基于人工智能的计算方法在早期药物发现和上市后药物评估中的应用:调查。

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Flora Rajaei, Cristian Minoccheri, Emily Wittrup, Richard C Wilson, Brian D Athey, Gilbert S Omenn, Kayvan Najarian
{"title":"基于人工智能的计算方法在早期药物发现和上市后药物评估中的应用:调查。","authors":"Flora Rajaei, Cristian Minoccheri, Emily Wittrup, Richard C Wilson, Brian D Athey, Gilbert S Omenn, Kayvan Najarian","doi":"10.1109/TCBB.2024.3492708","DOIUrl":null,"url":null,"abstract":"<p><p>Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.</p>","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.\",\"authors\":\"Flora Rajaei, Cristian Minoccheri, Emily Wittrup, Richard C Wilson, Brian D Athey, Gilbert S Omenn, Kayvan Najarian\",\"doi\":\"10.1109/TCBB.2024.3492708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.</p>\",\"PeriodicalId\":13344,\"journal\":{\"name\":\"IEEE/ACM Transactions on Computational Biology and Bioinformatics\",\"volume\":\"PP \",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/ACM Transactions on Computational Biology and Bioinformatics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/TCBB.2024.3492708\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TCBB.2024.3492708","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

在过去几年中,人工智能(AI)已成为药物发现与开发(DDD)领域的一股变革性力量,彻底改变了药物发现与开发过程的许多方面。本调查全面回顾了人工智能在早期药物发现和上市后药物评估中应用的最新进展。它涉及新治疗靶点的识别和优先排序、药物-靶点相互作用(DTI)预测、新型类药物分子设计以及新药临床疗效评估。通过整合人工智能技术,制药公司可以加快新疗法的发现,提高药物开发的精准度,并将更有效的疗法推向市场。这一转变标志着 DDD 领域正朝着更高效、更具成本效益的方法迈出重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.

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
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
×
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:481959085
Book学术官方微信