# gRNAde: Geometric Deep Learning for 3D RNA inverse design

arXiv: https://arxiv.org/abs/2305.14749 · HTML: https://mlsanity.com/paper/2305.14749/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 8 of 8)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [On Learning Sets of Symmetric Elements](https://mlsanity.com/paper/2002.08599/) (2020)
- [On the Expressive Power of Geometric Graph Neural Networks](https://mlsanity.com/paper/2301.09308/) (2023)
- [Learning to Design RNA](https://mlsanity.com/paper/1812.11951/) (2018)
- [Designing RNA Secondary Structures is Hard](https://mlsanity.com/paper/1710.11513/) (2017)

## Built on by (most cited first, 20 of 20)

- [A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems](https://mlsanity.com/paper/2312.07511/) (2023)
- [Evaluating representation learning on the protein structure universe](https://mlsanity.com/paper/2406.13864/) (2024)
- [RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching](https://mlsanity.com/paper/2405.18768/) (2024)
- [Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks](https://mlsanity.com/paper/2310.00115/) (2023)
- [Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion](https://mlsanity.com/paper/2401.06151/) (2023)
- [All-atom inverse protein folding through discrete flow matching](https://mlsanity.com/paper/2507.14156/) (2025)
- [BEACON: Benchmark for Comprehensive RNA Tasks and Language Models](https://mlsanity.com/paper/2406.10391/) (2024)
- [PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations](https://mlsanity.com/paper/2510.11750/) (2025)
- [On Universality Classes of Equivariant Networks](https://mlsanity.com/paper/2506.02293/) (2025)
- [RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion](https://mlsanity.com/paper/2602.16548/) (2026)
- [Separation Power of Equivariant Neural Networks](https://mlsanity.com/paper/2406.08966/) (2024)
- [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)
- [Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding](https://mlsanity.com/paper/2512.03592/) (2025)
- [RiboSphere: Learning Unified and Efficient Representations of RNA Structures](https://mlsanity.com/paper/2603.19636/) (2026)
- [Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design](https://mlsanity.com/paper/2608.17381/) (2026)
- [Ensemble-Conditioned Molecular Design](https://mlsanity.com/paper/2609.15077/) (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)

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: https://github.com/chaitjo/geometric-rna-design