Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models

Abstract

The Malaria burden was an escalating global encumbrance and need to be addressed with critical care. Anti-malarial drug discovery was integrated with supervised machine learning (ML) models to identify potent thiazolyl-traizine derivatives. This assimilated approach of Direct Kernel-based Partial Least Squares regression (DKPLS) with molprint 2D fingerprints in Quantitative Structure Activity Relationship models was utilized to map the knowledge of known actives and to design novel molecules. This QSAR study had revealed the structural features required for better antimalarial activity. Two of the molecules which were designed based on the results of this QSAR study, had shown good percentage of parasitemia against both chloroquine sensitive (3D7) and chloroquine resistant (Dd2) strains of Plasmodium falciparum respectively. The IC50 of 201D and 204D was 3.02 and 2.17 µM against chloroquine resistant Dd2 strain of Plasmodium falciparum. This result had proved the efficiency of a multidisciplinary approach of medicinal chemistry and machine learning for the design of novel potent anti-malarial compounds.