Ligandmpnn Packer Noise002

ligandmpnn-packer-noise002

OFoldX pipeline artifact for protein side-chain packing, using the ligandmpnn-packer architecture.

Disclaimer

This model card was generated by the OFoldX team for an OFoldX pipeline artifact. The upstream model authors did not write this card unless explicitly stated otherwise.

OFoldX is pre-alpha research software. Check the source checkpoint, upstream release, and local validation before using the artifact for scientific or operational decisions.

Model Details

LigandMPNN side-chain-packing model for packing rotamers around a fixed sequence.

Converted LigandMPNN side-chain packing checkpoint.

Model Provenance

• Upstream Project: LigandMPNN
• Primary Paper: Atomic context-conditioned protein sequence design using LigandMPNN
• Upstream License: MIT for upstream LigandMPNN code and model parameters

Model Specification

Field	Value
Repository	oteam/ligandmpnn-packer-noise002
Artifact Kind	pipeline
Task	side_chain_packing
Architecture	ligandmpnn-packer
Entrypoint	ofoldx.pipelines.side_chain_packing.SideChainPackingPipeline

[!NOTE] Checkpoint metadata: k_neighbors=32, atom_context_num=16; the noiseXXX suffix identifies the training-noise variant.

Links

• Hub repository: oteam/ligandmpnn-packer-noise002
• Upstream paper: Atomic context-conditioned protein sequence design using LigandMPNN
• Upstream repository: LigandMPNN
• Code: ofoldx/pipelines/side_chain_packing.py
• Project repository: https://github.com/OTeam-AI4S/OFoldX
• Issues: https://github.com/OTeam-AI4S/OFoldX/issues

Usage

The artifact depends on the ofoldx library. Install it with pip:

pip install ofoldx

Pipeline Usage

Load the artifact from oteam/ligandmpnn-packer-noise002 with the OFoldX task pipeline. Use AutoModel or AutoProcessor only when you need lower-level control:

from ofoldx.pipelines import Pipeline

pipeline = Pipeline.from_pretrained("oteam/ligandmpnn-packer-noise002")

When a matching processor is available, load it with AutoProcessor.from_pretrained(...) and pass the processed batch to the model.

Interface

• Task: side_chain_packing
• Artifact kind: pipeline
• Architecture: ligandmpnn-packer
• Runtime files: manifest.json, config.json, and model.safetensors when present

Training Details

OFoldX did not train these weights. This repository contains a converted checkpoint and OFoldX runtime metadata for loading it.

Training Data

The side-chain packing checkpoint is distributed with the LigandMPNN model parameters and shares the upstream LigandMPNN release provenance.

Training Procedure

The upstream packer predicts side-chain chi torsions from backbone, ligand atoms, and sequence with a LigandMPNN-style architecture. OFoldX converts the released side-chain packing checkpoint; it does not run training.

Evaluation

OFoldX conversion reports and contract tests validate artifact structure and checkpoint loading. Task-level scientific evaluation should be checked against the corresponding upstream model release or paper.

Limitations

• This artifact is distributed for research use.
• Inputs must match the model-specific processor and expected biomolecular representation.
• OFoldX is pre-alpha, so APIs and artifact metadata may still change before a stable release.

Citation

Please cite the upstream LigandMPNN work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository.

@article{dauparas2025atomic,
  author = {Dauparas, Justas and Lee, Gyu Rie and Pecoraro, Robert and An, Linna and Anishchenko, Ivan and Glasscock, Cameron and Baker, David},
  title = {Atomic context-conditioned protein sequence design using LigandMPNN},
  journal = {Nature Methods},
  year = {2025},
  doi = {10.1038/s41592-025-02626-1}
}

Contact

Please use OFoldX GitHub issues for questions or comments about this model card.

License

The Hub license metadata, when present, reflects the source checkpoint or upstream project license. The OFoldX project license is not yet finalized. The source checkpoint is associated with the upstream license noted above: MIT for upstream LigandMPNN code and model parameters. Review both OFoldX and upstream terms before redistribution or production use.