Official PyTorch-based implementation of Paper "An Image-based Protein-Ligand Binding Representation Learning Framework via Multi-Level Flexible Dynamics Trajectory Pre-training".
[2024/06/28] Repository installation completed.
CUDA 11.6
Ubuntu 18.04
# create conda env
conda create -n ImagePLB python=3.9
conda activate ImagePLB
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
# install environment
pip install rdkit
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install biopython==1.79
pip install easydict
pip install tqdm
pip install timm==0.6.12
pip install tensorboard
pip install scikit-learn
pip install setuptools==59.5.0
pip install pandas
pip install torch-cluster torch-scatter torch-sparse torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.13.1%2Bcu116.html
pip install torch-geometric==1.6.0
pip install dgl-cu116
pip install ogb
pip install seaborn
conda install openbabel -c conda-forge
pip install einopsWe use PyMOL to genearte multi-view ligand images from molecular conformations. Here is the PyMOL script to get the multi-view ligand images, you can run it in the PyMOL command:
Click here for the code!
sdf_filepath=demo.sdf # sdf file path of ligand
rotate_direction=x
rotate=0
save_img_path=demo_frame.png
load $sdf_filepath;bg_color white;set stick_ball,on;set stick_ball_ratio,3.5;set stick_radius,0.15;set sphere_scale,0.2;set valence,1;set valence_mode,0;set valence_size, 0.1;rotate $rotate_direction, $rotate;save $save_img_path;quit;Note that we used 4 views by setting the following parameters:
- rotate_direction=x; rotate=0
- rotate_direction=x; rotate=180
- rotate_direction=y; rotate=180
- rotate_direction=z; rotate=180
Of course, to save you time on data preprocessing, we also provide download links for all data for your free access.
| Name | Download link | Description |
|---|---|---|
| multi_view_trajectory_video.tar.gz | BaiduCloud | ligand trajectory with multi-view images. |
| pocket.tar.gz | OneDrive | pocket trajectory with 3D graphs. |
Please download all data listed above and put it in datasets/pre-training/MISATO/processed/ if you want to pre-train ImagePLB from scratch.
The directory is organized in the following format:
datasets/pre-training/MISATO/processed/
+---pocket
| | train.npz
| |
+---multi_view_trajectory_video
| +---1A0Q
| | +---x_0
| | | mov0001.png
| | | mov0002.png
| | | ...
| | +---x_180
| | | mov0001.png
| | | mov0002.png
| | | ...
| | +---y_180
| | | mov0001.png
| | | mov0002.png
| | | ...
| | +---z_180
| | | mov0001.png
| | | mov0002.png
| | | ...
The pre-trained ImagePLB (ImagePLB-P) can be accessed in following table.
| Name | Download link | Description |
|---|---|---|
| ImagePLB-P.pth | OneDrive | You can download the ImagePLB-P and put it in the directory: resumes/. |
If you want to pre-train your own ImagePLB-P, see the command below.
Usage:
usage: pretrain_ImagePLB.py [-h] [--dataroot DATAROOT] [--workers WORKERS]
[--model_name MODEL_NAME]
[--max_len_pocket MAX_LEN_POCKET] [--center]
[--n_dim_graph N_DIM_GRAPH] [--lr LR]
[--momentum MOMENTUM]
[--weight-decay WEIGHT_DECAY] [--weighted_loss]
[--runseed RUNSEED] [--start_epoch START_EPOCH]
[--epochs EPOCHS] [--batch BATCH]
[--imageSize IMAGESIZE] [--resume RESUME]
[--n_ckpt_save N_CKPT_SAVE]
[--n_batch_step_optim N_BATCH_STEP_OPTIM]
[--lambda_next_mol LAMBDA_NEXT_MOL]
[--lambda_next_pocket LAMBDA_NEXT_POCKET]
[--lambda_next_complex LAMBDA_NEXT_COMPLEX]
[--log_dir LOG_DIR] [--tb_step_num TB_STEP_NUM]run command in pretrain folder to pre-train ImagePLB:
CUDA_VISIBLE_DEVICES=0,1,2,3 python pretrain_ImagePLB.py \
--workers 16 \
--batch 128 \
--epochs 30 \
--lr 0.001 \
--dataroot ../datasets/pre-training/MISATO/processed \
--log_dir ./experiments/pretrain_ImagePLB \
--weighted_lossAll downstream task data is publicly accessible below:
| Datasets | Links | Description |
|---|---|---|
| PDBBind | OneDrive | Including PDBBind-30, PDBBind-60, PDBBind-Scaffold. |
| LEP | OneDrive | Dataset of ligand efficacy prediction. |
datasets/fine-tuning/
+---pdbbind
| +---ligand
| | +---1a4k
| | | x_0.png
| | | x_180.png
| | | y_180.png
| | | z_180.png
| +---30
| | | train.npz
| | | valid.npz
| | | test.npz
| +---60
| | | train.npz
| | | valid.npz
| | | test.npz
| +---scaffold
| | | train.npz
| | | valid.npz
| | | test.npz
+---lep
| +---ligand
| | +---Lig2__6BQG__6BQH
| | | x_0.png
| | | x_180.png
| | | y_180.png
| | | z_180.png
| +---protein
| | | train.npz
| | | val.npz
| | | test.npz
- run command in finetune folder for PDBBind:
python pdbbind.py \
--batch 32 \
--epochs 20 \
--lr 0.0001 \
--egnn_dropout 0.3 \
--predictor_dropout 0.3 \
--dataroot ../datasets/fine-tuning/pdbbind \
--split_type scaffold \
--resume ../resumes/ImagePLB-P.pth \
--log_dir ./experiments/pdbbind/scaffold/rs0/ \
--runseed 0 \
--dist-url tcp://127.0.0.1:12312- run command in finetune folder for LEP：
python lep.py \
--batch 32 \
--epochs 100 \
--lr 0.0001 \
--dataroot ../datasets/fine-tuning/lep \
--split_type protein \
--egnn_dropout 0.5 \
--predictor_dropout 0.5 \
--resume ../resumes/ImagePLB-P.pth \
--log_dir ./experiments/lep/rs0/ \
--runseed 0 \
--dist-url tcp://127.0.0.1:12345We provide detailed training logs and corresponding checkpoints, you can easily see more training details from the logs and directly use our trained models for structure-based virtual screening.
| Name | Download link | Description |
|---|---|---|
| PDBBind-30 | OneDrive | The training details of ImagePLB-P on PDBBind-30 |
| PDBBind-60 | OneDrive | The training details of ImagePLB-P on PDBBind-60 |
| PDBBind-Scaffold | OneDrive | The training details of ImagePLB-P on PDBBind-Scaffold |
| LEP | OneDrive | The training details of ImagePLB-P on LEP |
The files include training logs and checkpoints for training ImagePLB-P with three random seeds (0, 1, 2).
If our paper or code is helpful to you, please do not hesitate to point a star for our repository and cite the following content.