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AEV-PLIG

AEV-PLIG is a GNN-based scoring function that predicts the binding affinity of a bound protein-ligand complex given its 3D structure.

Installation guide

AEV-PLIG has been tested on the following systems:

  • macOS: Monterey (12.5.1)

Create conda environment

Installation times may vary, but took around 30 seconds on Mac M1. For macOS:

conda env create --file aev-plig-mac.yml

For Linux:

conda env create --file aev-plig-linux.yml

Install packages manually:

conda create --name aev-plig python=3.8
conda activate aev-plig
pip install torch torchvision torchaudio
pip install torch-scatter
pip install torch_geometric
pip install rdkit
pip install torchani
pip install qcelemental
pip install pandas

Demo

This section demonstrates how to train your own AEV-PLIG model, and how to use AEV-PLIG to make predictions.

The computational requirements for each script are included, and unless otherwise specified, the hardware used is a Mac M1 CPU.

Training

Download training data

Download the training datasets PDBbind and BindingNet

wget http://pdbbind.org.cn/download/PDBbind_v2020_other_PL.tar.gz
wget http://pdbbind.org.cn/download/PDBbind_v2020_refined.tar.gz
wget http://bindingnet.huanglab.org.cn/api/api/download/binding_database

Put PDBbind data into data/pdbbind/refined-set and data/pdbbind/general-set

Put BindingNet data into data/bindingnet/from_chembl_client

Generate PDBbind and BindingNet graphs

The following scripts will generate graphs into pdbbind.pickle and bindingnet.pickle. Takes around 30 minute in total to run.

python generate_pdbbind_graphs.py
python generate_bindingnet_graphs.py

Generate data for pytorch

Running this script takes around 2 minutes.

python create_pytorch_data.py

The script outputs the following files in data/processed/:

pdbbind_U_bindingnet_ligsim90_train.pt, pdbbind_U_bindingnet_ligsim90_valid.pt, and pdbbind_U_bindingnet_ligsim90_test.pt

Run training

Running the following script takes 25 hours using a NVIDIA GeForce GTX 1080 Ti GPU. Once a model has been trained, the next section describes how to use it for predictions.

python training.py --activation_function=leaky_relu --batch_size=128 --dataset=pdbbind_U_bindingnet_ligsim90 --epochs=200 --head=3 --hidden_dim=256 --lr=0.00012291937615434127 --model=GATv2Net

The trained models are saved in output/trained_models

Predictions

In order to make predictions, the model requires a .csv file with the following columns:

  • unique_id, unique identifier for the datapoint
  • pK, binding affinity label in pk units
  • sdf_file, relative path to the ligand .sdf file
  • pdb_file, relative path to the protein .pdb file

An example dataset is included in data/example_dataset.csv for this demo.

python process_and_predict.py --dataset_csv=data/example_dataset.csv --data_name=example --trained_model_name=20231116-181233_model_GATv2Net_pdbbind_core

The script processes data in dataset_csv, and removes datapoints if:

  1. .sdf file cannot be read by RDkit
  2. Molecule contains rare element
  3. Molecule has undefined bond type

The script then creates graphs and pytorch data to run the AEV-PLIG model specified with trained_model_name. The default is AEV-PLIG trained on PDBbind v2020 but we recommend using AEV-PLIG trained with PDBbind v2020 and BindingNet.

The predictions are saved under output/predictions/data_name_predictions.csv

For the example dataset, the script takes around 20 seconds to run