AEV-PLIG is a GNN-based scoring function that predicts the binding affinity of a bound protein-ligand complex given its 3D structure.
AEV-PLIG has been tested on the following systems:
- macOS: Monterey (12.5.1)
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
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.
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
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
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
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
In order to make predictions, the model requires a .csv file with the following columns:
- unique_id, unique identifier for the datapoint
- 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:
- .sdf file cannot be read by RDkit
- Molecule contains rare element
- 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