| --- |
| license: apache-2.0 |
| library_name: pytorch |
| inference: false |
| tags: |
| - nmr |
| - nmr-spectroscopy |
| - spectroscopy |
| - chemistry |
| - cheminformatics |
| - deformable-detr |
| - object-detection |
| --- |
| |
| # MolDeTr — chemistry-informed deep learning for ¹H NMR multiplet detection |
|
|
| MolDeTr is a 1D Deformable-DETR that reads a ¹H NMR spectrum window and returns the spin systems in it |
| directly: for each group of equivalent protons it gives the chemical shift (δ), the coupling (J), the |
| proton count, and the line width — in one forward pass, with no prior structure and no iterative fitting. |
|
|
| - **Paper:** [*Analytical Chemistry*, 2026](https://doi.org/10.1021/acs.analchem.5c03465) |
| - **Code:** <https://github.com/smidooo/MolDeTr> |
| - **Canonical release (weights + data):** Zenodo [10.5281/zenodo.21217101](https://doi.org/10.5281/zenodo.21217101). **This repo mirrors the checkpoint from that deposit for convenience — Zenodo is authoritative.** |
|
|
| ## What's here |
|
|
| One file: `model_spin_system_ABCDEFG_exp2.pth` (~974 MB), the trained checkpoint. It is byte-identical to |
| the file in the Zenodo deposit (MD5 `faf842d1a1d8beae67e0544e28f226b5`). |
|
|
| ## Usage |
|
|
| The model is custom (a 1D detection transformer), so it runs through the repo code rather than a standard |
| `transformers` pipeline: |
|
|
| ```bash |
| git clone https://github.com/smidooo/MolDeTr && cd MolDeTr |
| pip install -e . |
| huggingface-cli download smidooo/moldetr model_spin_system_ABCDEFG_exp2.pth --local-dir moldetr/model |
| python scripts/predict.py --demo # or: python app.py (Gradio Detect + Simulate app) |
| ``` |
|
|
| See the [repository README](https://github.com/smidooo/MolDeTr) for the input contract (a 6144-point, |
| 5.12 points/Hz, 1200 Hz window) and the interactive app. |
|
|
| ## Benchmark |
|
|
| On the experimental test set (13 ROIs across 12 spectra, 80–600 MHz, vs. ground truth): median |Δδ| |
| **0.89 Hz**, median |ΔJ| **0.20 Hz**, and **93.5 %** proton-count accuracy. |
|
|
| ## License & citation |
|
|
| Apache-2.0. If MolDeTr helps your work, please [cite the paper](https://doi.org/10.1021/acs.analchem.5c03465). |
|
|
