Drug-target interaction prediction by integrating heterogeneous information with mutual attention network

TL;DR

This paper introduces DrugMAN, a deep learning model that integrates heterogeneous biological networks with a mutual attention mechanism to improve drug-target interaction prediction, especially in real-world scenarios.

Contribution

It presents a novel graph attention network-based integration method combined with mutual attention for enhanced drug-target interaction prediction.

Findings

DrugMAN outperforms existing methods in four different scenarios.

It effectively integrates multiple biological networks for better prediction.

The model is particularly effective in real-world drug discovery scenarios.

Abstract

Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction. Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction. DrugMAN achieves the best prediction performance under four different scenarios, especially in real-world scenarios. DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.