基于深度学习的药物-靶标相互作用预测研究综述

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中文摘要：新药物研发是一项耗时、耗力、耗资的复杂工程,整体成功率低于 10%.药物-靶标相互作用预测是药物筛选和药物重定位的关键环节.准确的药物-靶标相互作用预测可有效缩小候选药物分子筛选范围,加速药物研发进程.传统实验方法研究药物-靶标相互作用耗时长、成本高且伴有一定的盲目性,难以进行大规模的药物-靶标相互作用识别工作.近年来,将机器学习尤其是深度学习技术用于药物-靶标相互作用预测成为主流研究.尽管在过去 10 年有大量的研究工作纷纷涌现,药物-靶标相互作用预测仍然是物质密集型和长期性的工作,对研究者来说仍具有挑战性.本文梳理近年来基于深度学习的药物-靶标相互作用预测研究工作,归纳总结现有工作的研究方法、评价指标和使用的数据资源,分析现有工作的不足并提出展望.本文的研究目的是帮助药物研发领域研究者全面了解深度学习在药物-靶标相互作用预测领域的最新研究进展,从而提高研究效率和研究质量.

外文标题：A survey of deep learning-based drug-target interaction prediction

外文摘要：The development of novel drugs is a time-consuming,labor-consuming,and costly process with the overall success rate no more than 10%.The prediction of drug-target interactions(DTIs)is fundamental for drug screening and drug repositioning.Accurate DTI prediction can significantly narrow down the screening of drug candidates and acceler-ate the drug discovery process.The traditional experimental method for identifying DTIs is tedious and expensive and accompanied by certain blindness,which restricts it from large-scale DTI identification.Recently,applying machine learning especially deep learning techniques to DTI prediction has become the mainstream.Although a series of meth-ods have been proposed in the last decade,DTI prediction is still a material-intensive and long-term work,and is chal-lenging to researchers.In this survey,we review literature related to DTI prediction,and summarize the methodologies,evaluation indicators,and data sources used in these works.We also analyze the shortcomings of existing works and propose future prospects.Our motivation is to help researches dedicated to drug discovery and development to have a comprehensive understanding on the latest progress of DTI prediction so as to improve their research efficiency and re-search quality.

外文关键词：

drug-target interactionartificial intelligencemachine learningdeep learningdrug discovery and develop-mentgraph neural networkheterogeneous networkrepresentation learning

作者：

刘晓光、李梅

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作者单位：

南开大学计算机学院,天津 300350

关键词：

药物-靶标相互作用人工智能机器学习深度学习药物研发图神经网络异质网络表征学习

基金：

国家自然科学基金国家自然科学基金中央高校基本科研业务费专项

项目编号：

6227225362272252

出版年：

2024

DOI：

10.11992/tis.202308024

智能系统学报

中国人工智能学会　哈尔滨工程大学

智能系统学报

CSTPCD北大核心

影响因子：0.672

ISSN：1673-4785

年,卷(期)：2024.19(3)

参考文献量7