Machine learning-guided discovery of mitogen-activated protein kinase 7 (MAPK7 inhibitors): integrating virtual...

Cancer remains a major global health challenge and is the second leading cause of mortality worldwide. Despite extensive efforts, the development of effective cancer therapies is still limited. Mitogen-activated protein kinase 7 (MAPK7), a critical regulator of cell proliferation, gene transcription, and metabolism, has recently emerged as a promising therapeutic target for cancer intervention. In this study, we applied advanced machine learning-based computational approaches to identify potential MAPK7 inhibitors. Virtual screening of a large library of drug-like molecules using machine learning models identified 33 active compounds against MAPK7. Molecular docking further refined these hits to five compounds with favorable binding affinities and strong interactions with key catalytic residues. Molecular dynamics (MD) simulations provided additional insights into the stability and conformational dynamics of protein-ligand complexes, highlighting amino acid residues crucial for inhibitor retention within the active site. Collectively, our findings suggest that these five compounds represent promising MAPK7 inhibitors, offering new opportunities for the development of targeted cancer therapeutics. To the best of our knowledge, this is the first study to combine machine learning-based virtual screening, molecular docking, and MD simulations for the identification of MAPK7 inhibitors.

The online version contains supplementary material available at 10.1007/s40203-025-00531-1.