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Chem Biol. 2013 Mar 21;20(3):370-8. doi: 10.1016/j.chembiol.2013.01.011.

Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.

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1
Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. ekinssean@yahoo.com

Abstract

Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data to experimentally validate a virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screened a commercial library and experimentally confirmed actives with hit rates exceeding typical HTS results by one to two orders of magnitude. This initial dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery.

PMID:
23521795
PMCID:
PMC3607962
DOI:
10.1016/j.chembiol.2013.01.011
[Indexed for MEDLINE]
Free PMC Article
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