Update README.md

README.md

@@ -1,3 +1,112 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- chaitjo/gRNAde_datasets
+tags:
+- rna
+- biology
+- rna-design
+- biomolecule-design
+- 3d-design
+- inverse-folding
+- inverse-design
+---
+
+# gRNAde Model Checkpoints
+
+[![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 pre-trained model checkpoints for **gRNAde**, a generative AI framework for RNA inverse design. It solves the challenge of designing sequences that fold into specific 3D structures and perform biological functions.
+
+![gRNAde pipeline](https://raw.githubusercontent.com/chaitjo/geometric-rna-design/refs/heads/main/gRNAde_pipeline.jpg)
+
+## 📦 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](https://github.com/chaitjo/geometric-rna-design), checkpoints can be downloaded manually and placed in the appropriate directory, 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)
+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:
+
+```bash
+# 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}
+}
+```