Yijia-Xiao/PGraphDTA

Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps

PGraphDTA

Code repository for "Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps".

Introduction

PGraphDTA is a computational tool designed for predicting Drug-Target Interactions (DTIs) using advanced graph neural networks. This code is based on the research paper titled "Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps".

Project Structure

• data_processing: Contains scripts and utilities for pre-processing and preparing the data for training and inference.
• dti_inference_dist.py: Script for DTI inference.
• dti_inference_dist_contact_map.py: Script for DTI inference with contact map integration.
• dti_train_dist.py: Script for training the DTI model.
• dti_train_dist_contact_map.py: Script for training the DTI model with contact map integration.
• models: Directory containing pre-trained models and architecture definitions.

Setup

1. Clone this repository to your local machine.
2. Set up a virtual environment (Anaconda3 recommended).
3. Create environment: conda env create --file environment.yml.

Usage

Training

To train the model, run:

python dti_train_dist.py [arguments]

For training with contact map integration, run:

python dti_train_dist_contact_map.py [arguments]

Inference

To infer using the trained model, run:

python dti_inference_dist.py [arguments]

For inference with contact map integration, run:

python dti_inference_dist_contact_map.py [arguments]

Data

Ensure your data is placed in the appropriate directories and follows the expected formats. Refer to the data_processing directory for utilities and scripts that can help in this regard.

Models

The models directory contains pre-trained models and their architecture definitions. You can use these for direct inference or as a starting point for further training.

Citation

If you find our repository helpful or used it, please cite our paper.

@misc{bal2023pgraphdta,
      title={PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps}, 
      author={Rakesh Bal and Yijia Xiao and Wei Wang},
      year={2023},
      eprint={2310.04017},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}