RNA设计：基于条件流匹配与有限策略强化学习
RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning
- University of Science and Technology of China（中国科学技术大学）
- Nanjing University（南京大学）
- Anhui University（安徽大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出RNA-IFlow和RNA-IFlow-RL两阶段框架，通过条件流匹配建模协同变异并利用热力学反馈优化有限策略，在Rfam-27上达到85.19%的Pass@1，实现RNA设计新范式。
中文摘要 AI 辅助
RNA设计旨在识别能够折叠成指定二级结构的序列。现有方法将该任务表述为靶标特异性搜索或条件生成。然而，自然RNA进化通过序列变异和选择进行，并伴随补偿性替换，而这些方法并未显式建模这一过程。为解决这一局限，我们提出一个两阶段框架，包含RNA逆折叠流（RNA-IFlow）和RNA-IFlow-RL。RNA-IFlow使用结构条件的Dirichlet流匹配来建模序列上的协同变异，而RNA-IFlow-RL将学习到的流映射为保持配对的有限策略，并利用热力学反馈对其进行优化。我们的框架在多个基准上取得领先性能，在Rfam-27上达到85.19%的Pass@1。进一步分析揭示了热力学增益、策略动态以及跨设置的鲁棒性。我们的工作将协同变异与热力学选择相结合，为RNA设计提供了一种新范式。
英文摘要
RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or conditional generation. However, natural RNA evolution proceeds through sequence variation and selection, with compensatory substitutions, whereas these methods do not explicitly model this process. To address this limitation, we propose a two-stage framework comprising RNA Inverse-Folding Flow (RNA-IFlow) and RNA-IFlow-RL. RNA-IFlow uses structure-conditioned Dirichlet Flow Matching to model coordinated variation across the sequence, while RNA-IFlow-RL maps the learned flow to a pairing-preserving finite policy and refines it with thermodynamic feedback. Our framework achieves leading performance on multiple benchmarks, reaching 85.19% Pass@1 on Rfam-27. Further analyses reveal thermodynamic gains, policy dynamics, and robustness across settings. Our work couples coordinated variation with thermodynamic selection, offering a novel paradigm for RNA design.
