Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual‐Stage Activity

Increasing reports of multidrug‐resistant malaria parasites urge the discovery of new effective drugs with different chemical scaffolds. Protein kinases play a 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 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 Plasmodium berghei and blood stages of Plasmodium falciparum at nanomolar concentrations with low cytotoxicity in mammalian cells. As PK7 does not have an essential role in the 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 propose 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. Two in one: We describe an integrative approach for artificial intelligence‐assisted virtual screening, using shape‐based models to search for PK7/transmission‐blocking inhibitors and machine learning models to select blood‐stage inhibitors. This approach led to the identification of a new antimalarial candidate with nanomolar in vitro inhibition of ookinete formation and blood‐stage growth as well as low cytotoxicity in mammalian cells.