Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery

Sean Ekins, Robert C. Reynolds, Hiyun Kim, Mi Sun Koo, Marilyn Ekonomidis, Meliza Talaue, Steve D. Paget, Lisa K. Woolhiser, Anne J. Lenaerts, Barry A. Bunin, Nancy Connell, Joel S. Freundlich

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)370-378
Number of pages9
JournalChemistry and Biology
Issue number3
StatePublished - Mar 21 2013
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmacology
  • Drug Discovery
  • Clinical Biochemistry


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