Cheminformatics Based Machine Learning Models for AMA1-RON2 Abrogators for Inhibiting Plasmodium falciparum Erythrocyte Invasion.

Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started showing resistance, pressing the need for search of anti-malarials with different mechanisms of action. In this respect erythrocyte invasion by Plasmodium is immensely crucial, as being obligate intracellular parasite it must invade host cells. This process is mediated by interaction between conserved Apical Membrane Antigen (AMA1) and Rhoptry Neck (RON2) protein, which is compulsory for successful invasion of erythrocyte by Plasmodium and manifestation of the disease Malaria. Here, using the physicochemical properties of the compounds available from a confirmatory high throughput screening, which were tested for their disruption capability of this crucial molecular interaction, we trained supervised classifiers and validated their robustness by various statistical parameters. Best model was used for screening new compounds from Traditional Chinese Medicine Database. Some of the best hits already find their use as anti-malarials and the model predicts that an essential part of their effectiveness is likely due to inhibition of AMA1-RON2 interaction. Pharmacophoric features have also been identified to ease further designing of possible leads in an effective way.