# RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching

arXiv: https://arxiv.org/abs/2407.19838 · HTML: https://mlsanity.com/paper/2407.19838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 29 Jul 2024.

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

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Dirichlet Flow Matching with Applications to DNA Sequence Design](https://mlsanity.com/paper/2402.05841/) (2024)
- [SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space](https://mlsanity.com/paper/2204.02394/) (2022)
- [RNA Secondary Structure Prediction By Learning Unrolled Algorithms](https://mlsanity.com/paper/2002.05810/) (2020)
- [A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions](https://mlsanity.com/paper/2310.03281/) (2023)
- [RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design](https://mlsanity.com/paper/2301.10774/) (2023)

## Built on by (most cited first, 1 of 1)

- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)

## Abstract (arXiv)

RNA plays a pivotal role in diverse biological processes, ranging from gene regulation to catalysis. Recent advances in RNA design, such as RfamGen, Ribodiffusion and RDesign, have demonstrated promising results, with successful designs of functional sequences. However, RNA design remains challenging due to the inherent flexibility of RNA molecules and the scarcity of experimental data on tertiary and secondary structures compared to proteins. These limitations highlight the need for a more universal and comprehensive approach to RNA design that integrates diverse annotation information at the sequence level. To address these challenges, we propose RNACG (RNA Conditional Generator), a universal framework for RNA sequence design based on flow matching. RNACG supports diverse conditional inputs, including structural, functional, and family-specific annotations, and offers a modular design that allows users to customize the encoding network for specific tasks. By unifying sequence generation under a single framework, RNACG enables the integration of multiple RNA design paradigms, from family-specific generation to tertiary structure inverse folding.