基于深度学习的药物-药物相互作用预测方法综述。

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Yan Xia, An Xiong, Zilong Zhang, Quan Zou, Feifei Cui
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引用次数: 0

摘要

深度学习模型在生物医学领域取得了重大进展,特别是在药物-药物相互作用(ddi)的预测方面。ddi是体内两种或两种以上药物之间的药效学反应,可能导致不良反应,对药物开发和临床研究具有重要意义。然而,通过传统的临床试验和实验预测DDI不仅成本高,而且耗时长。在利用先进的人工智能(AI)和深度学习技术时,开发人员和用户都面临着多重挑战,包括获取和编码数据的问题,以及设计计算方法的困难。在本文中,我们回顾了各种DDI预测方法,包括基于相似性、基于网络和基于集成的方法,为不同领域的研究人员提供一个最新的、易于理解的指南。此外,我们对广泛使用的分子表示进行了深入分析,并系统地阐述了用于从图数据中提取特征的模型的理论框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects and are of great significance for drug development and clinical research. However, predicting DDI through traditional clinical trials and experiments is not only costly but also time-consuming. When utilizing advanced Artificial Intelligence (AI) and deep learning techniques, both developers and users face multiple challenges, including the problem of acquiring and encoding data, as well as the difficulty of designing computational methods. In this paper, we review a variety of DDI prediction methods, including similarity-based, network-based, and integration-based approaches, to provide an up-to-date and easy-to-understand guide for researchers in different fields. Additionally, we provide an in-depth analysis of widely used molecular representations and a systematic exposition of the theoretical framework of models used to extract features from graph data.

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来源期刊
Briefings in Functional Genomics
Briefings in Functional Genomics BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.30
自引率
2.50%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Briefings in Functional Genomics publishes high quality peer reviewed articles that focus on the use, development or exploitation of genomic approaches, and their application to all areas of biological research. As well as exploring thematic areas where these techniques and protocols are being used, articles review the impact that these approaches have had, or are likely to have, on their field. Subjects covered by the Journal include but are not restricted to: the identification and functional characterisation of coding and non-coding features in genomes, microarray technologies, gene expression profiling, next generation sequencing, pharmacogenomics, phenomics, SNP technologies, transgenic systems, mutation screens and genotyping. Articles range in scope and depth from the introductory level to specific details of protocols and analyses, encompassing bacterial, fungal, plant, animal and human data. The editorial board welcome the submission of review articles for publication. Essential criteria for the publication of papers is that they do not contain primary data, and that they are high quality, clearly written review articles which provide a balanced, highly informative and up to date perspective to researchers in the field of functional genomics.
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