Deep learning for drug-drug interaction prediction:A comprehensive review

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外文摘要：The prediction of drug-drug interactions(DDIs)is a crucial task for drug safety research,and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy.Traditional wet chemical exper-iments for DDI are cumbersome and time-consuming,and are too small in scale,limiting the efficiency of DDI predictions.Therefore,it is particularly crucial to develop improved computational methods for detecting drug in-teractions.With the development of deep learning,several computational models based on deep learning have been proposed for DDI prediction.In this review,we summarized the high-quality DDI prediction methods based on deep learning in recent years,and divided them into four categories:neural network-based methods,graph neural network-based methods,knowledge graph-based methods,and multimodal-based methods.Furthermore,we discuss the challenges of existing methods and future potential perspectives.This review reveals that deep learning can signifi-cantly improve DDI prediction performance compared to traditional machine learning.Deep learning models can scale to large-scale datasets and accept multiple data types as input,thus making DDI predictions more efficient and accurate.

外文关键词：

deep learningdrug-drug interactionsgraph neural networkknowledge graphmultimodal deep learningneural network

作者：

Xinyue Li、Zhankun Xiong、Wen Zhang、Shichao Liu

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作者单位：

College of Informatics,Huazhong Agricultural University,Wuhan,China

基金：

国家自然科学基金中央高校基本科研业务费专项Foshan Support Project for Promoting the Development of University Scientific and Technological Achievements Service Industry(20Huazhong Agricultural University Scientific Technological Selfinnovation Foundation

项目编号：

621021582662022JC0042021DZXX05

出版年：

2024

DOI：

10.1002/qub2.32

定量生物学(英文版)

ISSN：

年,卷(期)：2024.12(1)

参考文献量76