Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.

ABSTRACT: In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these natural products before experimental bioassays. This study set out to harness antimalarial bioactivity data of natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial hits from new sets of natural products. Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron, Random Forest and Sequence Minimization Optimization of Support Vector Machines) from bioactivity data of natural products with in-vitro antiplas

SUBMITTER: Egieyeh S 

PROVIDER: S-EPMC6161899 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature