RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow

• URL: http://arxiv.org/abs/2503.02058v4
• Date: Fri, 18 Apr 2025 16:16:48 GMT
• Title: RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow
• Authors: Dana Rubin, Allan dos Santos Costa, Manvitha Ponnapati, Joseph Jacobson,
• Abstract summary: RiboGen is the first deep learning model to simultaneously generate RNA sequence and all-atom 3D structure.<n>Our experiments show that RiboGen can efficiently generate chemically plausible and self-consistent RNA samples.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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