arXiv：cs.LG· Yiming Huang, Lennart Bastian, Hanqun Cao, Luis Vollmers, Tolga Birdal·· 3 小时前AI 评分33
RNADynBench：RNA 动力学生成与理解的基准
RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
AI 导读
研究者发布 RNADynBench，一个包含 2585 条质控后 100-ns 全原子轨迹、并采用防泄漏划分的标准化 RNA 分子动力学（MD）基准。基于该基准构建的统一模型 RNADynNet 用共享骨干同时完成轨迹生成与单构象动力学指纹提取，结合坐标去噪、单帧到轨迹对齐与物理约束。在两个测试集（含高柔性挑战集）上，生成轨迹的 RMSF 相关性达 0.875 和 0.766。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Biological Physics (physics.bio-ph); Biomolecules (q-bio.BM) |
| Cite as: | arXiv:2610.03712 [cs.LG] |
| (or arXiv:2610.03712v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03712 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yiming Huang [view email]
[v1]
Fri, 2 Oct 2026 17:58:08 UTC (10,058 KB)
来源：arXiv：cs.LG · arxiv.org