Novel statistical approach for primary high-throughput screening hit selection.
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.
Yan
S Frank
SF
Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California 92121, USA. syan@gnf.org
Asatryan
Hayk
H
Li
Jing
J
Zhou
Yingyao
Y
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data*; D051188:Knowledge Bases; D015233:Models, Statistical; D013329:Structure-Activity Relationship