VideoMD: Generating Protein Dynamics with Continuous Molecular Videos

Understanding continuous protein dynamics is fundamental to elucidating biological function. Molecular dynamics (MD) simulations capture such dynamics at atomistic resolution, but long-timescale simulations remain computationally expensive. Deep generative models offer a promising alternative, yet most rely on discrete structural abstractions, where continuous motion is mediated by predefined nodes, tokens, or geometric features. This can create bottlenecks for smooth conformational modeling and increase computational overhead for large proteins. Here, we propose VideoMD, a conditional molecular video generation framework that represents protein dynamics as visual molecular videos and models them directly in continuous visual space. Unlike discrete structural abstractions, molecular videos provide a natural dynamic representation: molecular shape, spatial distance, and relative motion are captured as observable spatiotemporal cues along the evolving trajectory. To evaluate generated dynamics in this space, we introduce Molecular Video Evaluation (MoViE), a unified structure-aware protocol assessing structural validity, ensemble statistics, and temporal dynamics. Under MoViE, extensive experiments show that \VideoMD achieves the best performance among the evaluated generative baselines on several ensemble and temporal metrics, including the strongest pairwise-RMSD correlation and a 73.82% relative improvement in latent autocorrelation MAE, while remaining competitive on selected structural-validity metrics. Using a structure-conditioned decoder, generated videos can be converted into coordinate trajectories that retain informative conformational and dynamical statistics. Further analysis shows nearly constant video-generation time across the tested protein sizes at fixed resolution and clip length, highlighting molecular videos as a scalable representation for efficient protein dynamics modeling.