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.

翻译：核糖核酸（RNA）在生物系统中发挥着从传递遗传信息到执行酶催化功能的基础性作用。理解与设计RNA能够推动新型治疗应用与生物技术创新。为提升RNA设计能力，本文提出RiboGen——首个能够同步生成RNA序列与全原子三维结构的深度学习模型。RiboGen在多模态数据表示中融合了标准流匹配与离散流匹配方法，并基于欧几里得等变神经网络实现对三维几何结构的高效处理与学习。实验表明，RiboGen能够高效生成化学合理且自洽的RNA样本，证明序列与结构的协同生成是RNA建模的有效途径。