graphLambda: Binding Affinity Prediction Using Graph Neural Networks.

This repo contains is about graphLambda, a deep learning model to score the binding affinity of protein-ligand complexes in PyTorch and PyTorch Geometric, developed by Ghaith Mqawass and Petr Popov.

Overview

We provide the implementation of the graphLambda model in Pytorch and PyTorch Geometric frameworks, along with the scripts that can be used to train the model and also replicate the results. All data used in this work can be downloaded from Zenodo. The repository is orignaized as follows:

• models contains:

  • various GNN models implemented in Pytorch. All possible combinations of (GCN,GAT,GIN) are provided.
  • graphLambda.py : The overall model implemented in Pytorch.

• Data contains:

  • Dataset.py : Dataset class that combines pre-computed BPS features. This dataset is to be passed to the dataloader to train the model.
  • data.txt : Description of the used data and benchmark. Also links to download the data are provided.
  • refined_data2020.csv: A csv file that contains PDB codes of protein-ligand complexes with the expiremental binding affinity.
  • QSAR_set1.csv , QSAR_set1.csv and coreset2016.csv : csv files of used benchmarks containing PDB codes of protein-ligand complexes with the expiremental binding affinity.

• BPS_features.py : A python script that computes BPS features.

Prepare the environment:

$ conda env create -f environment.yml
$ source activate myenv
$ conda env list

Training the model:

• Download the refined set from the official website of PDBBind
• Compute BPS features using BPS_features.py (Or you can download pre-calculated features along with train/validation ids from Zenodo
• In the directory "refined_set" place the downloaded "refined_set.h5" file and then run the notebook graphLambda_train.ipynb

Using the model:

• The final models can be downloaded from Zenodo: Zenodo
• To replicate the results you need to:
  • Download the test set "coreset" from Zenodo: Zenodo and place it in the same directory of the notebook. The downloaded folder contains the coreset from PDBbind with BPS features precomputed using BPS_features.py and stored *.h5 file . You need to load paths to the work directory and *.h5 file and coreset2016.csv file in the notebook graphLambda_train. Some preprcessing was already carried out to the original data :
  • Preprocessed the PDB samples by removing water molecules.
  • Generated bps_features.h5 file usingBPS_features.py. For inference you can use the script graphLambda_inference.py. An example of how to use it in inference is provided in the CACHE_Challenge directory.