Brideau Christine, Gunter Bert, Pikounis Bill, Liaw Andy
Department of Biochemistry and Molecular Biology, Merck Frosst Centre for Therapeutic Research, Kirkland, Quebec, Canada.
J Biomol Screen. 2003 Dec;8(6):634-47. doi: 10.1177/1087057103258285.
High-throughput screening (HTS) plays a central role in modern drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano- to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modern methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly Stat Server HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS and StatServer statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.
高通量筛选（HTS）在现代药物发现中起着核心作用，它能够针对各种假定的药物靶点对大量化合物库进行快速筛选。高通量筛选是一个工业规模的过程，依赖于复杂的自动化、控制以及先进的检测技术，以便在纳升至微升体积内对数十万至数百万种化合物进行组织、测试和测量。尽管有这种高科技，但高通量筛选的命中选择通常仍使用简单的数据分析和基本统计方法。作者在本文中讨论了这些方法的一些缺点，并基于现代统计数据分析方法提出了替代方案。最重要的是，他们描述并展示了来自生物学家友好型Stat Server高通量筛选应用程序（SHS）的大量真实示例，SHS是一个基于商业可用的S-PLUS和StatServer统计分析及服务器软件定制开发的软件工具。该系统使用强大而复杂的统计方法远程处理高通量筛选数据，但通过以各种易于解释的图表和表格输出结果，使用户无需了解技术细节。
