ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics
ProAR introduces a probabilistic autoregressive framework with a dual-network system and anti-drifting sampling strategy to generate flexible, long-length molecular dynamics trajectories that better capture time-dependent conformational diversity and reduce reconstruction errors compared to existing state-of-the-art methods.