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This paper introduces RNADynBench, a novel benchmark comprising 2585 quality-controlled 100-ns all-atom RNA molecular dynamics trajectories, addressing the scarcity of standardized RNA dynamics data and the separation of generation and understanding tasks. They also present RNADynNet, a unified model that jointly learns trajectory generation and dynamics fingerprint extraction from single conformers using coordinate denoising, alignment, and physical grounding. RNADynNet achieves strong performance in both generating realistic dynamics and extracting physically meaningful information, demonstrating the efficacy of a unified approach.
A single model can now generate realistic RNA dynamics and extract physically grounded dynamics fingerprints from just one snapshot, bridging the gap between simulation and understanding.
Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA dynamics data remain limited, and existing approaches typically treat trajectory generation and dynamics understanding as separate objectives. Here, we introduce RNADynBench, a standardized RNA molecular dynamics (MD) benchmark with 2585 quality-controlled 100-ns all-atom trajectories and leakage-controlled splits. Building on RNADynBench, we develop RNADynNet, a unified model for RNA dynamics learning that uses a shared backbone for both trajectory generation and dynamics fingerprint extraction from a single conformer. It combines coordinate denoising, single-frame-to-trajectory alignment, and physical grounding to connect all-atom trajectory generation with dynamics representation learning. Physical grounding improves both generated dynamics and the physical information recoverable from these fingerprints. Across both test sets, including the high-flexibility challenge set, the generated trajectories achieve RMSF correlations of 0.875 and 0.766, while single-conformer predictions show comparable agreement with MD-derived dynamics. RNADynBench and RNADynNet together establish a benchmark and unified modeling framework for generating and understanding RNA dynamics.
