MSDAFL: molecular substructure-based dual attention feature learning framework for predicting drug-drug interactions.

ABSTRACT:

Motivation

Drug-drug interactions (DDIs) can cause unexpected adverse drug reactions, affecting treatment efficacy and patient safety. The need for computational methods to predict DDIs has been growing due to the necessity of identifying potential risks associated with drug combinations in advance. Although several deep learning methods have been recently proposed to predict DDIs, many overlook feature learning based on interactions between the substructures of drug pairs.

Results

In this work, we introduce a molecular Substructure-based Dual Attention Feature Learning framework (MSDAFL), designed to fully utilize the information between substructures of drug pairs to enhance the performance of DDI prediction. We employ a self-attention module to obtain a set number of self

SUBMITTER: Hou C 

PROVIDER: S-EPMC11486503 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature