--- library_name: "ofoldx" tags: - "biology" - "biomolecular-design" - "protein" - "rna" - "dna" - "pipeline" - "ligandmpnn" - "design-generation" - "protein-design" artifact_kind: "pipeline" repo_id: "oteam/ligandmpnn-noise030" license: "mit" pipeline_tag: "other" task: "design_generation" model-index: - name: "ligandmpnn-noise030" results: [] widget: - pipeline_tag: "other" task: "design_generation" example_title: "Backbone sequence design" text: "input_structure: backbone.cif\ndesign_chains: A" input_format: "structure_path" - pipeline_tag: "other" task: "design_generation" example_title: "Binder design" text: "target_structure: target.cif\ntarget_chains: A\ndesign_chains: B" input_format: "structure_path" --- # ligandmpnn-noise030 OFoldX `pipeline` artifact for biomolecular design generation, using the `ligandmpnn` 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 sequence-design model with ligand/atom context features. Converted LigandMPNN sequence-design checkpoint with ligand and non-protein atom context. ### Model Provenance - **Upstream Project**: LigandMPNN - **Primary Paper**: [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1) - **Upstream License**: MIT for upstream LigandMPNN code and model parameters ### Model Specification | Field | Value | | ----- | ----- | | Repository | `oteam/ligandmpnn-noise030` | | Artifact Kind | `pipeline` | | Task | `design_generation` | | Architecture | `ligandmpnn` | | Entrypoint | `ofoldx.pipelines.design.DesignPipeline` | > [!NOTE] > Checkpoint metadata: `k_neighbors=32`, `atom_context_num=25`; the `noiseXXX` suffix identifies the training-noise variant. ### Links - **Hub repository**: [oteam/ligandmpnn-noise030](https://huggingface.co/oteam/ligandmpnn-noise030) - **Upstream paper**: [Atomic context-conditioned protein sequence design using LigandMPNN](https://doi.org/10.1038/s41592-025-02626-1) - **Upstream repository**: [LigandMPNN](https://github.com/dauparas/LigandMPNN) - **Code**: [`ofoldx/pipelines/design.py`](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/pipelines/design.py) - **Project repository**: [https://github.com/OTeam-AI4S/OFoldX](https://github.com/OTeam-AI4S/OFoldX) - **Issues**: [https://github.com/OTeam-AI4S/OFoldX/issues](https://github.com/OTeam-AI4S/OFoldX/issues) ## Usage The artifact depends on the [`ofoldx`](https://github.com/OTeam-AI4S/OFoldX) library. Install it with pip: ```bash pip install ofoldx ``` ### Pipeline Usage Load the artifact from `oteam/ligandmpnn-noise030` with the OFoldX task pipeline. Use `AutoModel` or `AutoProcessor` only when you need lower-level control: ```python from ofoldx.pipelines import Pipeline pipeline = Pipeline.from_pretrained("oteam/ligandmpnn-noise030") ``` When a matching processor is available, load it with `AutoProcessor.from_pretrained(...)` and pass the processed batch to the model. ### Interface - **Task**: `design_generation` - **Artifact kind**: `pipeline` - **Architecture**: `ligandmpnn` - **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 LigandMPNN was trained on PDB assemblies as of 2022-12-16 from X-ray crystallography or cryo-EM structures better than 3.5 A and shorter than 6,000 residues, with 30% sequence-identity clustering and non-protein atomic context such as ligands, nucleic acids, and metals. OFoldX does not redistribute the training set. ### Training Procedure Upstream LigandMPNN uses categorical cross-entropy sequence-design training with Adam, mixed precision/checkpointing, and released noisy-backbone checkpoint variants. OFoldX converts released LigandMPNN checkpoints into `model.safetensors`; it does not run LigandMPNN 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. ```bibtex @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](https://github.com/OTeam-AI4S/OFoldX/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.
