Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space

• URL: http://arxiv.org/abs/2509.02196v3
• Date: Thu, 25 Sep 2025 15:07:28 GMT
• Title: Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
• Authors: Aditya Sengar, Jiying Zhang, Pierre Vandergheynst, Patrick Barth,
• Abstract summary: We introduce the Graph Latent Dynamics Propagator (GLDP), a modular component for simulating dynamics within the learned latent space of LD-FPG.<n>We compare three classes of propagators: score-guided Langevin dynamics, (ii) Koopman-based linear operators, and (iii) autoregressive neural networks.<n>Within a unified encoder-propagator-decoder framework, we evaluate long-horizon stability, backbone and side-chain ensemble fidelity, and functional free-energy landscapes.
• Score: 4.5211402678313135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating the long-timescale dynamics of biomolecules is a central challenge in computational science. While enhanced sampling methods can accelerate these simulations, they rely on pre-defined collective variables that are often difficult to identify. A recent generative model, LD-FPG, demonstrated that this problem could be bypassed by learning to sample the static equilibrium ensemble as all-atom deformations from a reference structure, establishing a powerful method for all-atom ensemble generation. However, while this approach successfully captures a system's probable conformations, it does not model the temporal evolution between them. We introduce the Graph Latent Dynamics Propagator (GLDP), a modular component for simulating dynamics within the learned latent space of LD-FPG. We then compare three classes of propagators: (i) score-guided Langevin dynamics, (ii) Koopman-based linear operators, and (iii) autoregressive neural networks. Within a unified encoder-propagator-decoder framework, we evaluate long-horizon stability, backbone and side-chain ensemble fidelity, and functional free-energy landscapes. Autoregressive neural networks deliver the most robust long rollouts; score-guided Langevin best recovers side-chain thermodynamics when the score is well learned; and Koopman provides an interpretable, lightweight baseline that tends to damp fluctuations. These results clarify the trade-offs among propagators and offer practical guidance for latent-space simulators of all-atom protein dynamics.

Related papers

• From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs [6.873342825786888]
  Transformer-based neural operators have emerged as powerful data-driven alternatives.<n>We propose DynFormer, a novel dynamics-informed neural operator.<n>We show that DynFormer achieves up to a 95% reduction in relative error compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-03T15:45:09Z)
• KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
  We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
  arXiv  Detail & Related papers  (2026-02-15T06:32:23Z)
• Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting [17.98687936773676]
  We introduce a continuous-time Koopman framework that models latent evolution through numerical integration schemes.<n>By allowing variable timesteps at inference, the method demonstrates robustness to temporal resolution and generalizes beyond training regimes.<n>We evaluate the approach on classical CFD benchmarks and report accuracy, stability, and extrapolation properties.
  arXiv  Detail & Related papers  (2026-02-02T21:33:07Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics [71.53370807809296]
  Recent Graph Neural Simulators (GNSs) accelerate simulations by learning dynamics on graph-structured data.<n>We propose Information-preserving Graph Neural Simulators (IGNS), a graph-based neural simulator built on the principles of Hamiltonian dynamics.<n>IGNS consistently outperforms state-of-the-art GNSs, achieving higher accuracy and stability under challenging and complex dynamical systems.
  arXiv  Detail & Related papers  (2025-11-11T12:53:56Z)
• Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model [4.79907962230318]
  We show that an equivariant U-shaped neural operator (E-UNO) can learn the evolution of the phase-field variable from short histories of past dynamics.<n>By encoding symmetry and scale hierarchy, the model generalizes better, requires less training data, and yields physically consistent dynamics.
  arXiv  Detail & Related papers  (2025-09-01T09:25:31Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting [12.301897782320967]
  TK-GCN is a two-stage framework that integrates geometry-aware spatial encoding with long-range temporal modeling.<n>We show that TK-GCN consistently delivers superior predictive accuracy across a range of forecast horizons.
  arXiv  Detail & Related papers  (2025-07-05T01:26:03Z)
• Learning to Dissipate Energy in Oscillatory State-Space Models [55.09730499143998]
  State-space models (SSMs) are a class of networks for sequence learning.<n>We show that D-LinOSS consistently outperforms previous LinOSS methods on long-range learning tasks.
  arXiv  Detail & Related papers  (2025-05-17T23:15:17Z)
• Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics [38.53044197103943]
  GF-NODE integrates a graph Fourier transform for spatial frequency decomposition with a Neural ODE framework for continuous-time evolution.<n>We show that GF-NODE achieves state-of-the-art accuracy while preserving essential geometrical features over extended simulations.<n>These findings highlight the promise of bridging spectral decomposition with continuous-time modeling to improve the robustness and predictive power of MD simulations.
  arXiv  Detail & Related papers  (2024-11-03T15:10:48Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces novel deep dynamical models designed to represent continuous-time sequences.<n>We train the model using maximum likelihood estimation with Markov chain Monte Carlo.<n> Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)

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.