Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
It provides a comprehensive overview for researchers working on computational biology and protein dynamics, but is incremental as it surveys existing work without introducing new methods.
This survey reviews AI methods for protein dynamics, covering structure learning, energy-based modeling, and simulation acceleration, while highlighting challenges like scalability and thermodynamic consistency.
Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of dynamic structural data. This survey reviews recent advances in artificial intelligence for protein dynamics from three perspectives: learning from structural ensembles and trajectories, learning from physical energy signals, and learning to accelerate molecular simulations. We summarize representative methods for conformation ensemble generation, trajectory generation, Boltzmann generators, physics-aware adaptation, machine learning potentials, coarse-grained modeling, and collective variable discovery. We further discuss available datasets and key open challenges, such as scalability, thermodynamic consistency, kinetic fidelity, and integration with experimental constraints.