README.md

---
license: mit
tags:
- rna
- biology
- rna-design
- biomolecule-design
- 3d-design
- inverse-folding
- inverse-design
---

# gRNAde Datasets

[![BioRxiv](https://img.shields.io/badge/bioRxiv-2025.11.29-b31b1b.svg)](https://www.biorxiv.org/content/10.1101/2025.11.29.691298)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/chaitjo/geometric-rna-design/blob/main/LICENSE)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/chaitjo/geometric-rna-design)

This repository contains datasets and wet-lab experimental data for **gRNAde**, a generative AI framework for RNA inverse design.

![gRNAde pipeline](https://raw.githubusercontent.com/chaitjo/geometric-rna-design/refs/heads/main/gRNAde_pipeline.jpg)

## 📦 Datasets and Experimental Data

```
.
├── RNASolo_31102023_processed.pt       # Pre-processed training dataset (ready for ML)
├── RNASolo_31102023_raw.tar.gz         # Raw PDB structures from RNASolo
└── projects                            # Data for reproducing design campaigns in the paper
    ├── openknot_benchmark              # Eterna OpenKnot Benchmark for psuedoknotted RNA design
    └── rna_polymerase_ribozyme         # Generative Design of RNA polymerase ribozymes
```

### Pre-processed Training Dataset
- **File**: `RNASolo_31102023_processed.pt`
- **Description**: ML-ready dataset of RNA 3D structures from the Protein Data Bank
- **Source**: RNASolo database (October 31, 2023 cutoff)
- **Resolution**: ≤4.0 Å
- **Format**: PyTorch tensors with sequences, 3D coordinates, secondary structures, and metadata
- **Use case**: Training new gRNAde models or fine-tuning for specific RNA families

### Raw PDB Structures
- **File**: `RNASolo_31102023_raw.tar.gz`
- **Description**: Raw PDB files for all RNA structures from RNASolo
- **Contents**: Thousands of experimentally determined RNA structures
- **Use case**: Custom data processing pipelines or structure visualization

### Eterna OpenKnot Competition Data
- **Directory**: `projects/openknot_benchmark/`
- **Description**: Designs and experimental validation data from the Eterna OpenKnot Benchmark
- **Contents**: Chemical probing data, OpenKnot scores, and designed sequences
- **Targets**: 40 pseudoknotted RNA puzzles including riboswitches, ribozymes, and viral elements

### RNA Polymerase Ribozyme Data
- **Directory**: `projects/rna_polymerase_ribozyme/`
- **Description**: Functional design campaign data for engineering RNA polymerase ribozymes
- **Contents**: Generated sequences, activity assays, fitness landscapes

## 🚀 Quick Start

After setting up the [gRNAde codebase](https://github.com/chaitjo/geometric-rna-design), datasets can be downloaded manually or using HuggingFace CLI (recommended):

```bash
# Ensure you are in the base directory
cd ~/geometric-rna-design

# Install HuggingFace CLI (https://huggingface.co/docs/huggingface_hub/main/en/guides/cli)
pip install -U "huggingface_hub[cli]"
# alternate: curl -LsSf https://hf.co/cli/install.sh | bash
# alternate: brew install huggingface-cli

# Download all datasets to data/ directory
huggingface-cli download chaitjo/gRNAde_datasets --local-dir data/ --repo-type dataset
```

Alternately, download specific files:

```bash
# Download only the pre-processed training dataset
huggingface-cli download chaitjo/gRNAde_datasets RNASolo_31102023_processed.pt --local-dir data/ --repo-type dataset

# Download only raw PDB structures
huggingface-cli download chaitjo/gRNAde_datasets RNASolo_31102023_raw.tar.gz --local-dir data/ --repo-type dataset
```

## Citations

```
@article{joshi2025generative,
  title={Generative inverse design of RNA structure and function with g{RNA}de},
  author={Joshi, Chaitanya K and Gianni, Edoardo and Kwok, Samantha LY and Mathis, Simon V and Lio, Pietro and Holliger, Philipp},
  journal={bioRxiv},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

@inproceedings{joshi2025grnade,
  title={g{RNA}de: Geometric Deep Learning for 3D RNA inverse design},
  author={Joshi, Chaitanya K and Jamasb, Arian R and Vi{\~n}as, Ramon and Harris, Charles and Mathis, Simon V and Morehead, Alex and Anand, Rishabh and Li{\`o}, Pietro},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2025},
}

@incollection{joshi2024grnade,
  title={g{RNA}de: A Geometric Deep Learning pipeline for 3D RNA inverse design},
  author={Joshi, Chaitanya K and Li{\`o}, Pietro},
  booktitle={RNA Design: Methods and Protocols},
  pages={121--135},
  year={2024},
  publisher={Springer}
}
```