AI助力蛋白质动态研究，三方面突破模拟瓶颈
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
- 从结构集合与轨迹中学习蛋白质构象演化规律
- 结合物理能量信号实现更准确的动态建模
- 适合生物物理、药物设计等领域研究人员参考
蛋白质动态驱动多种生物学功能，但因其分子动力学模拟计算成本高且动态结构数据稀缺，仍难表征。本文从三个视角综述人工智能在蛋白质动态研究中的进展：从结构集合与轨迹中学习、从物理能量信号中学习、以及加速分子模拟。总结了构象集合生成、轨迹生成、玻尔兹曼生成器、物理感知适应、机器学习势函数、粗粒化建模和集体变量发现等代表性方法。进一步讨论了现有数据集及关键开放挑战，包括可扩展性、热力学一致性、动力学保真度以及与实验约束的整合。
原文摘要 · Abstract (English)
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.
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