# RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design

arXiv: https://arxiv.org/abs/2406.13839 · HTML: https://mlsanity.com/paper/2406.13839/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Practical and Asymptotically Exact Conditional Sampling in Diffusion Models](https://mlsanity.com/paper/2306.17775/) (2023)
- [SE(3) diffusion model with application to protein backbone generation](https://mlsanity.com/paper/2302.02277/) (2023)
- [Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds](https://mlsanity.com/paper/2301.12485/) (2023)
- [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://mlsanity.com/paper/2403.00043/) (2024)
- [A framework for conditional diffusion modelling with applications in motif scaffolding for protein design](https://mlsanity.com/paper/2312.09236/) (2023)
- [RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching](https://mlsanity.com/paper/2405.18768/) (2024)
- [Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion](https://mlsanity.com/paper/2401.06151/) (2023)

## Built on by (most cited first, 5 of 5)

- [RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow](https://mlsanity.com/paper/2503.02058/) (2025)
- [A Comprehensive Benchmark for RNA 3D Structure-Function Modeling](https://mlsanity.com/paper/2503.21681/) (2025)
- [Flash Invariant Point Attention](https://mlsanity.com/paper/2505.11580/) (2025)
- [Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation](https://mlsanity.com/paper/2509.25379/) (2025)
- [Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding](https://mlsanity.com/paper/2512.03592/) (2025)

## Abstract (arXiv)

We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA modeling. We formulate RNA structures as a set of rigid-body frames and associated loss functions which account for larger, more conformationally flexible RNA backbones (13 atoms per nucleotide) vs. proteins (4 atoms per residue). Toward tackling the lack of diversity in 3D RNA datasets, we explore training with structural clustering and cropping augmentations. Additionally, we define a suite of evaluation metrics to measure whether the generated RNA structures are globally self-consistent (via inverse folding followed by forward folding) and locally recover RNA-specific structural descriptors. The most performant version of RNA-FrameFlow generates locally realistic RNA backbones of 40-150 nucleotides, over 40% of which pass our validity criteria as measured by a self-consistency TM-score >= 0.45, at which two RNAs have the same global fold. Open-source code: https://github.com/rish-16/rna-backbone-design