xianyuco/SS-GNN

SS-GNN

This is a Pytorch implementation of SS-GNN, a simple-structured GNN model for drug-target binding affinity (DTBA) prediction as described in the following paper:

The SS-GNN defines the prediction of DTBA as a regression task, in which the model’s input is the drug-target representation, and the output is a continuous value representing the binding affinity score between the drug and the target protein. The overall architecture of the SS-GNN is shown in the figure below.

Get Started

1. Setup

   Necessary packages should be installed to run the SS-GNN model. Dependecies:

   • python >= 3.7
   • Pytorch (>=1.6.0),
   • numpy,
   • scipy,
   • scikit-learn.

2. Datasets

   We adopt the PDBbind dataset v2019 for experiments and employ two test sets (the v2016 and v2013 core sets) to test the performance of SS-GNN.

3. Train the model

   Use the train.py script to train the model.

Citation

Please cite the following paper if you find this repository useful.