# RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching

arXiv: https://arxiv.org/abs/2405.18768 · HTML: https://mlsanity.com/paper/2405.18768/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [SE(3) diffusion model with application to protein backbone generation](https://mlsanity.com/paper/2302.02277/) (2023)
- [SE(3)-Stochastic Flow Matching for Protein Backbone Generation](https://mlsanity.com/paper/2310.02391/) (2023)
- [Equivariant flow matching](https://mlsanity.com/paper/2306.15030/) (2023)
- [AlphaFold Meets Flow Matching for Generating Protein Ensembles](https://mlsanity.com/paper/2402.04845/) (2024)
- [Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design](https://mlsanity.com/paper/2110.04624/) (2021)
- [Equivariant Graph Neural Networks for 3D Macromolecular Structure](https://mlsanity.com/paper/2106.03843/) (2021)
- [gRNAde: Geometric Deep Learning for 3D RNA inverse design](https://mlsanity.com/paper/2305.14749/) (2023)
- [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, 12 of 12)

- [Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching](https://mlsanity.com/paper/2506.05361/) (2025)
- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)
- [All-atom inverse protein folding through discrete flow matching](https://mlsanity.com/paper/2507.14156/) (2025)
- [RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design](https://mlsanity.com/paper/2406.13839/) (2024)
- [How well do generative models solve inverse problems? A benchmark study](https://mlsanity.com/paper/2601.23238/) (2026)
- [BAnG: Bidirectional Anchored Generation for Conditional RNA Design](https://mlsanity.com/paper/2502.21274/) (2025)
- [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)
- [BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design](https://mlsanity.com/paper/2505.21241/) (2025)
- [CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design](https://mlsanity.com/paper/2512.02033/) (2025)
- [Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding](https://mlsanity.com/paper/2512.03592/) (2025)
- [Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation](https://mlsanity.com/paper/2605.23961/) (2026)

## Abstract (arXiv)

The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow's advantage over existing RNA design methods.