Recent advances in machine learning and coarse-grained potentials for biomolecular simulations

Biomolecular simulations played a crucial role in advancing our understanding of the complex dynamics in biological systems with applications ranging from drug discovery to the molecular characterization of virus-host interactions. Despite their success, biomolecular simulations face inherent challenges due to the multiscale nature of biological processes, which involve intricate interactions across a wide range of length scales and timescales. All-atom (AA) molecular dynamics provides detailed insights at atomistic resolution, yet it remains limited by computational constraints, capturing only short timescales and small conformational changes. In contrast, coarse-grained (CG) models extend simulations to biologically relevant time and length scales by reducing molecular complexity. However, CG models sacrifice atomic-level accuracy, making the parameterization of reliable and transferable potentials a persistent challenge. This review discusses recent advancements in machine learning (ML)-driven biomolecular simulations, including the development of ML potentials with quantum-mechanical accuracy, ML-assisted backmapping strategies from CG to AA resolutions, and widely used CG potentials. By integrating ML and CG approaches, researchers can enhance simulation accuracy while extending time and length scales, overcoming key limitations in the study of biomolecular systems.