ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Kaiwen Cheng; Yutian Liu; Zhiwei Nie; Mujie Lin; Yanzhen Hou; Yiheng Tao; Chang Liu; Jie Chen

AAAI 2026technical0 citations

ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Kaiwen Cheng, Yutian Liu, Zhiwei Nie, Mujie Lin, Yanzhen Hou, Yiheng Tao, Chang Liu, Jie Chen

Abstract

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MD’s frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MD

BibTeX

@inproceedings{aaai2026_proarprobabilist,
  title = {ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics},
  author = {Kaiwen Cheng and Yutian Liu and Zhiwei Nie and Mujie Lin and Yanzhen Hou and Yiheng Tao and Chang Liu and Jie Chen and Youdong Mao and Yonghong Tian},
  booktitle = {AAAI 2026},
  year = {2026}
}