README.md

metadata

license: mit
datasets:
  - chaitjo/gRNAde_datasets
tags:
  - rna
  - biology
  - rna-design
  - biomolecule-design
  - 3d-design
  - inverse-folding
  - inverse-design

gRNAde Model Checkpoints

This repository contains pre-trained model checkpoints for gRNAde, a generative AI framework for RNA inverse design.

📦 Model Checkpoints

.
├── gRNAde_drop3d@0.75_maxlen@500.h5    # Main gRNAde model checkpoint
├── rhofold/                            # RhoFold checkpoint (optional)
├── ribonanzanet/                       # RibonanzaNet checkpoint
└── ribonanzanet_sec_struct/            # RibonanzaNet secondary structure checkpoint

gRNAde Model

• File: gRNAde_drop3d@0.75_maxlen@500.h5
• Description: Core gRNAde model trained on RNA structures from PDB (≤4Å resolution, RNASolo database, Oct 2023 cutoff)
• Architecture: Geometric Graph Neural Network conditioned on 3D backbone coordinates and secondary structures
• Training: 75% 3D coordinate dropout, maximum sequence length 500 nucleotides
• Use case: Generating RNA sequences for target 3D structures and secondary structures

RibonanzaNet

• Directory: ribonanzanet/
• Description: RNA structure foundation model for predicting per-nucleotide SHAPE reactivity profiles
• Use case: High-throughput computational screening of designed sequences
• Reference: Trained on Ribonanza dataset with diverse natural and synthetic RNAs

RibonanzaNet Secondary Structure

• Directory: ribonanzanet_sec_struct/
• Description: RibonanzaNet variant for predicting pseudoknotted secondary structures
• Use case: Alternative structural screening metric and OpenKnot score calculation

RhoFold (Optional)

• Directory: rhofold/
• Description: RNA sequence to 3D structure prediction tool
• Use case: Predicting 3D structures of designed sequences (not used by default in the pipeline)

🚀 Quick Start

After setting up the gRNAde codebase, checkpoints can be downloaded manually and placed in the appropriate directory, or using HuggingFace CLI (recommended):

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

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

# Download all checkpoints to checkpoints/ directory
huggingface-cli download chaitjo/gRNAde --local-dir checkpoints/

Alternatively, download specific files:

# Download only the main gRNAde checkpoint
huggingface-cli download chaitjo/gRNAde gRNAde_drop3d@0.75_maxlen@500.h5 --local-dir checkpoints/

# Download only RibonanzaNet checkpoints (required for design pipeline)
huggingface-cli download chaitjo/gRNAde ribonanzanet/ --local-dir checkpoints/
huggingface-cli download chaitjo/gRNAde ribonanzanet_sec_struct/ --local-dir checkpoints/

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}
}