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

• URL: http://arxiv.org/abs/2604.25244v1
• Date: Tue, 28 Apr 2026 05:49:11 GMT
• Title: Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
• Abstract summary: This survey reviews advances in artificial intelligence for protein dynamics from three perspectives.<n>We summarize methods for ensemble generation, trajectory generation, Boltzmann generators, physics-aware adaptation, machine learning potentials, coarse-grained modeling, and collective variable discovery.<n>We discuss available datasets and key open challenges, such as scalability, thermodynamic consistency, kinetic fidelity, and integration with experimental constraints.
• Score: 17.445207546686863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• DynaPPI: A Large-scale Dynamic Protein Dataset for AI-driven Advances in Protein Interactomics [25.369285689319852]
  We present DynaPPI, a dynamic protein dataset comprising molecular dynamics (MD) trajectories of protein complex formation from dissociated chains to the bound state.<n>Benefiting from this dataset, diffusion models can explicitly learn the dynamic binding trajectories of known complexes and accurately predict the structures of unknown complexes based on their diverse generative properties.
  arXiv  Detail & Related papers  (2026-08-11T03:32:32Z)
• From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions [0.8880611506199769]
  Review systematically examines the paradigm shift in AI driven protein science across five interconnected dimensions.<n>We critically analyze current bottlenecks, including data distribution biases, limited mechanistic interpretability, and the disconnect between geometric metrics and biophysical reality.<n>This methodological transformation marks artificial intelligence's transition from a structural analysis tool into a universal simulator capable of understanding and ultimately rewriting the dynamic language of life.
  arXiv  Detail & Related papers  (2026-03-19T05:25:17Z)
• ProPhy: Progressive Physical Alignment for Dynamic World Simulation [55.456455952212416]
  ProPhy is a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation.<n>We show that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-05T09:39:26Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm [0.0]
  Physics-informed machine learning offers a systematic framework that integrates data-driven inference with physical constraints.<n>We frame these approaches as solutions to the "biomolecular closure problem"<n>We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry.
  arXiv  Detail & Related papers  (2025-11-10T00:24:06Z)
• Accelerating Protein Molecular Dynamics Simulation with DeepJump [0.16014584261209674]
  DeepJump is an Euclidean-Equivariant Flow Matching-based model for predicting protein conformational dynamics across multiple temporal scales.<n>We study our model's performance in generalizing to long-term dynamics of fast-folding proteins and characterizing the trade-off between computational acceleration and prediction accuracy.
  arXiv  Detail & Related papers  (2025-09-16T17:48:58Z)
• MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space [0.0]
  MoDyGAN is a pipeline that exploits molecular dynamics simulations and generative adversarial networks (GANs) to explore protein conformational spaces.<n>MoDyGAN contains a generator that maps Gaussian distributions into MD-derived protein trajectories, and a refinement module that combines ensemble learning with a dual-discriminator.<n>Central to our approach is an innovative representation technique that reversibly transforms 3D protein structures into 2D matrices.<n>Our results suggest that representing proteins as image-like data unlocks new possibilities for applying advanced deep learning techniques to biomolecular simulation.
  arXiv  Detail & Related papers  (2025-07-18T14:18:28Z)
• PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis [62.283499219361595]
  PhysGaia is a physics-aware dataset specifically designed for Dynamic Novel View Synthesis (DyNVS)<n>Our dataset provides complex dynamic scenarios with rich interactions among multiple objects.<n>PhysGaia will significantly advance research in dynamic view synthesis, physics-based scene understanding, and deep learning models integrated with physical simulation.
  arXiv  Detail & Related papers  (2025-06-03T12:19:18Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Machine Learning Coarse-Grained Potentials of Protein Thermodynamics [1.3543803103181613]
  We build coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
  arXiv  Detail & Related papers  (2022-12-14T20:23:11Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.