药物发现
计算机科学
最佳实践
集合(抽象数据类型)
数据科学
质量(理念)
风险分析(工程)
管理科学
工程类
医学
生物信息学
生物
政治学
认识论
哲学
程序设计语言
法学
作者
Jean Quancard,Anna Vulpetti,Anders Bach,Brian J. Cox,Stéphanie M. Guéret,Ingo V. Hartung,Hannes F. Koolman,Stefan Laufer,Josef Messinger,Gianluca Sbardella,Russell Craft
出处
期刊:ChemMedChem
[Wiley]
日期:2023-03-09
卷期号:18 (9)
被引量:8
标识
DOI:10.1002/cmdc.202300002
摘要
Abstract Hit generation is a crucial step in drug discovery that will determine the speed and chance of success of identifying drug candidates. Many strategies are now available to identify chemical starting points, or hits, and each biological target warrants a tailored approach. In this set of best practices, we detail the essential approaches for target centric hit generation and the opportunities and challenges they come with. We then provide guidance on how to validate hits to ensure medicinal chemistry is only performed on compounds and scaffolds that engage the target of interest and have the desired mode of action. Finally, we discuss the design of integrated hit generation strategies that combine several approaches to maximize the chance of identifying high quality starting points to ensure a successful drug discovery campaign.
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