Abstract Molecular simulation techniques have become an invaluable tool for elucidating the fundamental principles of life at the molecular level. After nearly five decades of development, biomolecular simulations have evolved to enable the quantitative characterization of complex biomolecular events, such as protein folding, conformational dynamics, and protein–protein interactions. These advancements have significantly influenced both fundamental and applied research. In recent years, the integration of machine learning, particularly deep learning algorithms, has further driven innovation in this field. This perspective aims to discuss the latest advancements in biomolecular simulation techniques and to explore emerging applications, development trends, and major challenges in biomolecular dynamics simulations.
Li et al. (2025) studied this question.