Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors.

ABSTRACT: Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively invol

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6097978 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature