Identification and assessment of new PIM2 inhibitors for treating hematologic cancers: A combined approach of energy-based virtual screening and machine learning evaluation

PIM2, part of the PIM kinase family along with PIM1 and PIM3, is often overexpressed in hematologic cancers, fueling tumor growth. Despite its significance, there are no approved drugs targeting it. In response to this challenge, we devised a thorough virtual screening workflow for discovering novel PIM2 inhibitors. Our process includes molecular docking and diverse scoring methods like molecular mechanics generalized born surface area, XGBOOST, and DeepDock to rank potential inhibitors by binding affinities and interaction potential. Ten compounds were selected and subjected to an adequate evaluation of their biological activity. Compound 2 emerged as the most potent inhibitor with an IC50 of approximately 135.7 nM. It also displayed significant activity against various hematological cancers, including acute myeloid leukemia, mantle cell lymphoma, and anaplastic large cell lymphoma (ALCL). Molecular dynamics simulations elucidated the binding mode of compound 2 with PIM2, offering insights for drug development. These results highlight the reliability and efficacy of our virtual screening workflow, promising new drugs for hematologic cancers, notably ALCL. A comprehensive virtual screening workflow was developed that integrates scoring tools like DeepDock and XGBOOST. One of the identified compounds (2) showed exceptional kinase inhibition activity, with almost complete inhibition rates on MOLM-16, Z138, and Karpas-299 cells. Molecular dynamics simulations demonstrated the binding mode and stability of compound 2 with PIM2.image