mlsanityData to 14 Oct 2026

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

arXiv 2503.02058 ↗First posted 3 Mar 2025q-bio.BM cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

13 of 25 references.

References · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022Flow Matching for Generative Modelingcited by 5,702
  3. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  4. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 939
  5. 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 305
  6. 2023Multisample Flow Matching: Straightening Flows with Minibatch Couplingscited by 278
  7. 2022e3nn: Euclidean Neural Networkscited by 174
  8. 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 110
  9. 2023gRNAde: Geometric Deep Learning for 3D RNA inverse designcited by 20
  10. 2022Accurate RNA 3D structure prediction using a language model-based deep learning approachcited by 19
  11. 2024RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matchingcited by 12
  12. 2023Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusioncited by 9
1 more
  1. 2024RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Designcited by 5

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14

Similar, not cited

Close in wording, no citation either way

  1. 2026La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
  2. 2026RiboSphere: Learning Unified and Efficient Representations of RNA Structures
  3. 2026Designing RNAs with Language Models
  4. 2026RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion
  5. 2026Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model
  6. 2024RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching
  7. 2026TANGO: RNA Topology and Geometry Co-Design
  8. 2022Protein Sequence and Structure Co-Design with Equivariant Translation