--- library_name: "ofoldx" tags: - "biology" - "biomolecular-design" - "protein" - "rna" - "dna" - "pipeline" - "rfdiffusion" - "design-generation" - "protein-design" artifact_kind: "pipeline" repo_id: "oteam/rfdiffusion-complex-base" license: "bsd-3-clause" pipeline_tag: "other" task: "design_generation" model-index: - name: "rfdiffusion-complex-base" results: [] widget: - pipeline_tag: "other" task: "design_generation" example_title: "Backbone generation" text: "contig: 100-150\nhotspot_residues: []" input_format: "structure_path" - pipeline_tag: "other" task: "design_generation" example_title: "Motif scaffolding" text: "input_structure: motif.cif\ncontig: A10-25/80-120" input_format: "structure_path" --- # rfdiffusion-complex-base OFoldX `pipeline` artifact for biomolecular design generation, using the `rfdiffusion` 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 RFdiffusion backbone-generation model for protein design and motif scaffolding. Converted RFdiffusion checkpoint for backbone generation, motif scaffolding, and binder design. ### Model Provenance - **Upstream Project**: RFdiffusion - **Source Release**: [https://github.com/RosettaCommons/RFdiffusion/blob/main/scripts/download_models.sh](https://github.com/RosettaCommons/RFdiffusion/blob/main/scripts/download_models.sh) - **Primary Paper**: [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8) - **Upstream License**: BSD for upstream RFdiffusion code and referenced model weights ### Model Specification | Field | Value | | ----- | ----- | | Repository | `oteam/rfdiffusion-complex-base` | | Artifact Kind | `pipeline` | | Task | `design_generation` | | Architecture | `rfdiffusion` | | Entrypoint | `ofoldx.pipelines.design.DesignPipeline` | ### Links - **Hub repository**: [oteam/rfdiffusion-complex-base](https://huggingface.co/oteam/rfdiffusion-complex-base) - **Upstream paper**: [De novo design of protein structure and function with RFdiffusion](https://doi.org/10.1038/s41586-023-06415-8) - **Upstream repository**: [RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) - **Source checkpoint release**: [https://github.com/RosettaCommons/RFdiffusion/blob/main/scripts/download_models.sh](https://github.com/RosettaCommons/RFdiffusion/blob/main/scripts/download_models.sh) - **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/rfdiffusion-complex-base` 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/rfdiffusion-complex-base") ``` 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**: `rfdiffusion` - **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 RFdiffusion fine-tunes a RoseTTAFold-style structure network as a denoising diffusion model over PDB protein structures. OFoldX does not redistribute the training set. ### Training Procedure Upstream RFdiffusion noises residue frames with Gaussian C-alpha translation noise and rotational Brownian motion, trains denoising to true frames with self-conditioning, and uses checkpoint-specific inference configs. OFoldX converts released RFdiffusion checkpoints into `model.safetensors`; it does not run RFdiffusion 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 RFdiffusion work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository. ```bibtex @article{watson2023denovo, author = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. and Trippe, Brian L. and Yim, Jason and Eisenach, Helen E. and Ahern, Woody and Borst, Andrew J. and Ragotte, Robert J. and Milles, Lukas F. and others}, title = {De novo design of protein structure and function with RFdiffusion}, journal = {Nature}, volume = {620}, number = {7976}, pages = {1089--1100}, year = {2023}, doi = {10.1038/s41586-023-06415-8} } ``` ## 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: BSD for upstream RFdiffusion code and referenced model weights. Review both OFoldX and upstream terms before redistribution or production use.
