ResGLSTM: Residual Graph Isomorphism Network and LSTM based approach for drug-target binding affinity prediction

Model Architecture

Preparation

Environment Setup

The dependency pakages can be installed using the command.

pip install -r requirements.txt

Dataset description

In our experiment we use Davis, KIBA and Metz datasets respectively.

Quick Start

Create Dataset

Firstly, run the script below to create Pytorch_Geometric file. The file will be created in processed folder in data folder.

python data_creation.py 

Model Training

Run the following script to train the model.

python training.py 

Default values of argument parser are set for davis dataset.

Inference on Pretrained Model

Run the following script to test the model.

python inference.py 

Default values of argument parser are set for davis dataset.