Protenix

Pre-trained model for all-atom structure prediction of proteins, nucleic acids, small molecules, and their complexes.

Disclaimer

This is an UNOFFICIAL implementation of the Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction by Xinshi Chen, Yuxuan Zhang, et al.

The OFFICIAL repository of Protenix is at bytedance/Protenix.

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

Model Details

Protenix combines atom and token embeddings with an MSA-conditioned recycled Pairformer trunk, EDM coordinate diffusion, and distogram and confidence heads. Template, structure-conditioned, and constraint variants provide additional structural evidence; the constraint model supports atom contacts, token contacts, pockets, and substructures.

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

Variants

oteam/protenix: The default v1.0 all-atom structure predictor.
oteam/protenix-20250630: The v1.0 model trained on a later data cutoff.
oteam/protenix-v0.5: The v0.5 base model, also available as oteam/protenix-base.
oteam/protenix-constraint: The v0.5 model with constraint conditioning.
oteam/protenix-mini-tmpl: The v0.5 mini model with structure conditioning.
Protenix-v2: The larger template-conditioned model, using locally converted parameters. Select the protenix-v2 checkpoint identifier when converting its weights.

Model Specification

Variant	Pairformer Blocks	MSA Stages	Single Size	Pair Size	Recycling Steps	Num Parameters (M)
Base v0.5	48	4	384	128	10	368.123
Constraint v0.5	48	4	384	128	10	368.339
Template v1	48	4	384	128	10	368.549
Mini v0.5	16	1	384	128	4	134.102
Mini ESM / ISM v0.5	16	1	384	128	4	2967.899
Structure condition v0.5	16	1	384	128	4	134.212
Tiny v0.5	8	1	384	128	4	109.535
Protenix-v2	48	4	384	256	10	464.535

The Mini ESM and ISM counts include the frozen ESM2 encoder.

Usage

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

pip install ofoldx

Direct Use

Structure Prediction

You can use this model directly with a pipeline for all-atom structure prediction:

import ofoldx
from ofoldx.io import read_structure

structure, report = read_structure("input.cif")
pipeline = ofoldx.pipeline("fold", model="oteam/protenix")
result = pipeline(structure, seed=7)

The result contains predicted structures and confidence values. Atoms must have known elements and formal charges.

Downstream Use

Chemical Guidance

You can enable training-free chemical guidance during sampling:

import ofoldx
from ofoldx.io import read_structure
from ofoldx.models.protenix import ProtenixTfgGuidance

structure, report = read_structure("input.cif")
pipeline = ofoldx.pipeline("fold", model="oteam/protenix")
result = pipeline(structure, tfg_guidance=ProtenixTfgGuidance(), seed=7)

Guidance is disabled by default and can be configured through ProtenixTfgGuidance.

Template repositories accept templates=. Structure-conditioned repositories accept structure_condition= with an in-memory structure. Constraint repositories accept atom_contacts, token_contacts, pocket, and substructure=. The mini template checkpoint uses the structure-conditioned graph, rather than the ordinary template graph.

Training Details

Protenix is trained with coordinate, geometric, distogram, and confidence objectives in a staged training program.

Training Data

The Protenix training data includes biomolecular structures from the PDB and AlphaFold 2 predictions used for distillation. The upstream training guide describes data preparation.

Training Procedure

Protenix is trained in stages with coordinate denoising, geometric, distogram, and confidence objectives.

Citation

@article{protenix2025advancing,
  author = {ByteDance AML AI4Science Team and Chen, Xinshi and Zhang, Yuxuan and others},
  title = {Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction},
  year = {2025},
  doi = {10.1101/2025.01.08.631967},
  journal = {bioRxiv}
}

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 Protenix 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

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