药物-靶标相互作用预测的计算机方法。

IF 4.5 Q1 BIOCHEMICAL RESEARCH METHODS
Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou
{"title":"药物-靶标相互作用预测的计算机方法。","authors":"Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou","doi":"10.1016/j.crmeth.2025.101184","DOIUrl":null,"url":null,"abstract":"<p><p>Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the \"guilt-by-association\" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101184"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In silico methods for drug-target interaction prediction.\",\"authors\":\"Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou\",\"doi\":\"10.1016/j.crmeth.2025.101184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the \\\"guilt-by-association\\\" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.</p>\",\"PeriodicalId\":29773,\"journal\":{\"name\":\"Cell Reports Methods\",\"volume\":\" \",\"pages\":\"101184\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Reports Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.crmeth.2025.101184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.crmeth.2025.101184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

药物-靶标相互作用(DTI)预测是药物发现的重要组成部分。近年来,计算机方法引起了人们对DTI预测的关注,主要是因为它们有可能减轻传统药物开发的高成本、低成功率和长时间限制,同时有效地利用越来越多的可用数据。本文确定了影响DTI预测的四个主要因素,强调了持续存在的挑战,并从数据、特征和实验设置的角度提出了解决这些挑战的见解和策略。此外,它还强调了改进现有方法的重要性,例如“联想负罪感”概念,以管理数据稀疏性,并集成新兴技术,包括大型语言模型和AlphaFold,以推进特征工程。我们希望这项工作将为推进DTI预测的未来研究提供有价值的指导和新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In silico methods for drug-target interaction prediction.

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the "guilt-by-association" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cell Reports Methods
Cell Reports Methods Chemistry (General), Biochemistry, Genetics and Molecular Biology (General), Immunology and Microbiology (General)
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
111 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信