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

ABSTRACT:

Background

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.

Methods

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

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11577831 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature