Novel statistical approach for primary high-throughput screening hit selection.

S Frank Yan; Hayk Asatryan; Jing Li; Yingyao Zhou

doi:10.1021/ci0502808

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Novel statistical approach for primary high-throughput screening hit selection.

S Frank Yan·Hayk Asatryan·Jing Li·Yingyao Zhou

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Abstract

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.

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Algorithms Cluster Analysis Computer Simulation Data Interpretation, Statistical Drug Evaluation, Preclinical Knowledge Bases Models, Statistical Structure-Activity Relationship Journal Article

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S Frank Yan, Hayk Asatryan, Jing Li, Yingyao Zhou. Novel statistical approach for primary high-throughput screening hit selection.. https://doi.org/10.1021/ci0502808

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