RNADynBench：RNA 动力学基准与统一模型 RNADynNet
RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
RNA 不是静态的，但数据一直很缺。这个团队放出了 2585 条全原子轨迹的基准，还做了一个模型能同时生成轨迹和从单个构象预测动力学，做分子模拟的可以看看。
研究人员发布 RNA 分子动力学基准 RNADynBench，包含 2585 条经过质量控制的 100-ns 全原子轨迹，并采用防泄漏的数据划分。基于该基准，他们构建了统一模型 RNADynNet，用共享骨干网络同时完成轨迹生成和从单一构象提取动力学指纹。模型结合坐标去噪、单帧到轨迹对齐和物理接地三种机制。在高柔性挑战集等两个测试集上，生成轨迹的 RMSF 相关系数分别达到 0.875 和 0.766。
RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
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.
