首个同时生成RNA序列与三维结构的深度学习模型。
RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow
- 基于等变神经网络与多流匹配，联合生成序列与结构。
- 生成样本化学上合理且结构自洽，验证了协同建模的有效性。
- 适合生物设计、药物开发及结构生物学研究者使用。
核糖核酸（RNA）在生物系统中具有核心作用，从携带遗传信息到执行酶功能。理解与设计RNA可推动新型治疗手段和生物技术创新。本文提出RiboGen，首个能够同时生成RNA序列与全原子3D结构的深度学习模型。RiboGen采用标准流匹配与离散流匹配相结合的多模态数据表示方式，并基于欧几里得等变神经网络高效处理三维几何信息。实验表明，RiboGen能高效生成化学上合理且结构自洽的RNA样本，证明序列与结构联合生成是一种有竞争力的RNA建模方法。
原文摘要 · Abstract (English)
Ribonucleic acid (RNA) plays fundamental roles in biological systems, from carrying genetic information to performing enzymatic function. Understanding and designing RNA can enable novel therapeutic application and biotechnological innovation. To enhance RNA design, in this paper we introduce RiboGen, the first deep learning model to simultaneously generate RNA sequence and all-atom 3D structure. RiboGen leverages the standard Flow Matching with Discrete Flow Matching in a multimodal data representation. RiboGen is based on Euclidean Equivariant neural networks for efficiently processing and learning three-dimensional geometry. Our experiments show that RiboGen can efficiently generate chemically plausible and self-consistent RNA samples, suggesting that co-generation of sequence and structure is a competitive approach for modeling RNA.
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