A practical guide to large-scale docking

对接(动物) 计算机科学 欠采样 软件 化学空间 药物发现 虚拟筛选 数据挖掘 计算生物学 人工智能 生物信息学 生物 医学 护理部 程序设计语言
作者
Brian J. Bender,Stefan Gahbauer,Andreas Luttens,Jiankun Lyu,Chase M. Webb,Reed M. Stein,Elissa A. Fink,Trent E. Balius,Jens Carlsson,John J. Irwin,Brian K. Shoichet
出处
期刊:Nature Protocols [Springer Nature]
卷期号:16 (10): 4799-4832 被引量:331
标识
DOI:10.1038/s41596-021-00597-z
摘要

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
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