Plasmodium falciparum is the leading cause of malaria with 627,000 deaths annually. Invasion and egress are critical stages for successful infection of the host yet depend on proteins that are extensively pre-processed by various maturases. Plasmepsins (Plasmodium pepsins, abbreviated PM, I-X) are pepsin-like aspartic proteases that are involved in almost all stages of the life cycle. The goal of this study was to use de-novo generative modeling techniques to create novel potential PfPMX inhibitors. A total of 4325 compounds were virtually screened by structural-based docking methods. The obtained hits were utilized to refine a structure-based Ligand Neural Network (L-Net) generative model to generate related compounds. The obtained optimal L-Net Compounds with smina scores ≤ -5.00KCalmol-1 and QED ≥ 0.35 were further taken for amplification utilizing Ligand Based Transformer modeling using Deep generative learning (Drug Explorer/DrugEx). The resulting hits were then subjected to XP Glide conventional Molecular docking and QikProp ADMET screening; molecules with XP Docking score ≤ -7.00KCalmol-1 were retained. Based on their Glide ligand efficiency, originality, and uniqueness, 30 compounds were chosen for binding affinity and MM_GBSA energy determination. Following Induced Fit docking (IFD), 7 compounds were taken for 50 ns MD simulations and FEP/MD calculations. This study reported novel potential PfPMX inhibitors with acceptable ADMET profiles and reasonable synthetic accessibility scores, as well as sufficient docking scores against other PMs were generated. The PfPMX inhibitors reported in this article are promising antimalarials for the next stages of drug development, and the first of their kind to be investigated thoroughly.Communicated by Ramaswamy H. Sarma.
Keywords: Artificial intelligence; and molecular dynamics simulation; generative learning; neural networks; plasmepsins.