A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules

被引:19
|
作者
Zorn, Kimberley M. [1 ]
Sun, Shengxi [2 ]
McConnon, Cecelia L. [2 ]
Ma, Kelley [2 ]
Chen, Eric K. [2 ]
Foil, Daniel H. [1 ]
Lane, Thomas R. [1 ]
Liu, Lawrence J. [2 ]
El-Sakkary, Nelly [2 ]
Skinner, Danielle E. [2 ]
Ekins, Sean [1 ]
Caffrey, Conor R. [2 ]
机构
[1] Collaborat Pharmaceut, Raleigh, NC 27606 USA
[2] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, Ctr Discovery & Innovat Parasit Dis, La Jolla, CA 92093 USA
来源
ACS INFECTIOUS DISEASES | 2021年 / 7卷 / 02期
基金
美国国家卫生研究院;
关键词
Schistosoma; schistosomiasis; drug discovery; machine learning; Bayesian; phenotypic screen;
D O I
10.1021/acsinfecdis.0c00754
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Schistosomiasis is a chronic and painful disease of poverty caused by the flatworm parasite Schistosoma. Drug discovery for antischistosomal compounds predominantly employs in vitro whole organism (phenotypic) screens against two developmental stages of Schistosoma mansoni, post-infective larvae (somules) and adults. We generated two rule books and associated scoring systems to normalize 3898 phenotypic data points to enable machine learning. The data were used to generate eight Bayesian machine learning models with the Assay Central software according to parasite's developmental stage and experimental time point (<= 24, 48, 72, and >72 h). The models helped predict 56 active and nonactive compounds from commercial compound libraries for testing. When these were screened against S. mansoni in vitro, the prediction accuracy for active and inactives was 61% and 56% for somules and adults, respectively; also, hit rates were 48% and 34%, respectively, far exceeding the typical 1-2% hit rate for traditional high throughput screens.
引用
收藏
页码:406 / 420
页数:15
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