| --- |
| license: mit |
| library_name: onnxruntime |
| tags: |
| - browser |
| - onnx |
| - wasm |
| - webgpu |
| - protein-design |
| - proteinmpnn |
| - ligandmpnn |
| --- |
| |
| # MurrellLab webports |
|
|
| Browser-ready model assets for unofficial web ports maintained by MurrellLab. |
| This repository stores model files that are too large or unsuitable for the |
| ordinary Git history of the corresponding software repositories. |
|
|
| ## webProteinMPNN |
|
|
| `webProteinMPNN/` contains nine ONNX model bundles converted from the official |
| ProteinMPNN checkpoints. They support ProteinMPNN, solubleMPNN, and CA-only |
| ProteinMPNN inference through ONNX Runtime Web using WebAssembly or WebGPU. |
|
|
| These models were trained and released by the original ProteinMPNN authors. |
| MurrellLab converted the checkpoints into a browser-oriented graph format; it |
| did not train the underlying models. The source checkpoint SHA-256, upstream |
| commit, exported-file hashes, tensor dimensions, and atom mode are recorded in |
| each `manifest.json`. A collection-wide index is available at |
| `webProteinMPNN/catalog.json`. |
|
|
| Code, usage documentation, and validation details are maintained at |
| https://github.com/MurrellGroup/webProteinMPNN. |
|
|
| ### License and attribution |
|
|
| The upstream ProteinMPNN code and checkpoints are MIT licensed. The upstream |
| notice is preserved in `webProteinMPNN/LICENSE.ProteinMPNN`, with additional |
| provenance in `webProteinMPNN/THIRD_PARTY_NOTICES.md`. Please cite: |
|
|
| > Dauparas J, Anishchenko I, Bennett N, et al. Robust deep learning-based |
| > protein sequence design using ProteinMPNN. Science. 2022;378(6615):49-56. |
| > https://doi.org/10.1126/science.add2187 |
|
|
| ### Intended use and limitations |
|
|
| The bundles are intended for research use in protein sequence design from |
| protein backbone or CA-only coordinates. They preserve the behavior and |
| limitations of the corresponding upstream checkpoint; conversion does not |
| constitute new training or biological validation. Generated sequences require |
| independent computational and experimental evaluation. These files are not a |
| hosted inference API and are not suitable for clinical decision-making. |
|
|
| ## webLigandMPNN |
|
|
| `webLigandMPNN/` contains four LigandMPNN sequence-design bundles and one |
| learned side-chain packing bundle converted to ONNX for browser and Node.js |
| inference through ONNX Runtime Web. It contains converted web assets only; the |
| original PyTorch `.pt` checkpoints are not stored in this repository. |
|
|
| The models were trained and released by the original LigandMPNN authors. |
| MurrellLab converted the checkpoints into browser-oriented graphs; it did not |
| train the underlying models. Each bundle records the original checkpoint hash, |
| locked upstream revision, converted-file hashes, dimensions, validation status, |
| and applicable notices. A collection-wide index is available at |
| `webLigandMPNN/catalog.json`. |
|
|
| Code, usage documentation, and validation details are maintained at |
| https://github.com/MurrellGroup/webLigandMPNN. |
|
|
| ### License and attribution |
|
|
| The upstream LigandMPNN code and model parameters are MIT licensed. The locked |
| upstream license and additional attribution are preserved under |
| `webLigandMPNN/`. Packing geometry derived from OpenFold constants retains the |
| applicable Apache-2.0 license and source notice in the packing bundle. Please |
| cite the original LigandMPNN publication and repository as described by the |
| upstream project. |
|
|
| ### Intended use and limitations |
|
|
| These bundles are intended for research use in ligand-conditioned protein |
| sequence design and side-chain packing. Conversion does not constitute new |
| training or independent biological validation. Generated structures and |
| sequences require independent computational and experimental evaluation. These |
| files are static model assets, not a hosted inference API, and are not suitable |
| for clinical decision-making. |
|
|
