SeFMol

The official repository of our paper "Steering Semi-flexible Molecular Diffusion Model for Structure-Based Drug Design with Reinforcement Learning"

Prerequisites

We have presented the conda environment file in ./environment.yml.

Install via Conda and Pip

conda create -n SeFMol python=3.9
conda activate SeFMol
conda install pytorch==1.13.1  pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c conda-forge pdbfixer
conda install conda-forge::openbabel

pip isntall protobuf==5.27.1
pip install networkx==3.2.1
pip install rdkit==2023.9.6
pip install biopython==1.83

Data

The data used for training / evaluating the model are organized in the data Google Drive folder.

To train the model from scratch, you need to download the preprocessed lmdb file and split file:

• crossdocked_v1.1_rmsd1.0_pocket10_processed_final.lmdb
• crossdocked_pocket10_pose_split.pt

To evaluate the model on the test set, you need to download and unzip the test_set.zip. It includes the original PDB files that will be used in Vina Docking.

Training

Ridid pre-training:

python train_rigid_pt.py  

Ridid finetuing:

python train_rigid_ft.py

Semi-flexible training:

python train_sfrl.py

Sampling

python sample.py --config configs/rl.yml --start_index 0  --end_index 99 

Evaluation

Evaluation from sampling results

python eval_split_diff.py