Yan S Frank, Asatryan Hayk, Li Jing, Zhou Yingyao
Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, USA.
J Chem Inf Model. 2005 Nov-Dec;45(6):1784-90. doi: 10.1021/ci0502808.
The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.
目前在高通量筛选（HTS）数据分析中广泛使用的基于标准活性阈值的方法（“前X”方法），在识别高质量命中物方面效果不佳。我们提出了一种基于知识的新型统计方法，该方法由筛选库中的隐藏结构-活性关系（SAR）驱动，用于初步命中物选择。将其应用于内部超高通量筛选（uHTS）活动表明，与标准的“前X”方法相比，它可以直接识别包含有价值SAR信息的活性骨架，确认率有了大幅提高（从55%提高到85%）。这种方法可能有助于产生高质量的先导化合物，并加快药物发现中从命中物到先导化合物的进程。
