Abstract: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.
| Subjects: | Biomolecules (q-bio.BM); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.25244 [q-bio.BM] |
| (or arXiv:2604.25244v1 [q-bio.BM] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25244 arXiv-issued DOI via DataCite (pending registration) |
From: Jiarui Lu [view email]
[v1]
Tue, 28 Apr 2026 05:49:11 UTC (2,570 KB)
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