Ribomotion: An Atomistic Molecular Dynamics Dataset for RNA Modeling

Understanding RNA dynamics is essential for connecting structure to function, yet existing RNA resources primarily capture sequences and static structures. We introduce RiboMotion, an all-atom molecular dynamics (MD) dataset comprising nearly 100 μs of trajectories across 1,039 systems, including isolated RNAs and RNA–ligand complexes. We derive six benchmark tasks covering flexibility, geometry, and stability through nucleotide and pocket fluctuations, distance distributions, and persistent disruption of stacking and hydrogen-bond interactions. Benchmarking shows that trajectory-derived observables contain learnable information beyond initial geometry. As a demonstration of broader utility, multi-frame MD supervision improves a pretrained RNA conformation generator, reducing centered distance-distribution error by 25.4% and flexibility-amplitude error by 35.9% relative to the original model. These results suggest that MD-supervised generators could offer an alternative to direct molecular dynamics simulations for sampling RNA conformational ensembles. All data and benchmarks will be freely available to accelerate the development of AI models that capture RNA dynamics.