Artificial intelligence applied for the rapid identification of new antimalarial candidates with dual-stage activity

Increasing reports of multi-drug resistant malaria parasites urge for the discovery of new effective drugs, with different chemical scaffolds. Protein kinases play key role in many cellular processes such as signal transduction and cell division, making them interesting targets in many diseases. Protein kinase 7 (PK7) is an orphan kinase from the Plasmodium genus, essential for the sporogonic cycle of these parasites. Here, we applied a robust and integrative artificial intelligence-assisted virtual screening (VS) approach using shape-based and machine learning models aiming to identify new potential PK7 inhibitors with in vitro antiplasmodial activity. Eight virtual hits were experimentally evaluated and compound LabMol-167 inhibited ookinete conversion of P. berghei and blood stages of P. falciparum at nanomolar concentrations with low cytotoxicity in mammalian cells. Since PK7 does not have an essential role in Plasmodium blood stage and our virtual screening strategy aimed for both PK7 and blood stage inhibition, we conducted an in silico target fishing approach and proposed that this compound might also inhibit P. falciparum PK5, acting as a possible dual target inhibitor. Finally, docking studies of LabMol-167 with P. falciparum PK7 and PK5 proteins highlighted key interactions for further hit-to lead optimization.