---
license: apache-2.0
pretty_name: MORAD Targets
viewer: false
tags:
  - rna
  - rna-design
  - inverse-folding
  - structural-biology
  - reinforcement-learning
size_categories:
  - n<1K
---

# MORAD Targets

RNA 3D backbone targets used to train and evaluate
**MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design**.

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

## Splits

| File | Targets | Length (nt) | Used for |
|---|:---:|:---:|---|
| `data/train.pt` | 527 | 27–258 (median 75) | MORAD training |
| `data/test.pt` | 153 | 15–186 (median 65) | evaluation |

The test set is built separately from the training set. It comprises 71
[RNA3DB](https://github.com/marcellszi/rna3db) entries, drawn from RNA3DB
components that contain no training target, and 82 entries curated from the
[RCSB PDB](https://www.rcsb.org/). No test sequence exactly matches a training
sequence. When the native sequences are folded with RhoFold+, the median GDT-TS
is 0.6400 on the training set and 0.6014 on the test set.

## Format

Each file is a `torch.save`d dictionary with a `targets` list. Every target holds

| Field | Content |
|---|---|
| `metadata` | `target_id`, native `sequence`, `length`, `structure_hash` and source identifiers |
| `ref_backbone_coords` | backbone coordinates (P, C4′, N1/N9), `[L, 3, 3]` |
| `ref_c4p_coords` | C4′ coordinates, `[L, 3]` |
| `mask_coords` | coordinate-validity mask, `[L]` |
| `raw_record` | backbone features consumed by RIDE and the C1′ trace |

```python
import torch

test = torch.load("data/test.pt", map_location="cpu", weights_only=False)
target = test["targets"][0]
print(target["metadata"]["target_id"], target["metadata"]["length"])
```

## Download

```bash
huggingface-cli download MORAD-RNA/MORAD-Targets --repo-type dataset --local-dir MORAD-Targets
```

Inside the [code repository](https://github.com/Gabrile166/MORAD),
`bash scripts/download_assets.sh data` places the files where the configs expect
them. SHA-256 checksums are listed in `SHA256SUMS`.

## 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}
}
```