| --- |
| license: cc-by-4.0 |
| library_name: onnx |
| pipeline_tag: other |
| tags: |
| - protein-nmr |
| - chemical-shifts |
| - graph-neural-network |
| - painn |
| - onnx |
| --- |
| |
| # CAUSTIC model weights |
|
|
| Model parameters and the post-prediction calibrator for |
| [CAUSTIC](https://github.com/maxzinke/caustic-nmr), which predicts protein backbone |
| chemical shifts (H, HA, N, CA, CB, C') from a 3D structure. |
|
|
| **You do not need to download these by hand.** The same bytes are bundled inside the |
| `caustic-nmr` Python package (`pip install caustic-nmr==0.4.0`). This repository is |
| the citable, tagged, DOI-carrying home of the exact files a package version uses. |
|
|
| | File | Bytes | SHA-256 | |
| |---|---|---| |
| | `best_v2_carbons.onnx` | 3,045,952 | `ebc7bbc2fc59327a50384105207958948cd90d7b5c7ea1ec2906b473e02948b2` | |
| | `sa16_calibrator_v2.json` | 846 | `32d277df600a8e6b2f84e2f7ccaaaf6d9de1332664df61e51b36f31fdf267bf4` | |
|
|
| Tag `v0.4.0` of this repository corresponds to `caustic-nmr` 0.4.0. |
|
|
| ## What the files are |
|
|
| - `best_v2_carbons.onnx` — PaiNN equivariant graph neural network (741,024 parameters, |
| ONNX opset 17) trained on BMRB-linked experimental structures with carbon-aggressive |
| label-noise cleaning. Architecture, features and training recipe: |
| [docs/METHOD.md](https://github.com/maxzinke/caustic-nmr/blob/main/docs/METHOD.md). |
| - `sa16_calibrator_v2.json` — per-nucleus global offsets and cysteine CB modifiers applied |
| after prediction (the "slim" SA16 v2 calibrator). |
|
|
| Training data, split protocol and licences: |
| [docs/DATA.md](https://github.com/maxzinke/caustic-nmr/blob/main/docs/DATA.md). |
| Benchmark protocol and numbers: |
| [docs/BENCHMARKS.md](https://github.com/maxzinke/caustic-nmr/blob/main/docs/BENCHMARKS.md). |
|
|
| ## Licence |
|
|
| These files are released under **CC BY 4.0** (see |
| [LICENSE-WEIGHTS](https://github.com/maxzinke/caustic-nmr/blob/main/LICENSE-WEIGHTS)). |
| The package code is MIT. Attribution: *CAUSTIC model weights, Maximilian Zinke, 2026, |
| https://github.com/maxzinke/caustic-nmr*. |
|
|
| ## How to cite |
|
|
| Zinke, M. *CAUSTIC: conformation-aware uncertainty and shift prediction from protein |
| conformer ensembles*. Zenodo. https://doi.org/10.5281/zenodo.22213167 (concept DOI, resolves to the |
| latest version). Machine-readable metadata: |
| [CITATION.cff](https://github.com/maxzinke/caustic-nmr/blob/main/CITATION.cff). |
|
|
