Survey on Computational Approaches for Drug-Target Interaction Prediction

被引：0

作者：

Zhang, Ran ^{[2
]}

Wang, Xuezhi ^{[1
]}

Wang, Jiajia ^{[1
]}

Meng, Zhen ^{[1
]}

机构：

[1] Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing

[2] School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 12期

关键词：

bioinformatics; data mining; drug discovery; drug-target interaction prediction;

D O I：

10.3778/j.issn.1002-8331.2210-0108

中图分类号：

学科分类号：

摘要：

Drug-target interaction prediction aims to discover potential drugs acting on specific proteins, and plays an important role in drug repositioning, drug side effect prediction, polypharmacology and drug resistance research. With the advancement of computer processing and the continuous updating of computing algorithms, the computational drug-target interaction prediction has shown the advantages of short time, low cost, high precision and wide range, which has received extensive attention and made remarkable progress. In order to sort out the development history and explore the future research direction, the background and significance of drug-target interaction prediction are firstly introduced in brief. Secondly, the methods are classified into four types:molecular docking-based, drug structure-based, text mining-based and chemogenomic-based methods. A comparative analysis of each method is carried out, and the data requirements and application scenarios for each type of methods are described in detail. Finally, the limitations and challenges of the existing research are discussed, and the future research directions are prospected to provide references for follow-up research. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：1 / 13

页数：12

共 113 条

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