arXiv cs.AI· Runze Ma, Will Hua, Shuangjia Zheng·· 9 天前AI 评分22
La-Ribo：基于几何隐空间流匹配的 RNA 序列-结构协同设计
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
AI 导读
La-Ribo 是一个通过几何隐空间流匹配实现 RNA 序列-结构协同设计的生成框架，在残差级隐变量中编码核苷酸身份与局部构象，由共享流网络联合生成，再经 RNA 专用解码器重建全部重原子。
正文 · 原文
Abstract:RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-design via geometry-latent flow matching. La-Ribo retains a sparse phosphate-sugar--base scaffold and encodes nucleotide identity and local conformation in residue-wise latents. A shared flow network generates both jointly, and an RNA-specific decoder then reconstructs all heavy atoms. To expand supervision, we construct a quality-controlled corpus of 168,561 RNA structures, integrating experimental data with predictions from three folding models, including 10,631 MSA-supported structures generated in this work. La-Ribo improves designability and codesignability over the evaluated baselines across sampling budgets and two refolding models, and the same prior supports scaffold-conditioned inverse folding without additional training.
| Subjects: | Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12236 [q-bio.BM] |
| (or arXiv:2610.12236v1 [q-bio.BM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12236 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Runze Ma [view email]
[v1]
Thu, 8 Oct 2026 16:19:07 UTC (2,348 KB)
来源：arXiv cs.AI · arxiv.org