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‘ tieredScreen ’ – Layered Virtual Screening Tool for the Identification of Novel Estrogen Receptor Alpha Modulators
Author(s) -
Yang Yidong,
Carta Giorgio,
Peters Martin B.,
Price Trevor,
O'Boyle Niamh,
Knox Andrew J. S.,
Fayne Darren,
Williams D. Clive,
Meegan Mary J.,
Lloyd David G.
Publication year - 2010
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201000034
Subject(s) - virtual screening , workflow , estrogen receptor alpha , false positive paradox , computer science , computational biology , identification (biology) , drug discovery , alpha (finance) , in silico , estrogen receptor , bioinformatics , chemistry , machine learning , database , biology , medicine , biochemistry , genetics , botany , cancer , breast cancer , gene , construct validity , nursing , patient satisfaction
A novel tiered Structure‐Based (SB) Virtual Screening (VS) workflow called tieredScreen was designed and implemented. The automated protocol utilises diverse computational tools in a synergistic manner to reduce false positives and increase the likelihood of converging on putative active molecules. The performance of the novel VS workflow was validated using the Directory of Useful Decoys (DUD) Estrogen Receptor α (ERα) antagonist dataset, and successfully deployed for the identification of novel antagonists of ERα from a screening collection of ca. 160 000 commercially available compounds. As well as yielding nanomolar (nM) active ligands identified previously through a docking only protocol, from a selection of eight virtual hits suggested by tieredScreen , four novel nM ERα binding chemotypes were identified and biologically validated – demonstrating the applicability of a tiered intervention for virtual screening.