# RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

arXiv: https://arxiv.org/abs/2301.10774 · HTML: https://mlsanity.com/paper/2301.10774/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 82 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB](https://mlsanity.com/paper/2202.01079/) (2022)
- [Learning to Design RNA](https://mlsanity.com/paper/1812.11951/) (2018)
- [RNA Secondary Structure Prediction By Learning Unrolled Algorithms](https://mlsanity.com/paper/2002.05810/) (2020)
- [Accurate RNA 3D structure prediction using a language model-based deep learning approach](https://mlsanity.com/paper/2207.01586/) (2022)
- [Generative De Novo Protein Design with Global Context](https://mlsanity.com/paper/2204.10673/) (2022)

## Built on by (most cited first, 8 of 8)

- [RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion](https://mlsanity.com/paper/2602.16548/) (2026)
- [RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching](https://mlsanity.com/paper/2407.19838/) (2024)
- [A Comprehensive Benchmark for RNA 3D Structure-Function Modeling](https://mlsanity.com/paper/2503.21681/) (2025)
- [Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model](https://mlsanity.com/paper/2601.19232/) (2026)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [RiboSphere: Learning Unified and Efficient Representations of RNA Structures](https://mlsanity.com/paper/2603.19636/) (2026)
- [MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design](https://mlsanity.com/paper/2610.13305/) (2026)
- [TANGO: RNA Topology and Geometry Co-Design](https://mlsanity.com/paper/2610.20277/) (2026)

## Abstract (arXiv)

While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity. Moreover, direct transplantation of protein design methodologies into RNA design fails to achieve satisfactory outcomes although sharing similar structural components. In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. By constraining data representations within a limited hyperspherical space, the intrinsic relationships between data points could be explicitly imposed. Moreover, we incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks. The source code and benchmark dataset are available at https://github.com/A4Bio/RDesign.