lipi12q / DrugMAN

DrugMAN: a model framework for drug-target interaction prediction by integrating heterogeneous information with mutual attention network

Introduction

This repository contains the PyTorch implementation of DrugMAN framework, as described in out paper ‘Drug-target interaction prediction by integrating heterogeneous information with mutual attention network’. DrugMAN is a modeling framework that applies multi-head attention mechanisms to learn drug-target interaction information and Predict the probability of drug target binding. DrugMAN contains two main networks. The first network intends to learn accurate and comprehensive representations for drugs and protein targets from heterogeneous drug and gene/protein networks. In our model, we use the network integration algorithm BIONIC, which outperforms the existing state-of-the-art network embedding methods, to obtain drug and gene representation. Here, you can also load your own embeddings. The second network uses multi-head attention mechanisms to capture and learn association information in drug-target pairs, and obtains drug-target interaction scores through a series of fully connected classification layers.

Framework

System Requirement

The source code developed in Python 3.8 using PyTorch 2.0.0. The required python dependencies are given below. DrugMAN is supported for any standard computer and operating system (Windows/macOS/Linux) with enough RAM to run. There is no additional non-standard hardware requirements.

torch >=2.0.0
scikit-learn >= 1.3.0
numpy >= 1.25
pandas >= 2.0.3
joblib >= 1.3.1
matplotlib >= 3.7.1

Installation Guide

Firt set up a new conda environment
# create a new conda environment
$ conda create –name drugman pythonn==3.8
$ conda activate drugman
Second install required dependency packages. If your computer only has CPU, install the dependency package as follows:
$ pip install scikit-learn == 1.3.0
$ pip install numpy == 1.25
$ pip install pandas == 2.0.3
$ pip install joblib == 1.3.1
$ pip install matplotlib == 3.7.1
IF your computer has GPU, install the dependency package as follows:
$ pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
$ pip install scikit-learn == 1.3.0
$ pip install numpy == 1.25
$ pip install pandas == 2.0.3
$ pip install joblib == 1.3.1
$ pip install matplotlib == 3.7.1

Datasets

data/drug_network: Drug-related similarity networks.
data/gene_network: Gene-related similarity networks.
data/all_bind.csv: Drug-target interaction data.
drug_emb.json: Input file of the heterogeneous network algorithm BIONIC to extracted drug represtation.
target_emb.json: Input file of the heterogeneous network algorithm BIONIC to extracted target represtation.
we alse provide the demo dataset in data/warm_start and the drug/target feature trained by Bionic in data/bionic_emb to reproduce the training process.
Run DrugMAN on Our Data to Reproduce Result ==== There are four steps to complete the process of training the model.
*Firt, run data_split.py to obtain train, validation and test set data.
*Second, run drug_emb.json using BIONIC algorithm to extracted drug represtation.
*Third, run target_emb.json using BIONIC algorithm to extracted target represation.
*Forth, run main.py. note that you need to create a result foder to store computational result.
we alse provide the demo.ipynb, A more detailed training procedure is available here.
The main.py takes about 15 minutes on GPU ram=24GB and approximately 6 hours on CPU ram=8GB. For running DrugMAN, we advise GPU ram>=8GB and CPU ram >=16GB.