oteam/rfdiffusion-inpaint-seq-fold

RFdiffusion

Pre-trained model for protein backbone generation from contig specifications and optional structural motifs.

Disclaimer

This is an UNOFFICIAL implementation of the De novo design of protein structure and function with RFdiffusion by Joseph L. Watson, et al.

The OFFICIAL repository of RFdiffusion is at RosettaCommons/RFdiffusion.

The team releasing RFdiffusion did not write this model card for this model so this model card has been written by the OFoldX team.

Model Details

RFdiffusion reverses diffusion over protein residue frames with a RoseTTAFold-style denoiser and SE(3)-equivariant structure updates. Its sampler couples Gaussian C-alpha translation diffusion with IGSO(3) rotation diffusion. Sequence, MSA, pair, template, and timestep features provide model-specific conditioning.

Please refer to the Training Details section for more information on the training process.

Variants

• oteam/rfdiffusion-base: De novo generation and motif scaffolding (Base_ckpt.pt).
• oteam/rfdiffusion-base-epoch8: The earlier base checkpoint (Base_epoch8_ckpt.pt).
• oteam/rfdiffusion-complex-base: Complex and binder design (Complex_base_ckpt.pt).
• oteam/rfdiffusion-complex-beta: Topology-diverse, beta-rich binder design (Complex_beta_ckpt.pt).
• oteam/rfdiffusion-complex-fold-base: Fold-conditioned complex design (Complex_Fold_base_ckpt.pt).
• oteam/rfdiffusion-inpaint-seq: Sequence-conditioned inpainting (InpaintSeq_ckpt.pt).
• oteam/rfdiffusion-inpaint-seq-fold: Fold-conditioned inpainting (InpaintSeq_Fold_ckpt.pt).
• oteam/rfdiffusion-active-site: Active-site motif scaffolding (ActiveSite_ckpt.pt).

Model Specification

Variant	Main Blocks	Extra Blocks	Refinement Blocks	MSA Size	Pair Size	Diffusion Timesteps	Num Parameters (M)
Base, complex, inpaint and active-site	32	4	4	256	128	50	59.879
Fold-conditioned	32	4	4	256	128	50	59.880

Links

• Code: ofoldx.models.rfdiffusion
• Paper: De novo design of protein structure and function with RFdiffusion
• Developed by: Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker
• Model type: SE(3)-equivariant protein backbone diffusion model
• Original Repository: RosettaCommons/RFdiffusion

Usage

The model file depends on the ofoldx library. You can install it using pip:

pip install ofoldx

Direct Use

Backbone Design

You can use this model directly with a pipeline for 100-residue protein backbone generation:

import ofoldx
from ofoldx.models.conditions import GeneratedSegment
from ofoldx.models.rfdiffusion import RfDiffusionPlacements

placement = RfDiffusionPlacements({"binder": {"binder": GeneratedSegment("protein", 100, 100)}})
pipeline = ofoldx.pipeline("design", model="oteam/rfdiffusion-base")
result = pipeline(None, placement=placement, seed=7)

Generated residues have the unknown identity UNK. Use a sequence-design model such as ProteinMPNN to assign amino acids.

Training Details

RFdiffusion is trained to recover protein backbones from noisy coordinates with optional structural conditioning.

Training Data

RFdiffusion is fine-tuned from RoseTTAFold on protein structures from the PDB, including monomers and protein complexes.

Training Procedure

RFdiffusion learns to denoise protein residue frames with self-conditioning across diffusion steps.

Citation

@article{watson2023denovo,
  author = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. 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}
}

The artifacts distributed in this repository are part of the OFoldX project. If OFoldX supports your research, please cite the OFoldX project as follows:

@software{chen2026ofoldx,
  author = {Chen, Zhiyuan},
  title  = {OFoldX},
  year   = {2026},
  url    = {https://github.com/OTeam-AI4S/OFoldX},
}

Contact

Please use GitHub issues of OFoldX for any questions or comments on the model implementation or model card.

Please contact the authors of the RFdiffusion paper for questions or comments on the paper/model.

License

The model weights, implementation, configuration, and documentation distributed by OFoldX are licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

SPDX-License-Identifier: AGPL-3.0-or-later