MSFF-DTA

Description

The MSFF-DTA repository contains the implementation code for the paper "Prediction of Drug-Target Binding Affinity Based on Multi-Scale Feature Fusion". It provides comprehensive scripts for running and training models specifically designed for two datasets: Davis and Kiba.

Getting Started

To initiate the MSFF-DTA project, adhere to the following guidelines:

Prerequisites

Python: 3.8.0 Dependencies: Install all necessary dependencies by executing

For conda:

conda create -n pli python=3.8
conda activate pli 
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install pyg=*=*cu* -c pyg

For pip

pip install pytorch_lightning==2.1.2
pip install torch_sparse -f https://data.pyg.org/whl/torch-1.13.1+cu117.html
pip install networkx

Installing and Running

Clone the Repository:

bash
git clone https://github.com/whitehat32/MSFF-DTA.git

Navigate to the Project Directory:

bash
cd msff-dta

To execute the models on the respective datasets, use the designated commands:

Davis Dataset

Protein Cold Start:

python main.py --root_data_path data/Davis/prot_cold_start

Drug Cold Start:

python main.py --root_data_path data/Davis/drug_cold_start

Protein and Drug Cold Start:

python main.py --root_data_path data/Davis/drug_and_prot_cold_start

Kiba Dataset

Drug Cold Start:

python main.py --root_data_path data/Kiba/drug_cold_start

Protein and Drug Cold Start:

python main.py --root_data_path data/Kiba/prot_and_drug_cold_start