patjiang / gaabind-test

GAABind

Code and trained model for our paper GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction

Installation

You can set up the environment using Anaconda or Docker.

Using Anaconda:

git clone https://github.com/Mercuryhs/GAABind.git
cd GAABind
conda env create -f environment.yml
conda activate gaabind

Using Docker image:

git clone https://github.com/Mercuryhs/GAABind.git
docker pull mercuryyy/gaabind:latest
docker run --shm-size 8G --gpus all -it -v $PWD/GAABind:/GAABind gaabind:latest /bin/bash

Build Docker image from scratch:

git clone https://github.com/Mercuryhs/GAABind.git
cd GAABind
docker build -t gaabind:latest .
docker run --shm-size 8G --gpus all -it -v $PWD:/GAABind gaabind:latest /bin/bash

Dataset

The processed dataset can be downloaded from zenodo. If you want to train GAABind from scratch, or reproduce the GAABind results, you can:

1. download dataset from zenodo;
2. unzip the zip file and place it into data_path dataset.

Running GAABind on your own dataset

We provided both jupyter notebook tutorials and python scripts for running GAABind on your own dataset.

1. Usage examples can be seen directly in the jupyter notebooks included in the repo:

GAABind_example.ipynb

2. Predict your own molecules with the trained model in batch mode:

Step 1: Preprocess the dataset using the following format:

example_data/
├── Mpro-x11271
│   ├── ligand.txt
│   ├── pocket.txt
│   └── receptor.pdb
└── Mpro-x12715
    ├── ligand.txt
    ├── pocket.txt
    └── receptor.pdb

The ligand file can be in .sdf, .mol2, or .mol format, or you can provide the ligand's SMILES representation in a .txt file. The pocket.txt contains the residues of binding pockets, each residue is named by the chain, residue number, and three-letter abbreviation as follows:

A_166_GLU
A_49_MET
A_143_GLY
...

Run the following command to preprocess the dataset

python preprocess.py --input_path [raw_path] --output_path [processed_path]

Step 2: Predict the dataset using the following command

python predict.py --input_path [processed_path] --output_path [predict_path] 

Then you can find the prediction results in the predict_path.

Reproducing paper numbers

Download and unzip the dataset, then place it into the dataset directory.

Running the following command to reproduce the evaluation result on CASF2016:

python inference.py --input_path dataset/PDBBind/processed --complex_list dataset/PDBBind/test.txt --output_path casf2016_predict_result --batch_size 3

Running the following command to reproduce the evaluation result on Mpro dataset:

python inference.py --input_path dataset/Mpro/processed --complex_list dataset/Mpro/test.txt --output_path mpro_predict_result --batch_size 2

Training a model yourself

You can also retraining the model yourself by using the following command:

OMP_NUM_THREADS=8 CUDA_VISIBLE_DEVICES='0,1' torchrun --nproc_per_node=2 train.py --data_dir dataset/PDBBind

Reference

Under Review