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ECloudGen

This figure represent: Electron Clouds; Protein-Ligand Interactions; Latent Diffusion Process

Environment

Install via conda yaml file (cuda 11.3)

conda env create -f ecloudgen.yml
conda activate ecloudgen 

Install manually

This environment has been successfully tested on CUDA==11.3

conda create -n ecloudgen rdkit openbabel moleculekit scikit-learn scipy jupyter python-lmdb pytorch cudatoolkit=11.3 omegaconf einops accelerate biopython h5py wandb xtb ignite gpytorch altair python=3.9 -c conda-forge 

Data

Protein-ligand pair dataset preparation.

You can download the raw data as provided in ResGen. You can also download the processed protein-ligand pair from the this link.

Note: index.pkl, split_by_name.pt. are automatically downloaded with the SurfGen code. index.pkl saves the information of each protein-ligand pair, while split_by_name.pt save the train-test split of the dataset.

tar -xzvf crossdocked_pocket10.tar.gz
# Then follow the ./dataset/readme.md for processing protein-ligand dataset from scratch. 

Generation

# modify the data path and batch_size in the ./configs/eclouddiff.yml 
python generate_from_pdb.py --pdb_file pdb_file ./play_around/peptide_example/7ux5_protein.pdb --lig_file ./play_around/peptide_example/7ux5_peptide.sdf --outputs_dir results

Training

The training process is released as train.py, the following command is an example of how to train a model.

# prepare a demo data
python ./datasets/generate_pktlig_data.py
# modify the data path and batch_size in the ./configs/eclouddiff.yml 
python train_eclouddiff.py