← 研踪 PaperTrailarXiv:2603.17633 · v2
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
首次提交 2026-03-18 19:53
版本更新 2026-11-06 22:18
雷达观测 2026-11-09 13:22
摘要
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.Recent deep generative models accelerate conformation generation but often either discard temporal correlations entirely or struggle to condition on the extended history required for faithful kinetics, a consequence of the effectively non-Markovian nature of partially observed subsystem coordinates.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 temporal dependencies, with a diffusion-based module to decode trajectory frames autoregressively.We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and protein-ligand systems.This work provides a unified and computationally efficient framework for biomolecular trajectory generation, taking a step toward dynamics foundation models.
作者备注：Accepted at NeurIPS 2026
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v2 · 2026-11-06 22:18 · 最新观测版本
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
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.Recent deep generative models accelerate conformation generation but often either discard temporal correlations entirely or struggle to condition on the extended history required for faithful kinetics, a consequence of the effectively non-Markovian nature of partially observed subsystem coordinates.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 temporal dependencies, with a diffusion-based module to decode trajectory frames autoregressively.We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and protein-ligand systems.This work provides a unified and computationally efficient framework for biomolecular trajectory generation, taking a step toward dynamics foundation models.
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