CAUSTIC model weights
Model parameters and the post-prediction calibrator for CAUSTIC, 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.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. Benchmark protocol and numbers: docs/BENCHMARKS.md.
Licence
These files are released under CC BY 4.0 (see 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.