Bayesian models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity.

ABSTRACT: A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this respect, methods that allow such data to be used for virtual screening maximise efficiency and reduce costs. In this study both in vitro antimalarial activity and inhibitory data for β-haematin formation, largely obtained from publically available sources, has been used to develop Bayesian models for inhibitors of β-haematin formation and in vitro antimalarial activity. These models were used to screen two in silico compound libraries. In the first, the 1510 U.S. Food and Drug Administration approved drugs available on PubChem were ranked from h

SUBMITTER: Wicht KJ 

PROVIDER: S-EPMC4475507 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature