Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Pocket2Mol used equivariant graph neural networks to improve efficiency and molecule quality of previous structure-based drug design model.

Installation

Update: Now the codes are compatible with PyTorch Geometric (PyG) >= 2.0.

Dependency

The codes have been tested in the following environment:

Package	Version
Python	3.8.12
PyTorch	1.10.1
CUDA	11.3.1
PyTorch Geometric	2.0.0
RDKit	2022.03
BioPython	1.79

Install via conda yaml file (cuda 11.3)

conda env create -f env_cuda113.yml
conda activate Pocket2Mol

Install manually

conda create -n Pocket2Mol python=3.8
conda activate Pocket2Mol

# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg

# Install other tools
conda install -c conda-forge rdkit
conda install biopython -c conda-forge # used only in sample_for_pdb.py
conda install pyyaml easydict python-lmdb -c conda-forge

# Install tensorboard only for training
conda install tensorboard -c conda-forge  

Datasets

Please refer to README.md in the data folder.

Sampling

NOTE: It is highly recommended to add taskset -c to use only one cpu when sampling (e.g. taskset -c 0 python sample_xxx.py to use CPU 0), which is much faster. The reason is not clear yet.

Sampling for pockets in the testset

To sample molecules for the i-th pocket in the testset, please first download the trained models following README.md in the ckpt folder. Then, run the following command:

python sample.py --data_id {i} --outdir ./outputs  # Replace {i} with the index of the data. i should be between 0 and 99 for the testset.

We recommend to specify the GPU device number and restrict the cpu cores using command like:

CUDA_VISIBLE_DIVICES=0  taskset -c 0 python sample.py --data_id 0 --outdir ./outputs

We also provide a bash file batch_sample.sh for sampling molecules for the whole test set in parallel. For example, to sample with three workers, run the following commands in three panes.

CUDA_VISIBLE_DEVICES=0 taskset -c 0 bash batch_sample.sh  3 0 0

CUDA_VISIBLE_DEVICES=0 taskset -c 1 bash batch_sample.sh  3 1 0

CUDA_VISIBLE_DEVICES=0 taskset -c 2 bash batch_sample.sh  3 2 0

The three parameters of batch_sample.py represent the number of workers, the index of current worker and the start index of the datapoint in the test set, respectively.

NOTE: We find it much faster to use only one CPU for one sampling program (i.e., set taskset -c to use one CPU).

Sampling for PDB pockets

To generate ligands for your own pocket, you need to provide the PDB structure file of the protein, the center coordinate of the pocket bounding box, and optionally the side length of the bounding box (default: 23Å). Note that there is a blank before the first value of the center parameter. The blank cannot be omitted if the first value is negative (e.g., --center " -1.5,28.0,36.0").

Example:

python sample_for_pdb.py \
      --pdb_path ./example/4yhj.pdb
      --center " 32.0,28.0,36.0"

Training

python train.py --config ./configs/train.yml --logdir ./logs

For training, we recommend to install apex for lower gpu memory usage. If so, change the value of train/use_apex in the configs/train.yml file.

Citation

@inproceedings{peng2022pocket2mol,
  title={Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets},
  author={Xingang Peng and Shitong Luo and Jiaqi Guan and Qi Xie and Jian Peng and Jianzhu Ma},
  booktitle={International Conference on Machine Learning},
  year={2022}
}

Contact

Xingang Peng ([email protected])