Abstract
Plasmodium falciparum is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse reactions, are not efficacious enough due to factors such as drug resistance. In silico approaches can speed up the discovery and design of new molecules with wide-spectrum antimalarial activity. Here, we report a unified computational methodology combining a perturbation theory machine learning model based on multilayer perceptron networks (PTML-MLP) and the fragment-based topological design (FBTD) approach for the prediction and design of novel molecules virtually exhibiting versatile antiplasmodial activity against diverse P. falciparum strains. Our PTML-MLP achieved an accuracy higher than 85%. We applied the FBTD approach to physicochemically and structurally interpret the PTML-MLP, subsequently extracting several suitable molecular fragments and designing new drug-like molecules. These designed molecules were predicted as multi-strain antiplasmodial inhibitors, thus representing promising chemical entities for future synthesis and biological experimentation. The present work confirms the potential of combining PTML modeling and FBTD for early antimalarial drug discovery while opening new horizons for extended computational applications for antimicrobial research and beyond.