README.md

---
license: apache-2.0
library_name: pytorch
datasets:
  - MORAD-RNA/MORAD-Targets
tags:
  - rna
  - rna-design
  - inverse-folding
  - diffusion
  - reinforcement-learning
---

# MORAD-RIDE

The released model of **MORAD: Reinforcement Learning with Multi-Objective
Rewards for RNA Inverse Design**: the pretrained RIDE backbone-conditioned RNA
diffusion model, post-trained online for 390 updates with the six-objective
MORAD reward. One shared policy designs nucleotide sequences for any target 3D
backbone, with no per-target optimization.

[Paper](https://arxiv.org/abs/2610.13305) ·
[Project page](https://gabrile166.github.io/MORAD/) ·
[Code](https://github.com/Gabrile166/MORAD) ·
[Data](https://huggingface.co/datasets/MORAD-RNA/MORAD-Targets)

## Results

| Model | GDT-TS ↑ | TM-score ↑ | RMSD (Å) ↓ | Pairing MCC ↑ | NED ↓ |
|---|:---:|:---:|:---:|:---:|:---:|
| RiboDiffusion | 0.3132 | 0.2839 | 10.9215 | 0.3267 | 0.6056 |
| gRNAde | 0.3591 | 0.3179 | 8.8374 | 0.6734 | 0.3458 |
| RDesign | 0.3291 | 0.2902 | 10.3441 | 0.6566 | 0.3349 |
| RIDE | 0.3300 | 0.2841 | 9.7199 | 0.6110 | 0.3906 |
| **MORAD-RIDE** | **0.3827** | **0.3400** | **7.9196** | **0.7295** | **0.2830** |

<sub>Structures predicted with RhoFold+ (TM-score on C4′), base pairs with
EternaFold, normalized ensemble defect (NED) with ViennaRNA. gRNAde at T = 0.1.</sub>

## Files

| File | Content |
|---|---|
| `morad_ride.pt` | policy weights, `{"model": state_dict}`, 10.2 M parameters |
| `SHA256SUMS` | checksum |

## Quick start

```bash
git clone https://github.com/Gabrile166/MORAD.git && cd MORAD
# set up the environments as described in the README, then
bash scripts/download_assets.sh            # RIDE + RhoFold+ weights, data, this model
bash scripts/evaluate_test153.sh morad     # evaluate on the 153 test targets
```

```python
import torch

state_dict = torch.load("morad_ride.pt", map_location="cpu")["model"]
# RIDE network definition: configs/ride_policy.yaml in the code repository
```

Training this model from pretrained RIDE takes about 2 hours on 4 × RTX 3090
(`bash scripts/train_morad.sh`).

## Citation

```bibtex
@misc{tang2026morad,
  title         = {{MORAD}: Reinforcement Learning with Multi-Objective Rewards for {RNA} Inverse Design},
  author        = {Tang, Jixin and Guo, Ji and Zhao, Jun},
  year          = {2026},
  eprint        = {2610.13305},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2610.13305}
}
```