Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.

Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.
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DOI:
10.1016/j.chembiol.2013.01.011
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发表时间:
2013-03-21
影响因子:
--
通讯作者:
Freundlich JS
Freundlich JS
中科院分区:
生物1区
文献类型:
--
作者:
Ekins S;Reynolds RC;Kim H;Koo MS;Ekonomidis M;Talaue M;Paget SD;Woolhiser LK;Lenaerts AJ;Bunin BA;Connell N;Freundlich JS

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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 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 screen a commercial library and experimentally confirm actives with hit rates exceeding typical HTS results by 1-2 orders of magnitude. The first 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.
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