 4abg_824 0.96 4abg_149 0.93 4abg_814 1.00
233 3gy4 3gy4_184 0.34 3gy4_39 0.50 3gy4_14 0.64
234 1k1i 1k1i_896 1.65 1k1i_79 1.99 1k1i_71 1.97
235 1o3f 1o3f_372 0.49 1o3f_752 0.66 1o3f_910 0.58
236 2yge 2yge_35 0.83 2yge_198 1.01 2yge_74 0.68
237 2fxs 2fxs_927 0.79 2fxs_905 1.62 2fxs_953 0.54
238 2iwx 2iwx_394 0.36 2iwx_351 0.33 2iwx_366 0.54
239 2wer 2wer_888 0.72 2wer_73 0.27 2wer_390 0.41
240 2vw5 2vw5_227 0.60 2vw5_381 0.43 2vw5_249 0.28
241 4kzq 4kzq_14 0.45 4kzq_5 0.66 4kzq_133 0.74
242 4kzu 4kzu_256 0.53 4kzu_206 0.45 4kzu_5 0.88
243 4j21 4j21_50 0.34 4j21_430 0.88 4j21_119 1.35
244 4j3l 4j3l_761 0.60 4j3l_858 0.90 4j3l_733 0.63
245 3kr8 3kr8_884 0.56 3kr8_894 0.62 3kr8_785 0.68
246 2ymd 2ymd_484 1.53 2ymd_587 1.52 2ymd_434 4.18
247 2wnc 2wnc_514 1.65 2wnc_587 3.03 2wnc_532 1.79
248 2xys 2xys_499 1.73 2xys_443 2.02 2xys_398 0.44
249 2wn9 2wn9_11 4.21 2wn9_448 2.49 2wn9_474 3.64
250 2x00 2x00_817 1.42 2x00_705 1.23 2x00_464 1.72
251 3ozt 3ozt_202 0.52 3ozt_702 0.78 3ozt_70 0.26
252 3ozs 3ozs_255 0.69 3ozs_201 0.79 3ozs_224 1.24
253 3oe5 3oe5_236 0.42 3oe5_203 0.70 3oe5_266 0.67
254 3oe4 3oe4_203 0.47 3oe4_292 0.34 3oe4_328 0.42
255 3nw9 3nw9_210 0.81 3nw9_945 0.53 3nw9_975 1.01
256 3ao4 3ao4_165 0.61 3ao4_195 0.68 3ao4_456 0.80
257 3zt2 3zt2_5 0.39 3zt2_8 0.49 3zt2_323 0.82
258 3zsx 3zsx_293 1.23 3zsx_955 1.91 3zsx_961 1.78
259 4cig 4cig_809 1.14 4cig_784 2.57 4cig_787 1.79
260 3zso 3zso_923 2.49 3zso_799 1.59 3zso_902 1.59
261 3n7a 3n7a_552 1.06 3n7a_495 1.11 3n7a_993 1.36
262 4ciw 4ciw_482 1.12 4ciw_451 0.78 4ciw_267 0.46
263 3n86 3n86_531 1.45 3n86_870 0.87 3n86_861 0.90
264 3n76 3n76_586 2.15 3n76_758 1.26 3n76_578 1.34
265 2xb8 2xb8_35 0.74 2xb8_133 0.62 2xb8_28 0.66
266 4bkt 4bkt_17 0.99 4bkt_369 1.00 4bkt_541 1.90
267 4w9c 4w9c_719 0.92 4w9c_925 1.15 4w9c_379 0.53
268 4w9l 4w9l_368 0.71 4w9l_204 0.96 4w9l_264 0.88
269 4w9i 4w9i_204 0.42 4w9i_795 0.74 4w9i_221 0.45
270 4w9h 4w9h_368 0.40 4w9h_307 0.38 4w9h_765 0.96
271 3nq9 3nq9_700 2.59 3nq9_470 1.37 3nq9_473 1.47
272 3ueu 3ueu_52 0.87 3ueu_438 1.14 3ueu_425 1.51
273 3uev 3uev_684 6.20 3uev_655 4.50 3uev_700 1.00
274 3uew 3uew_613 1.76 3uew_624 1.57 3uew_282 1.09
275 3uex 3uex_588 6.75 3uex_640 4.72 3uex_227 0.73
276 3lka 3lka_495 1.55 3lka_443 2.05 3lka_498 4.67
277 3ehy 3ehy_685 1.05 3ehy_706 0.66 3ehy_977 0.73
278 3tsk 3tsk_713 3.96 3tsk_423 3.84 3tsk_263 3.98
279 3nx7 3nx7_36 0.73 3nx7_854 1.92 3nx7_778 0.99
280 4gr0 4gr0_326 0.91 4gr0_206 0.97 4gr0_279 0.95
281 3dxg 3dxg_465 4.27 3dxg_464 4.51 3dxg_450 3.06
282 3d6q 3d6q_23 2.26 3d6q_50 2.46 3d6q_276 2.75
283 1w4o 1w4o_367 0.69 1w4o_376 0.67 1w4o_313 0.81
284 1o0h 1o0h_603 2.49 1o0h_564 2.05 1o0h_353 0.92
285 1u1b 1u1b_919 2.04 1u1b_2 2.80 1u1b_929 1.97
Summary of the docking power: ========================================
Among the top1 binding pose ranked by the given scoring function:
Number of correct binding poses = 225, success rate = 78.9%
Among the top2 binding pose ranked by the given scoring function:
Number of correct binding poses = 249, success rate = 87.4%
Among the top3 binding pose ranked by the given scoring function:
Number of correct binding poses = 262, success rate = 91.9%
Spearman correlation coefficient in rmsd range [0-2]: 0.503
Spearman correlation coefficient in rmsd range [0-3]: 0.606
Spearman correlation coefficient in rmsd range [0-4]: 0.664
Spearman correlation coefficient in rmsd range [0-5]: 0.690
Spearman correlation coefficient in rmsd range [0-6]: 0.710
Spearman correlation coefficient in rmsd range [0-7]: 0.718
Spearman correlation coefficient in rmsd range [0-8]: 0.724
Spearman correlation coefficient in rmsd range [0-9]: 0.727
Spearman correlation coefficient in rmsd range [0-10]: 0.735
======================================================================
Template command for running the bootstrap in R program===============
rm(list=ls());
require(boot);
data_all<-read.table("DeepDock_Top1.results",header=TRUE);
data<-as.matrix(data_all[,2]);
mymean<-function(x,indices) sum(x[indices])/285;
data.boot<-boot(aa,mymean,R=10000,stype="i",sim="ordinary");
sink("DeepDock_Top1-ci.results");
a<-boot.ci(data.boot,conf=0.9,type=c("bca"));
print(a);
sink();
========================================================================
================================================
FILE: Validation_CASF2016/Score_CoreSet_docking_CASF2016.csv
================================================
PDB_ID,Score_3A,Score_5A,Score_7A,Score_10A,Score_all
4k18,64.25368680000449,420.5898705181299,1376.762661813891,1410.0162568367755,1410.01638602799
4qac,83.2844061298711,450.01256968999587,1128.0662764096696,1154.7673063010277,1154.7690904503804
1o3f,187.7132053065723,778.5596122182768,1779.2439875640941,1823.1001001165655,1823.110781221914
4ih7,35.630378250691514,270.11676199153,787.7759051254867,814.7537476029596,814.755215144911
3dx1,49.94476787861658,202.24570100387868,449.72902901247784,462.38353806385936,462.39191325604116
1syi,116.9398155462117,391.15166747375895,625.6986253559332,630.2346845045558,630.234684508099
2p4y,57.90463862690466,471.202897482013,1760.421599629337,1796.915052619172,1796.9165506187253
3nq9,33.47588015171437,207.30927205929478,516.9870798188878,528.891814691647,528.8965928371339
3wtj,49.56685398444063,301.54900360610253,788.1912248573859,811.5618562122997,811.5618741514085
4w9i,96.53538985888554,508.11983168229045,1506.1435864409493,1547.9571235325861,1547.9576589526225
3b65,83.67242226319325,388.59912533848654,730.792876946801,735.69347322748,735.6934735857485
3pww,109.47361683367602,756.232060535363,2463.9988941129027,2524.53477613409,2524.535443629194
4w9c,76.86428371213454,414.5499905279886,1227.66042698994,1256.5354431236563,1256.5385864661514
3kwa,30.99373518349748,224.15072679477706,575.1643603336886,585.8942961196938,585.895796784691
3arq,41.46589454797301,366.78673511443844,1474.1056338105684,1514.0147807425642,1514.0216529107513
3d6q,52.57305927450281,291.370081673399,1021.173594464719,1057.5406470057792,1057.5407480872227
2w4x,65.65657006412503,332.5830237787524,872.037574312839,897.7391597361931,897.7423130070734
2j7h,97.90040477159023,354.7562210906335,607.4412389100081,614.8811845830869,614.8817966508483
2xbv,173.30578064984272,711.534934063176,1776.7760761242082,1814.5594376194824,1814.5594486388882
2cet,148.89340067116262,593.8951258807765,1336.2455920742618,1363.6287341153113,1363.6287760119342
3uri,108.93182696449132,781.0031900093652,2505.470324704075,2565.3778223449885,2565.4101926629755
4w9l,109.21992566122394,633.1535235609476,1841.3237394421858,1882.9890225879462,1882.9929035796474
3wz8,70.19069819496185,442.9084974991665,1523.6955066881048,1551.1895356411324,1551.1897490276883
2qbp,129.98330282846908,726.4363792184914,1862.613576237775,1903.3253945703898,1903.3260608572034
1bzc,142.20843878072364,560.535567283944,1354.7753249751152,1389.211418765663,1389.2296413652778
4lzs,60.4717740479385,307.14667456390157,868.3877508953361,891.8268682863918,891.8269576721908
2brb,225.0963547257851,995.2992740121092,2259.6152073754015,2316.3254264847515,2316.3259042459595
3g0w,104.25695170313782,460.5435946902714,806.6475176947316,809.8801258258953,809.8801258486827
2zb1,138.56445027826103,668.22042671508,1640.8178049609198,1679.8306384301418,1679.8311950399548
1bcu,30.149218568097865,226.22615242942663,831.6727823011795,853.5230411403893,853.5240848815982
3n86,170.66525811690448,577.7076506174901,930.9559258516196,938.9696563368942,938.9696582196754
4gid,282.8068246406915,1227.0103938011664,3178.59492693241,3247.98461944219,3247.9846385531496
3rlr,175.67725512671456,743.407529746633,1701.0242093568522,1732.353545994232,1732.3537639412748
3gy4,111.29248372740311,420.38025990806716,773.4290884200994,791.5258246028503,791.5275376826619
2al5,82.93395433719911,291.68055181097344,433.05404553953696,434.7932567006077,434.7932567016807
2zcr,89.4529798988706,578.8675614403595,1454.9294938745927,1485.633741020731,1485.6639689420872
4kz6,43.053031155720035,286.8594590513716,817.4868867109249,834.6251062873847,834.6266661854299
2pog,37.59505887069121,279.6626078727504,884.7315623261026,903.9807974390334,903.9809864298318
3gv9,51.97243037394204,264.2774430053365,707.2984425113002,730.2215897293113,730.2237246370099
4ciw,106.01380748458514,360.8458228780642,596.5508569387636,599.451834904386,599.4518356225294
3zso,89.34017751012705,569.5758815764781,1842.2477815110226,1893.1997948798576,1893.2078888279032
4agn,82.34596035507685,385.2471623221731,1298.2612796466055,1334.5469145926565,1334.5475079059402
4agq,74.29437993709442,426.2532388868197,1488.2560794522292,1534.0639998330294,1534.0641183221585
1p1q,74.86978910321334,299.50261411338124,461.56718949284925,463.35856886100555,463.3585688610053
1w4o,75.3190960193708,359.61339135867735,994.0042230701173,1019.8816660237684,1019.9086143043672
4ivb,240.88957019469748,969.3943805654129,2138.813561566484,2191.4259662414975,2191.426325088349
5c2h,155.39817508164646,694.2327565412025,1884.0479858969936,1928.7299648617225,1928.7316221750543
2zcq,41.66829266511523,320.53796067002,967.0759912227169,988.4208250315521,988.4378068859729
4kzq,241.5459781319007,921.271895182531,1751.8539737021754,1791.9297189940582,1791.931833176007
2w66,51.41834035728448,247.55760472681231,600.73799152601,617.2747460154192,617.2756125515189
3coz,127.91761583054837,604.8223512732975,1366.6604060734746,1387.255540083405,1387.2568462322295
1pxn,207.8112981679745,908.8779494880097,1931.9457029336947,1972.507201404742,1972.5087464050723
5tmn,128.0113955295065,663.0539037682043,1792.5686600436632,1831.9834478960079,1831.9866136573798
3acw,63.56074891081238,387.65490154021313,1016.2258677792,1035.3422418235805,1035.3422600105223
1q8t,114.807850322019,533.8765799231827,1139.8516759338022,1157.6315896228546,1157.6319985872306
3b68,132.1883954764254,548.3670773141253,1026.328769154475,1034.488374967187,1034.488374987008
4ivd,230.79211623304295,953.4396616327679,2259.728955611641,2314.575009128246,2314.5765522639163
4ogj,49.295854866203264,427.50475195372167,1383.4056356280162,1415.5166785165036,1415.5176162058542
4u4s,74.64814798186394,364.09075770596974,892.1262809810744,913.9977194033896,913.998691529616
4qd6,78.29298863252524,472.5207828870162,1517.7439125501155,1563.7078009262364,1563.7094154053989
4ddk,40.874313743084535,190.44147507297487,453.3582115073565,465.8315930818892,465.83483964612105
3rsx,57.1997951969251,289.6778936871291,1094.7634398440302,1127.0747597807167,1127.0760409553668
3mss,65.63472241458663,268.8088284197675,840.0642377278357,873.2838638626026,873.2867599547905
4eo8,95.61381106933699,504.4111707373174,1387.1250721306542,1418.2553356241979,1418.2700783156793
4e6q,188.80905044379313,832.1643044823866,1951.8010748544823,2012.7497470811925,2012.7511032569869
2yki,179.4577909762517,796.9256297213019,1845.5946985796675,1880.7050819371786,1880.708167897301
3zdg,53.17584631076557,290.0100541045753,555.7464125182674,565.1428135377329,565.1428411401416
2xdl,25.046986407646195,213.9510169873218,652.1815330659972,669.483275593557,669.4832788665115
4f9w,121.31322212815593,639.9163050832431,1734.352472814851,1766.48390832262,1766.484024874218
1nc1,134.33215818548123,512.18873009553,1034.1083366476532,1061.6449363070917,1061.644961292931
1q8u,139.44337534955733,654.0691865749186,1516.627664932013,1550.5415346597028,1550.5415436229564
2wn9,43.53979084967304,318.30683411279995,944.8581903903282,964.1829558687275,964.1830997941311
4ty7,108.41087746556879,579.6691740972386,1624.4405448150615,1674.0560482148421,1674.0562253298622
5c28,53.68383930761231,251.05801879449893,782.6509314473636,799.979425958907,799.9795015164239
3aru,51.940529475756726,250.76419576359316,969.5566459241943,997.7504712473973,997.7623273304991
3ozt,133.31380983772215,597.1863368379626,1718.2851693569096,1758.961263234737,1758.962471536315
2r9w,66.35331833988039,375.00175712194033,1406.438538461237,1459.480767609888,1459.4985280065216
4gkm,116.54629788710679,438.41271318554845,1230.9903091942963,1255.9281167736026,1255.9329754489506
3d4z,117.98636640595329,463.45002060688665,998.9402328376156,1019.9987522882255,1019.9989963300468
2qbr,122.68550640738262,622.0232172127211,1513.7827723686078,1557.1758556276138,1557.1765648920605
4kzu,134.3689919742847,550.6633246268267,1256.8281858477696,1282.939660238985,1282.9396637012564
1qkt,62.40675815102743,395.66152515381174,959.8945586155565,979.8526605845133,979.852692284026
3ao4,85.23851690854748,361.7577446786369,985.1113600639211,1008.0537617739011,1008.0555919808124
3o9i,268.3784052730718,1456.6249878459166,3566.1595485867338,3643.071880083336,3643.071888645334
3f3d,45.23135208030822,158.87551431375311,219.4810945638009,220.5924951469158,220.59249514691618
3ui7,90.67524311818082,492.25748725986335,1309.6792255343146,1336.7294084161467,1336.729527232955
3oe4,145.22316248164765,655.5279014005486,1843.529712982902,1883.0515954917594,1883.051990714626
3l7b,78.02043525361384,445.15621362249874,1097.351431522535,1118.14670524701,1118.147010634481
3syr,135.9825435979085,619.8941293411061,1316.4842610005849,1336.4913339408063,1336.4922975086379
2xys,47.12748264191115,407.84898648875765,1284.1159213013018,1313.1553571826812,1313.1559750655745
3ehy,79.51968957261982,372.63201814110056,1015.1562807368335,1042.7595852820173,1042.7609753934892
3udh,55.4528811251183,273.03351949819563,860.1572594749111,895.6043694388683,895.6067747370769
4m0y,48.5035581751718,332.9986808808436,1095.226761622166,1130.363662512227,1130.3636751330214
3b1m,75.60002189786577,546.2001290165846,1637.2178549406715,1669.5794516701646,1669.5821463783518
4f09,217.21849282768363,827.605522198621,1850.4025446250773,1906.8718886387267,1906.8743390709963
4jfs,57.51840860924338,309.4556481846409,938.8190806975498,966.5673315093054,966.5674391421736
3r88,39.315189436873105,220.0978493523487,750.2684029017379,781.4957760414951,781.5029808406172
4twp,206.61871310203037,813.1544450582226,1924.2868751807207,1976.3399736990912,1976.344617321613
3jvr,57.42774668533345,303.5280565186011,887.4144080314289,908.0905985515116,908.099629826913
2zy1,56.24394327551344,417.60781114420104,1083.9587020682507,1108.4762177079117,1108.4845986909856
4f2w,179.3348004840762,639.4944712419004,1166.3429739129276,1181.0689981831927,1181.068999191425
3p5o,84.95547075093268,539.1941368846183,1676.9997477633215,1713.6397785585273,1713.6402794920918
3uev,58.53790878146492,302.3698216700944,633.9253469052435,643.0756878215319,643.0858481843482
2c3i,55.99472438233325,369.20083849656845,1077.8447580761529,1114.4171499799984,1114.4193090517851
1gpk,51.904087341317364,287.0017284989793,815.1828381399653,825.2516273047795,825.253277224334
4j21,70.45708961275871,425.6765932825507,1120.6120670833652,1145.3497982480822,1145.3498714414793
2wbg,132.8108996150025,539.954395871111,1214.4132742930044,1232.268524902991,1232.268849090071
1u1b,102.35434660308442,578.4572686821949,1874.659728120159,1917.6950160034212,1917.7161081756105
3pyy,84.93198594046096,398.58810752169387,1095.1729051027498,1119.538126309157,1119.541408131035
3gbb,0.06890520124251087,17.112258941830518,224.56040119232824,234.22000679215128,234.22141951708397
2ymd,33.902769517632365,186.7005860405782,513.6216190227024,530.8385260310919,530.8387286837558
4rfm,170.94164607299794,738.5488600515625,1969.522222360323,2014.5475967687155,2014.549743883879
3up2,176.67445875063075,812.4485545878541,1951.4609516277649,2009.3128904130383,2009.3156948153432
2x00,35.55253248858926,414.3030290245455,1550.5906141425921,1585.6906892632205,1585.6981148300351
1c5z,114.18460401698978,362.5067965080121,651.2340136425198,660.2498932710652,660.2500705708463
3e5a,207.45615594069304,972.567304543089,2572.958311132982,2647.4248690226855,2647.425475325077
2br1,203.16008993633574,982.5232834127136,2349.5647993716966,2406.9794618234164,2406.9813715115665
4abg,112.16332946983172,440.29788805746205,971.3499095590251,988.5827419385889,988.5830351168673
3bgz,75.06061392673935,423.68347241477295,1153.5326489764461,1175.876432154148,1175.8786905410807
3e93,169.42238635634,807.9173749615867,2074.2737353995917,2132.071371823919,2132.1069635562267
1e66,84.86472985160272,382.53177048909424,1035.4035720462666,1056.1737168686532,1056.1737283722873
4ddh,43.68678178137616,241.86601266620013,697.5395534118229,711.1228184044425,711.1330140713152
3g2z,36.09592184920321,196.3288246736143,606.9050699498672,620.1920618246725,620.1921143597384
4bkt,49.777382355196565,276.77265894734813,764.5137563688843,783.666147516982,783.6661634336577
3zsx,86.11974064983634,480.03264077682,1583.6364761531033,1632.1643062248406,1632.1698844025266
2iwx,134.2220304929089,573.1245303091908,1468.4429236222811,1516.1954725182293,1516.1955719498892
4jxs,97.51338687062007,441.7541992662814,1233.3043756618786,1269.1094998150306,1269.1140611435237
1r5y,110.20274891665184,367.10939439531273,919.7978489195657,947.8726149380695,947.8728402214789
4agp,72.65663421149749,421.6920117003555,1439.942438449551,1486.2358593359477,1486.2404418967299
3fur,97.40766312559836,502.37752415421284,1543.722548733556,1572.4404511914063,1572.4457199084623
3f3e,49.10200938545378,174.5601983892319,246.14259264061258,247.42783114789205,247.427831147892
3gnw,80.69402766856462,484.08976934078527,1839.554422916218,1883.3035303999925,1883.3158690480209
1nc3,46.86511291162159,248.99366859202334,513.1663051687262,521.2688612608283,521.2689985170425
3rr4,159.92456426622462,524.3124708559553,1305.737185918533,1339.6268194558243,1339.6298765497845
1a30,53.961677635028366,379.01095830851625,1418.717343312916,1449.3258459373696,1449.3442527657917
5aba,71.49611435134136,367.18232875642553,1112.1481156246548,1150.5741424800733,1150.5808686017645
4de1,87.42883468650838,385.2398042337791,1018.1157004821957,1043.9526047761601,1043.9527303858333
3gr2,27.788878682078742,207.701501357501,767.1331941797696,787.7724348866395,787.7736947812699
3g31,18.029257028896097,172.79689279809622,614.9815420878512,634.0198019324653,634.0274957508785
3tsk,118.59162910714245,654.8028676461589,1831.0677026113597,1857.4317443721152,1857.4412365028113
3dx2,77.62261755818663,312.8474271744499,702.1014636353327,715.4697869564601,715.4705924259641
2wvt,45.5513382043203,244.01365119837658,559.6090696488351,571.3663333569793,571.3676306810315
2qbq,120.79627728464213,601.601343491938,1534.8531398172483,1563.4006789894263,1563.4009019108828
3jya,84.33424241265483,353.7782495328443,807.6351796301216,829.5187014312322,829.527908115732
1qf1,118.9266992560742,549.8295973885776,1321.944954054909,1345.8819438759872,1345.900802023985
4eky,156.04731732329054,710.659160173029,1595.0150816371872,1626.754733916453,1626.7554537138824
4x6p,230.740653255336,902.8622036130818,2138.372521532639,2188.206082605559,2188.2061829516533
3u5j,61.71826321839202,369.934843513085,1137.825125240837,1158.0395861550685,1158.040762476053
3kr8,234.239751706756,936.8166709605848,1793.3906661533704,1829.2453285778802,1829.2514473726626
1z6e,251.22075175167163,971.7924535206723,2106.4999214055374,2140.297734916342,2140.331660088661
2qnq,122.02950045273612,739.3365851587711,2278.0965045056123,2330.286232974413,2330.2862731385735
3u8n,90.68645476355074,388.8733442772483,807.045972962928,826.009204226714,826.009207876343
4de3,80.57383272553507,367.53461914187614,944.9751241193605,969.1684145047632,969.1699125147991
3n76,141.7337514036399,542.5673713656719,839.6542073584363,844.3327576710336,844.3327576775132
1k1i,144.29174986601691,677.4738968318584,1734.9734858798406,1765.3906560790276,1765.3915537278338
3ivg,206.88981912576955,862.1730408314874,2002.8738492641846,2043.7921863884978,2043.7922046942851
3fv2,89.06424278630699,378.84441766809385,663.3918065824995,667.5087662684567,667.5087663213866
4j3l,93.72864901644522,506.47409992684146,1496.212158757553,1531.2406045483078,1531.2459203587807
4crc,220.48465350929885,940.3150481862268,2173.9406087382754,2225.4778681033504,2225.481374192204
3f3c,81.40271228157549,272.13367352799406,372.0029738697915,373.4978740402,373.49787404020026
3ejr,113.52817646255357,531.5510045433952,1327.178039193103,1361.745176510526,1361.7456163807558
3u8k,53.86941290139106,312.6511405926052,706.5122757717229,728.1852055518196,728.1852261600418
1g2k,125.52618517921091,759.7399297594816,2380.311360847532,2436.4160704191327,2436.416147397584
2y5h,151.7552734845775,616.9879682710723,1586.8315216474932,1625.3303869148517,1625.3303915283748
2wtv,237.5239452363038,1077.9298408384448,2486.572910859298,2552.1841313140603,2552.1841877300285
1owh,113.99327224746764,544.6019259000719,1394.0877107788665,1423.6926582076999,1423.6929149257342
3myg,201.52254811885803,881.2333273219699,2147.788322411703,2196.868619652684,2196.8686302679994
3u9q,66.344683128555,262.88966322185445,538.5605437624948,547.1770216795074,547.1772674350055
3e92,151.93727012903167,611.5325971536917,1512.301593653244,1553.3868488146243,1553.3868544190211
1nvq,238.1609785931866,1092.741702076978,2766.019353018752,2838.2317575839743,2838.2348068192478
2cbv,49.675353615410195,229.4678236330383,467.18731839788404,477.743804067202,477.74435373965457
3ueu,40.28999503396703,267.4247688829804,623.5776551732604,632.5845493137072,632.5908813285776
1mq6,127.43889641182633,583.5974990153171,1525.2879496665114,1555.9590791614578,1555.962590395576
1s38,145.83515407134468,461.7395236971163,954.1938769766099,975.4034137459547,975.4034474861168
1sqa,136.56284539775865,665.1194562176377,1821.4459110911257,1859.815584768713,1859.8229705667316
2wer,129.09559500467367,614.8589105150033,1513.293209825466,1547.7262635134507,1547.7263016456343
3arp,73.43172980272209,449.2144512647047,1625.5244541812262,1663.6489560571597,1663.6515544933125
4eor,244.2885107643093,1095.2236089379583,2460.6179920682653,2525.9273528444332,2525.9292501869772
4cig,85.95338762511668,492.191298967175,1594.21158997267,1627.07524423406,1627.0794720228166
1gpn,45.62704517731523,274.1842163526325,834.5589901074534,849.3129184251891,849.3137180867575
1lpg,193.22148896914584,947.1987475029003,2263.6168115420237,2303.1871562109013,2303.193831732578
5dwr,125.11775024939983,661.149969526524,1606.6848810750785,1639.7477022739536,1639.7478198098959
2xnb,191.58487132590247,862.7263071273019,2010.8946573497824,2067.1474804569,2067.1517660659088
1eby,302.52884159279034,1664.0560327108558,4067.073187734105,4150.246495583658,4150.246506933233
3b27,118.77868994894992,522.2410974328983,1301.7360583165832,1331.2362279395181,1331.2363092731991
2p15,68.51987826862724,507.60617494357945,1093.6061438038307,1102.480015438066,1102.4800157423458
3oe5,138.49594780604414,604.0211829869484,1800.201597581935,1844.6387802581567,1844.6410242966076
2yfe,68.40220744845742,436.54098309478775,1237.0326694003368,1255.4206168547973,1255.4212137234701
1h22,77.0684679308703,457.0042675586628,1416.4852344152962,1447.3890478043634,1447.39178648152
3dxg,58.19801968574128,288.64185500252205,910.998444913614,936.362900120081,936.3987797659371
3pxf,64.9579400423415,372.9751703060586,1072.8481430803247,1101.8992725446496,1101.9029050832748
4owm,48.90567994533053,203.73922454535602,645.942318926862,677.0143368101316,677.0159379799243
4mgd,62.41492524213345,320.8070359724841,636.1791410654596,638.9067260626163,638.9067260629522
2xb8,107.5612130367144,533.4336900639117,1160.2829307272596,1183.2634206849702,1183.263474434914
1p1n,74.77395989365196,377.3417538774186,899.9481056441746,919.188195287589,919.1888903180904
3ryj,85.48544956005539,465.3829587029031,1121.5493665511776,1148.369664797751,1148.3720723829324
3jvs,53.34291702373166,348.26637075667185,1126.40973105261,1158.2886304160436,1158.2917699675697
4ivc,220.53126282308304,880.9216611111151,2079.0261640504896,2136.0982866282584,2136.0983026034137
3cj4,76.92793760074365,352.4700777723109,970.1691070523253,993.8992793276916,993.9159459202934
2zda,165.49968505790272,700.0078859933243,1763.0223329920107,1802.3621415826733,1802.3621632625031
4pcs,60.93095734289848,267.5773449953017,812.384149838497,834.0094253020367,834.013266670858
3nx7,72.72371012919413,398.13889031542743,1130.5324425064073,1163.425574045766,1163.4343693423777
3uuo,60.62135384933508,486.2549037536251,1338.5556036148923,1365.4361274938085,1365.4361338494891
4dli,111.2448089757235,530.8424439128381,1227.1981685336775,1250.9286583317112,1250.9305130745815
1yc1,150.7796642952348,756.5164805536721,1835.956447052314,1872.658901726459,1872.6655460231343
4m0z,125.05641661546355,492.2555160538128,1032.4677639705235,1044.9538787918962,1044.9538788114278
3f3a,60.30223853623419,294.4874886153735,684.8288095439605,698.8598858755284,698.8634892319202
1oyt,133.76831892463966,699.5763083049029,1906.4501342118588,1945.6895215608681,1945.6895610453817
1ydt,115.24941052680111,606.2644842782547,1493.5374105542262,1516.480007365356,1516.4800176539902
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4llx,46.00683188444216,197.23335396963458,479.90952804576915,488.48583613285075,488.4861243400829
3fcq,42.25228975587733,261.566776827906,638.8573379296824,651.8282750184031,651.8390274576104
3utu,225.23875247275987,931.7427595527023,2544.104586452801,2596.189097598867,2596.198935817625
3n7a,57.97671565108146,256.83354748881027,574.5588383083457,586.0978000280758,586.0978833066356
4gfm,125.98794257662138,569.011929023613,1292.9055677114086,1317.5101371285555,1317.5101723105827
3ge7,231.22448089814512,899.8286870221898,2033.2028496636508,2079.155231255804,2079.1579452458186
3arv,42.80072408691845,295.89778746300937,1262.7676876163055,1300.660916527866,1300.6668812797082
4gr0,173.93982304052824,815.5193868085455,2236.8230567696014,2278.3430728213843,2278.34325426704
3ebp,36.45758757074297,271.1664756557015,1150.9588412767002,1183.581304279767,1183.5813401490482
2weg,105.70536798314306,415.32447719286824,836.246592409931,855.3026935244592,855.3029755782493
4de2,82.62485222486708,397.23531833190555,1048.918049363602,1075.8287801035224,1075.8585285967845
3ozs,135.97341995304927,656.6217388919814,1781.3376111795078,1822.7931363007574,1822.7949736035534
3bv9,92.78522250446126,596.0005360315466,1914.592493904816,1963.2924130167037,1963.2947634885077
4w9h,88.94151490470644,448.12008540630995,1371.9626885572532,1399.8984306596237,1399.8984490186806
4j28,57.71785075668828,286.02902291166123,848.8829125005558,878.9510023612356,878.9556271363105
3uex,48.97673846842163,343.0098480966151,751.2928371452637,775.5814371519554,775.5885912593367
2j78,93.42421050671854,324.35266716792904,636.5928225317128,643.2013073580983,643.203776784657
2vvn,121.25871359862296,439.7696151006848,918.2049149225417,935.3666437652082,935.3666556841398
2wca,60.14131149128729,287.7483790383325,945.1417906466695,972.5191853098447,972.5272045296756
3b5r,85.14031993790273,445.83573458171804,818.404994221142,822.917973797609,822.9179741868712
4ih5,42.65882172739712,238.18691888116797,658.2399418492664,674.9561484168494,674.9566594296765
3fv1,67.78496642935463,336.51161717048024,637.9928586598876,642.6177229212294,642.6177229220117
4dld,90.6299993841409,401.9267417801127,1083.16014577152,1106.0615593262505,1106.0634304676785
3qgy,180.13477416149172,887.9314497410414,2245.775569298412,2300.8807403263745,2300.8815201960247
1z95,109.98616734830553,535.8970237594157,1054.411653664801,1063.6213094283548,1063.6213094471839
2yge,79.25614722819542,560.5654598005991,1701.0843074088323,1741.8298679451427,1741.8299586685807
3nw9,192.43401222301213,860.5991744589513,2310.500260486457,2354.521820074236,2354.522192703868
3gc5,240.42624848875653,752.8089198627645,1603.3890535855464,1641.6543387346146,1641.6606834050585
4wiv,54.853001988123665,349.935518609075,1241.087278246133,1277.0147157008228,1277.0147805132642
3zt2,104.25770343359606,487.8158581732334,1372.521522885772,1407.1888601177216,1407.2061846056133
1o5b,160.93466942353245,618.6211986771386,1181.4883029843752,1211.7285837127247,1211.7286381448473
4jia,158.90780972652135,779.6745767522239,2028.2698215169214,2077.843550961454,2077.8436823411726
2xj7,85.49192642237603,355.67439367670363,795.018193232819,812.8547385476201,812.8547642334973
1uto,61.82335925723835,244.95006430579645,557.6405887008694,567.5768045020687,567.5791350215774
4e5w,214.52904685386332,911.0836420991817,2020.4123612737687,2059.426070023246,2059.429874853043
4ea2,46.92482674903071,416.69535236420296,1067.7833006893652,1094.332694836215,1094.332976780729
5a7b,38.46176768777711,330.93160207513444,1475.9469612646212,1529.2097567444864,1529.209832986159
1z9g,66.76297713383313,351.19877126882136,887.8861771788729,907.3167578460889,907.3307995145109
2vw5,58.16591468760708,506.6658478536007,1640.2148178651134,1671.4352647955404,1671.4388395740048
2v7a,242.41069766204544,1102.1510732689155,2541.9270255857955,2594.9086674831974,2594.910329870373
3ary,33.79245434970182,232.8008153561731,881.1714886059469,908.3718731403166,908.3731393700996
1vso,63.565775462764584,351.3756280879247,1213.5210729500434,1243.9895302668367,1244.0811759594021
4f3c,206.6787010169106,734.8521731092297,1401.2404277770668,1421.7728651546204,1421.7728718077794
3ag9,183.89230222163638,959.7783038980517,2854.781539012572,2913.82829744705,2913.8310864326036
1y6r,180.9012235621722,654.9486376120151,1229.3933665065474,1253.082887456417,1253.0829026516183
3lka,46.046566727322414,235.12541962052015,717.9697104181355,752.2961821168801,752.2962197654572
1ps3,118.27019232842633,475.08686456914216,1077.8677808087857,1095.8332854178202,1095.834040408224
4hge,82.63889607048401,390.52319490000167,1228.4610373557575,1269.0476132704057,1269.0478918981612
4tmn,116.12725253463549,670.2215350485362,1800.0803038454644,1829.7197469684859,1829.725255220932
4djv,81.96157683353435,538.6322286483108,1515.6364498378477,1559.008854261301,1559.0090831009816
2v00,44.072573751782905,292.964116171927,904.1732951203179,939.7554480490517,939.7568191461457
4mme,93.56847547511411,440.9362315037897,1043.3673149732806,1059.8532200220782,1059.8533210123194
3kgp,54.88485232773245,233.4066682169148,526.4104625973101,547.8030058785748,547.8065314585393
2wnc,37.3217183027551,320.9530295801492,1189.0658582706171,1221.6075452362138,1221.6077414513825
3g2n,128.78744864150067,649.7932334548124,1383.2355529328995,1410.3164975694679,1410.3166088921055
2qe4,59.18318336516832,397.85282578070354,1120.9746627133277,1137.89513684174,1137.8951459254001
3twp,36.86541313427344,172.54929656024868,559.3674725785584,582.9002821976986,582.9057504317688
3uew,54.73958755960947,317.950103265201,715.332948548003,734.3868739752412,734.389216709491
4cr9,98.14717533858934,366.63615580663344,894.9392158466778,919.8097665886776,919.8099540489408
3qqs,85.01485266978405,373.8861189462175,1033.164024291146,1065.881687022129,1065.8867331677661
2fxs,142.77781172557016,623.3471435054576,1532.4977883319577,1562.5918435480082,1562.5929801704233
2hb1,108.47849154312999,357.7377929545499,828.6607000503641,842.174932739862,842.1767317854708
2vkm,254.91630382014935,1144.244950418687,3028.5249451825657,3089.8080403960225,3089.8098637010926
1o0h,81.71906056349664,378.5041988653759,1135.6250677890564,1160.2423775689188,1160.2766609077373
1h23,65.3583415403772,472.4492286832108,1392.9977916062126,1417.1257507123867,1417.132903146023
3coy,100.18055450613267,522.5367763653924,1295.5404114914504,1315.1144148759647,1315.1204641835157
3uo4,211.09336558049236,911.2020992548315,2182.0212392685,2236.8860604889574,2236.8887445480987
2xii,89.33508086530632,466.54889149824504,1365.694736325636,1405.2943471538833,1405.294438679588
3prs,130.70725787546476,889.9568202654133,2592.6726629200775,2646.5514175639005,2646.5522837493013
3dd0,104.86440447836316,493.10598302662896,1144.2114284721677,1167.993926260362,1167.9939274751985
3k5v,65.38989664514293,396.98004853614054,1068.8623746224484,1098.7566667694762,1098.7571431399026
4jsz,42.45553031912679,186.57489773939142,577.1679057415884,598.1887716622747,598.1897316425997
1ydr,158.3637422955135,691.8644980048871,1445.5240046677568,1468.756740296116,1468.7567565894021
4k77,162.22741381055394,687.0848374265557,1483.0052444782928,1512.373016298842,1512.373026380415
2fvd,148.39435185449702,730.4786786332929,1835.0235830294891,1878.5249366347057,1878.525016277487
================================================
FILE: Validation_CASF2016/Score_decoys_docking_CASF2016.csv
================================================
PDB_ID,Cpd_Name,Score_3A,Score_5A,Score_7A,Score_10A,Score_all
4k18,4k18_100,41.7124709276148,355.00999393874434,1234.1988897433216,1265.8331969710105,1265.8335978696152
4k18,4k18_105,36.50107725111608,320.5826389497487,1098.7196528481893,1128.4331804224062,1128.4344001617458
4k18,4k18_107,56.13413997037191,406.6658214522255,1261.5476637591444,1298.835192687907,1298.835338794891
4k18,4k18_118,44.23784542273988,336.9721288402966,1252.596765731967,1287.006223774616,1287.0066010553867
4k18,4k18_122,42.72467596870927,342.7847157990617,1211.367931058725,1252.029400636168,1252.029755610901
4k18,4k18_144,38.96232069443992,317.0233589810817,1195.317428787065,1230.1148576211904,1230.1163322862917
4k18,4k18_151,55.36144488283484,414.94967873970757,1227.4936860001094,1258.1147073742657,1258.115083025271
4k18,4k18_161,27.722437628691942,289.6039259012111,1017.4632349689235,1044.457285777602,1044.4577867794249
4k18,4k18_174,55.48357922434606,407.2049941833978,1305.7115494414534,1336.286635389485,1336.2868450665324
4k18,4k18_200,36.82264842008218,319.5509372458288,1188.0183291622443,1227.749170755791,1227.7512684613735
4k18,4k18_201,54.54037468823392,387.3761923465572,1285.5658636737983,1322.7375758769015,1322.737753254302
4k18,4k18_203,44.358268973006425,340.31005064602186,1164.465584215605,1208.6159774629493,1208.616182962977
4k18,4k18_215,54.68841334770205,400.9374901464637,1242.0733526197612,1275.8713160630066,1275.8716345414553
4k18,4k18_223,49.66981079515448,353.50821606858364,1116.5043906256494,1161.092669902385,1161.0928886979389
4k18,4k18_225,62.8812438851792,402.7473872652022,1354.0213112918489,1396.1531772015064,1396.1533166216968
4k18,4k18_23,61.85263685558704,410.74395494559946,1307.589971651261,1339.8244226977617,1339.8245624272124
4k18,4k18_233,50.32340600821812,338.6069590173869,1247.4384174408628,1284.1991886751362,1284.1993328837125
4k18,4k18_249,58.24430904554004,372.56336896225804,1270.8696008146292,1299.9544035778006,1299.9544881941845
4k18,4k18_254,63.618413833940885,408.1897073448878,1353.700594506805,1387.5523070059185,1387.5524327266733
4k18,4k18_267,53.228435255971746,375.75792629256716,1301.188120675373,1349.149733877627,1349.149968014605
4k18,4k18_282,48.4201894390463,382.2017071118331,1275.2868337194332,1315.1983727394856,1315.1986057549498
4k18,4k18_285,58.011601536769795,388.40472684061024,1343.4359593025777,1384.8266578345751,1384.826827319037
4k18,4k18_287,42.395932785930555,351.0311804296627,1213.3978059575388,1263.4269858915789,1263.428661631846
4k18,4k18_365,44.95053494515667,344.13376713090776,1136.3630823189344,1164.1386450032992,1164.1421378686384
4k18,4k18_38,55.70823074817767,408.05165069979864,1324.1401468661966,1364.3375140834557,1364.337729652478
4k18,4k18_408,31.436395330620563,328.5704765760239,980.3375192914201,1012.5148772240432,1012.5151875703648
4k18,4k18_415,23.931027122767695,257.6291066262192,1064.4572818267418,1096.5238544881888,1096.5239139979967
4k18,4k18_430,18.88957309942349,249.57278493104855,1047.4251339715036,1085.405770563039,1085.4107369833057
4k18,4k18_437,25.7570139814
gitextract_irimushr/ ├── .gitmodules ├── Dockerfile ├── LICENSE ├── README.md ├── Trained_models/ │ ├── DeepDock_pdbbindv2019_13K_loss.csv │ └── DeepDock_pdbbindv2019_13K_minTestLoss.chk ├── Validation_CASF2016/ │ ├── CASF2016_DockingPower_DeepDock.ipynb │ ├── CASF2016_ScoringPower_DeepDock.ipynb │ ├── CASF2016_ScreeningPower_DeepDock.ipynb │ ├── DockingPower_DeepDock_10A/ │ │ └── DockingPower_DeepDock_10A.out │ ├── DockingPower_DeepDock_3A/ │ │ └── DockingPower_DeepDock_3A.out │ ├── DockingPower_DeepDock_5A/ │ │ └── DockingPower_DeepDock_5A.out │ ├── DockingPower_DeepDock_7A/ │ │ └── DockingPower_DeepDock_7A.out │ ├── DockingPower_DeepDock_all/ │ │ └── DockingPower_DeepDock_all.out │ ├── Score_CoreSet_docking_CASF2016.csv │ ├── Score_decoys_docking_CASF2016.csv │ ├── ScoringPower_Deepdock/ │ │ ├── RankingPower_Deepdock_10A.out │ │ ├── RankingPower_Deepdock_3A.out │ │ ├── RankingPower_Deepdock_5A.out │ │ ├── RankingPower_Deepdock_7A.out │ │ ├── RankingPower_Deepdock_all.out │ │ ├── ScoringPower_Deepdock_10A.out │ │ ├── ScoringPower_Deepdock_3A.out │ │ ├── ScoringPower_Deepdock_5A.out │ │ ├── ScoringPower_Deepdock_7A.out │ │ └── ScoringPower_Deepdock_all.out │ ├── ScreeningPower_DeepDock_10A/ │ │ ├── ForwardScreeningPower_DeepDock_10A.out │ │ └── ReverseScreeningPower_DeepDock_10A.out │ ├── ScreeningPower_DeepDock_3A/ │ │ ├── ForwardScreeningPower_DeepDock_3A.out │ │ └── ReverseScreeningPower_DeepDock_3A.out │ ├── ScreeningPower_DeepDock_5A/ │ │ ├── ForwardScreeningPower_DeepDock_5A.out │ │ └── ReverseScreeningPower_DeepDock_5A.out │ ├── ScreeningPower_DeepDock_7A/ │ │ ├── ForwardScreeningPower_DeepDock_7A.out │ │ └── ReverseScreeningPower_DeepDock_7A.out │ └── ScreeningPower_DeepDock_all/ │ ├── ForwardScreeningPower_DeepDock_all.out │ └── ReverseScreeningPower_DeepDock_all.out ├── Validation_Docking/ │ ├── DockingResults_CASF2016_CoreSet.chk │ ├── DockingResults_CASF2016_CoreSet.csv │ ├── DockingResults_TestSet.chk │ ├── DockingResults_TestSet.csv │ ├── Docking_CASF2016_CoreSet.ipynb │ └── Docking_TestSet.ipynb ├── data/ │ ├── 1z6e_ligand.mol2 │ ├── 1z6e_protein.pdb │ ├── 1z6e_protein.ply │ ├── 2br1_ligand.mol2 │ ├── 2br1_protein.pdb │ ├── 2br1_protein.ply │ ├── 2wtv_ligand.mol2 │ ├── 2wtv_protein.pdb │ ├── 2wtv_protein.ply │ ├── 2yge_ligand.mol2 │ ├── 2yge_protein.pdb │ ├── 2yge_protein.ply │ ├── 4f2w_ligand.mol2 │ ├── 4f2w_protein.pdb │ ├── 4f2w_protein.ply │ ├── 4ivd_ligand.mol2 │ ├── 4ivd_protein.pdb │ ├── 4ivd_protein.ply │ ├── 4twp_ligand.mol2 │ ├── 4twp_protein.pdb │ ├── get_CASF_2016.sh │ └── get_deepdock_data.sh ├── deepdock/ │ ├── DockingFunction.py │ ├── __init__.py │ ├── models.py │ ├── prepare_target/ │ │ ├── __init_.py │ │ ├── computeAPBS.py │ │ ├── computeCharges.py │ │ ├── computeHydrophobicity.py │ │ ├── computeMSMS.py │ │ ├── computeTargetMesh.py │ │ ├── compute_normal.py │ │ ├── fixmesh.py │ │ └── save_ply.py │ └── utils/ │ ├── __init__.py │ ├── data.py │ ├── distributions.py │ └── mol2graph.py ├── examples/ │ ├── Docking_example.ipynb │ ├── Score_example.ipynb │ └── Train_DeepDock.ipynb ├── images/ │ └── Fig1.tiff ├── requirements.txt └── setup.py
SYMBOL INDEX (89 symbols across 13 files)
FILE: deepdock/DockingFunction.py
function score_compound (line 15) | def score_compound(ligand, target, model, dist_threshold=3., seed=None, ...
function dock_compound (line 56) | def dock_compound(mol, target_ply, model, dist_threshold=3., popsize=150...
class optimze_conformation (line 119) | class optimze_conformation():
method __init__ (line 120) | def __init__(self, mol, target_coords, n_particles, pi, mu, sigma, sav...
method score_conformation (line 138) | def score_conformation(self, values):
function SetDihedral (line 178) | def SetDihedral(conf, atom_idx, new_vale):
function GetDihedral (line 181) | def GetDihedral(conf, atom_idx):
function GetTransformationMatrix (line 184) | def GetTransformationMatrix(transformations):
function compute_euclidean_distances_matrix (line 192) | def compute_euclidean_distances_matrix(X, Y):
function calculate_probablity (line 200) | def calculate_probablity(pi, sigma, mu, y):
function apply_changes (line 208) | def apply_changes(mol, values, rotable_bonds):
function get_torsions (line 219) | def get_torsions(mol_list):
function get_random_conformation (line 261) | def get_random_conformation(mol, rotable_bonds=None, seed=None):
function atom_scores (line 280) | def atom_scores(ligand, target, probabilities, batch):
function calculate_atom_contribution (line 289) | def calculate_atom_contribution(ligand, target, model, dist_threshold=3....
FILE: deepdock/models.py
function compute_cluster_batch_index (line 13) | def compute_cluster_batch_index(cluster, batch):
class NodeSampling (line 21) | class NodeSampling(nn.Module):
method __init__ (line 22) | def __init__(self, nodes_per_graph):
method forward (line 27) | def forward(self, x):
class ResBlock (line 45) | class ResBlock(nn.Module):
method __init__ (line 46) | def __init__(self, in_channels, dropout_rate=0.15):
method forward (line 64) | def forward(self, data):
class EdgeModel (line 80) | class EdgeModel(torch.nn.Module):
method __init__ (line 81) | def __init__(self, in_channels):
method forward (line 85) | def forward(self, src, dest, edge_attr, u, batch):
class NodeModel (line 94) | class NodeModel(torch.nn.Module):
method __init__ (line 95) | def __init__(self, in_channels):
method forward (line 100) | def forward(self, x, edge_index, edge_attr, u, batch):
class TargetNet (line 114) | class TargetNet(nn.Module):
method __init__ (line 115) | def __init__(self, in_channels, edge_features=3, hidden_dim=128, resid...
method forward (line 126) | def forward(self, data):
class LigandNet (line 140) | class LigandNet(nn.Module):
method __init__ (line 141) | def __init__(self, in_channels, edge_features=6, hidden_dim=128, resid...
method forward (line 152) | def forward(self, data):
class DeepDock (line 163) | class DeepDock(nn.Module):
method __init__ (line 164) | def __init__(self, ligand_model, target_model, hidden_dim, n_gaussians...
method forward (line 185) | def forward(self, data_ligand, data_target, y=None):
method compute_euclidean_distances_matrix (line 226) | def compute_euclidean_distances_matrix(self, X, Y):
function mdn_loss_fn (line 236) | def mdn_loss_fn(pi, sigma, mu, y):
FILE: deepdock/prepare_target/computeAPBS.py
function computeAPBS (line 16) | def computeAPBS(vertices, pdb_file, tmp_file_base):
FILE: deepdock/prepare_target/computeCharges.py
function computeCharges (line 33) | def computeCharges(pdb_filename, vertices, names):
function computeChargeHelper (line 73) | def computeChargeHelper(atom_name, res, v):
function computeAngleDeviation (line 108) | def computeAngleDeviation(a, b, c, theta):
function computePlaneDeviation (line 113) | def computePlaneDeviation(a, b, c, d):
function computeAnglePenalty (line 121) | def computeAnglePenalty(angle_deviation):
function isPolarHydrogen (line 126) | def isPolarHydrogen(atom_name, res):
function isAcceptorAtom (line 133) | def isAcceptorAtom(atom_name, res):
function computeSatisfied_CO_HN (line 146) | def computeSatisfied_CO_HN(atoms):
function assignChargesToNewMesh (line 188) | def assignChargesToNewMesh(new_vertices, old_vertices, old_charges, seed...
FILE: deepdock/prepare_target/computeHydrophobicity.py
function computeHydrophobicity (line 33) | def computeHydrophobicity(names):
FILE: deepdock/prepare_target/computeMSMS.py
function computeMSMS (line 19) | def computeMSMS(pdb_file, protonate=True, one_cavity=None):
FILE: deepdock/prepare_target/computeTargetMesh.py
function compute_inp_surface (line 29) | def compute_inp_surface(target_filename, ligand_filename, dist_threshold...
FILE: deepdock/prepare_target/compute_normal.py
function compute_normal (line 19) | def compute_normal(vertex, face):
function crossp (line 71) | def crossp(x, y):
FILE: deepdock/prepare_target/fixmesh.py
function fix_mesh (line 16) | def fix_mesh(mesh, resolution, detail="normal"):
FILE: deepdock/prepare_target/save_ply.py
function save_ply (line 16) | def save_ply(
FILE: deepdock/utils/data.py
function read_ply (line 18) | def read_ply(path):
class PDBbind_protsurf_dataset (line 37) | class PDBbind_protsurf_dataset(Dataset):
method __init__ (line 38) | def __init__(self, pdb_IDs, root, transform=None, pre_transform=None):
method len (line 43) | def len(self):
method get (line 46) | def get(self, idx):
class PDBbind_complex_dataset (line 53) | class PDBbind_complex_dataset(Dataset):
method __init__ (line 54) | def __init__(self, data_path, transform=None, pre_transform=None,
method len (line 70) | def len(self):
method get (line 73) | def get(self, idx):
function compute_clusters (line 77) | def compute_clusters(data, n_clusters):
function compute_cluster_batch_index (line 90) | def compute_cluster_batch_index(cluster, batch):
function Mol2MolSupplier (line 98) | def Mol2MolSupplier (file=None, sanitize=True, cleanupSubstructures=True):
FILE: deepdock/utils/distributions.py
class MixtureSameFamily (line 9) | class MixtureSameFamily(Distribution):
method __init__ (line 48) | def __init__(self,
method expand (line 87) | def expand(self, batch_shape, _instance=None):
method support (line 105) | def support(self):
method mixture_distribution (line 111) | def mixture_distribution(self):
method component_distribution (line 115) | def component_distribution(self):
method mean (line 119) | def mean(self):
method variance (line 125) | def variance(self):
method cdf (line 135) | def cdf(self, x):
method log_prob (line 142) | def log_prob(self, x):
method sample (line 149) | def sample(self, sample_shape=torch.Size()):
method _pad (line 172) | def _pad(self, x):
method _pad_mixture_dimensions (line 175) | def _pad_mixture_dimensions(self, x):
method __repr__ (line 185) | def __repr__(self):
FILE: deepdock/utils/mol2graph.py
function oneHotVector (line 14) | def oneHotVector(val, lst):
function mol_to_nx (line 20) | def mol_to_nx(mol):
function get_bonds (line 59) | def get_bonds(mol_list, bidirectional=True):
function get_angles (line 75) | def get_angles(mol_list, bidirectional=True):
function get_torsions (line 97) | def get_torsions(mol_list, bidirectional=True):
function mol_with_atom_index (line 138) | def mol_with_atom_index( mol ):
function atomenvironments (line 144) | def atomenvironments(mol, radius=3):
Condensed preview — 86 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (9,114K chars).
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"chars": 3489,
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"path": "deepdock/prepare_target/computeHydrophobicity.py",
"chars": 815,
"preview": "import numpy as np\n\n\"\"\"\nTaken from: \ncomputeHydrophobicity.py - MaSIF\nPablo Gainza - LPDI STI EPFL 2019\n\"\"\"\n\n# Kyte Dool"
},
{
"path": "deepdock/prepare_target/computeMSMS.py",
"chars": 1755,
"preview": "import os\nfrom subprocess import Popen, PIPE\n\nfrom input_output.read_msms import read_msms\nfrom triangulation.xyzrn impo"
},
{
"path": "deepdock/prepare_target/computeTargetMesh.py",
"chars": 9146,
"preview": "import os\nimport sys\nimport numpy as np\nimport shutil\nimport pymesh\nimport Bio.PDB\nfrom Bio.PDB import * \nfrom rdkit imp"
},
{
"path": "deepdock/prepare_target/compute_normal.py",
"chars": 2219,
"preview": "import numpy as np\nfrom numpy.matlib import repmat\n\"\"\"\ncompute_normal.py: Compute the normals of a closed shape.\nPablo G"
},
{
"path": "deepdock/prepare_target/fixmesh.py",
"chars": 2361,
"preview": "import numpy as np\nfrom numpy.linalg import norm\nimport pymesh\n\n\"\"\"\nModified from: \nfixmesh.py - MaSIF\nPablo Gainza - LP"
},
{
"path": "deepdock/prepare_target/save_ply.py",
"chars": 1858,
"preview": "import pymesh\nimport numpy\n\"\"\"\nModified from: \nsave_ply.py - MaSIF\nPablo Gainza - LPDI STI EPFL 2019\n\"\"\"\n\n\"\"\"\nread_ply.p"
},
{
"path": "deepdock/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "deepdock/utils/data.py",
"chars": 4180,
"preview": "import torch\nfrom plyfile import PlyData\nfrom torch_geometric.data import Data, Dataset\nfrom torch_geometric.utils impor"
},
{
"path": "deepdock/utils/distributions.py",
"chars": 8607,
"preview": "import torch\nfrom torch.distributions.distribution import Distribution\nfrom torch.distributions import Categorical\nfrom "
},
{
"path": "deepdock/utils/mol2graph.py",
"chars": 6443,
"preview": "import numpy as np\nfrom rdkit.Chem import AllChem, Draw, Descriptors, rdMolTransforms\nimport rdkit.Chem as Chem\nimport r"
},
{
"path": "examples/Docking_example.ipynb",
"chars": 909884,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# DeepDock example\"\n ]\n },\n {\n "
},
{
"path": "examples/Score_example.ipynb",
"chars": 913321,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Score compounds usign DeepDock\"\n "
},
{
"path": "examples/Train_DeepDock.ipynb",
"chars": 42508,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\":"
},
{
"path": "requirements.txt",
"chars": 479,
"preview": "torch==1.4.0\n-f https://pytorch-geometric.com/whl/torch-1.4.0.html\ntorch-scatter==2.0.4+cu101\n-f https://pytorch-geometr"
},
{
"path": "setup.py",
"chars": 366,
"preview": "from setuptools import setup, find_packages\n\nsetup(\n name='deepdock',\n version='1.0',\n author='Oscar Mendez-Luc"
}
]
// ... and 4 more files (download for full content)
About this extraction
This page contains the full source code of the OptiMaL-PSE-Lab/DeepDock GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 86 files (23.2 MB), approximately 2.2M tokens, and a symbol index with 89 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.