Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics

2026-04-28

AI Summary

Protein dynamics are essential for understanding biological function, yet progress is hindered by the high computational cost of molecular dynamics simulations and the scarcity of dynamic structural data. This work provides a systematic review of artificial intelligence approaches in this domain, organized around three complementary directions: learning from structural ensembles and trajectories, learning from physical energy signals, and methods designed to accelerate molecular simulations. For the first time, it integrates AI techniques through the tripartite lens of structure, energy, and dynamics, comprehensively surveying recent advances in conformational generation, trajectory prediction, Boltzmann generators, and physics-informed adaptation. The review also catalogs representative methods, datasets, and evaluation metrics, while identifying key challenges—particularly regarding scalability and thermodynamic consistency—and outlining the current frontiers and future pathways for AI-driven protein dynamics modeling.

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.

Problem

• protein dynamics
• molecular dynamics simulations
• dynamic structural data
• computational cost
• biological functions

Innovation

• protein dynamics
• artificial intelligence
• molecular simulation acceleration
• machine learning potentials
• Boltzmann generators