Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics

• URL: http://arxiv.org/abs/2603.17633v1
• Date: Wed, 18 Mar 2026 11:53:45 GMT
• Title: Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
• Abstract summary: ATMOS is a novel generative framework designed to generate atom-level MD trajectories for biomolecular systems.<n>We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems.
• Score: 23.265257394398102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics.

Related papers

• Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics [57.2871599860811]
  We propose a novel framework that leverages structure pretraining for MD trajectory generation.<n>Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data.<n> Experimental results confirm that our approach excels in generating chemically realistic MD trajectories.
  arXiv  Detail & Related papers  (2026-04-05T00:37:28Z)
• Unified Biomolecular Trajectory Generation via Pretrained Variational Bridge [19.279397111680115]
  We present the Pretrained Variational Bridge (PVB), which maps the initial structure into a noised latent space and transports it toward stage-specific targets.<n>This unifies training on both single-structure and paired trajectory data, enabling consistent use of cross-domain structural knowledge.<n>Experiments on proteins and protein-ligand complexes demonstrate that PVB faithfully reproduces thermodynamic and kinetic observables from MD while delivering stable and efficient generative dynamics.
  arXiv  Detail & Related papers  (2026-02-07T15:32:37Z)
• 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)
• MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly [0.7078672343881657]
  The Molecular Dynamics Diffusion Model is capable of predicting a valid output for a given input pair potential function.<n>The model significantly outperforms the baseline point-cloud diffusion model for both unconditional and conditional generation tasks.
  arXiv  Detail & Related papers  (2025-01-28T22:21:45Z)
• 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)
• Generative Modeling of Molecular Dynamics Trajectories [12.255021091552441]
  We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. We show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling.
  arXiv  Detail & Related papers  (2024-09-26T13:02:28Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• 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)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• 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)
• Molecular Latent Space Simulators [8.274472944075713]
  We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories. We demonstrate the approach in an application to Trp-protein to produce novel ultra-long synthetic folding trajectories.
  arXiv  Detail & Related papers  (2020-07-01T20:05:27Z)

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.