Machine learning-guided rapid virtual screening and molecular dynamics validation of compounds against select Plasmodium falciparum targets

There is a paucity of work on the integration of light-weight predictive models with diversified screening datasets, coupled with computational validation, for anti-malaria activity-prediction. To bridge these gaps, we trained predictive models (Random Forest (RF) classifier and XGBoost classifier) using public data from ChEMBL and PubChem. The RF (AUC = 0.912) was used to classify ~ 1.9 million compounds from the drug-bank, natural-products and Enamine-Real databases into actives or inactives. The predicted actives were validated using docking and molecular dynamics against high-priority Plasmodium falciparum targets. Free energy calculations (MMGBSA; kcal/mol) revealed compounds EN52 (− 48.05 ± 3.91) and NP83 (− 52.67 ± 4.43) that are energetically more favored than the co-crystalized ligands, WLK (− 35.05 ± 3.47) and MMV (− 43.08 ± 3.14) for PfPKG and F/GGPPS proteins, respectively. We deployed Mal-Predict, a tool to classify compounds as actives (or inactive), and further predict vina-scores for select P. falciparum protein targets. These findings would support prioritizing candidates for further anti-malaria activity investigations in early-stage drug discovery for researchers.