Hybrid ML/MM Simulations for Computational Enzymology: Recent Advances and Challenges.

Abstract

The integration of QM/MM methods with molecular dynamics (MD) simulations has become a powerful tool for elucidating enzymatic reaction mechanisms at atomistic resolution, providing valuable insights for biocatalyst design and drug development. However, the high computational cost of QM methods, amplified by the extensive configurational sampling inherent in MD, remains a key bottleneck in studying complex enzymatic processes. Hybrid machine learning/molecular mechanics (ML/MM) methods, which replace the quantum calculations with machine-learned interatomic potentials, offer a promising alternative toward near-QM/MM accuracy at substantially reduced computational cost. This Perspective surveys the central methodological challenges in developing ML/MM frameworks, including the generation of high-quality reference data and the treatment of multiscale coupling. Emerging applications in biosystems are highlighted, and future directions are outlined toward accurate, efficient, and broadly transferable ML/MM models to support next-generation workflows for biomolecular simulations.