BoltzGen

Pre-trained model for protein and peptide binder design through joint sequence and structure generation, optional sequence redesign, and refolding-based evaluation.

Disclaimer

This is an UNOFFICIAL implementation of BoltzGen: Toward Universal Binder Design by Hannes Stark, Felix Faltings, et al.

The OFFICIAL repository of BoltzGen is at HannesStark/boltzgen.

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

Model Details

BoltzGen is an all-atom diffusion model for biomolecular binder design. A recycled Pairformer combines atom, token, MSA, template, contact, binding-site and secondary-structure features before an EDM denoiser generates sequence and atomic coordinates together. A separate inverse-folding model can redesign the sequence on the generated backbone.

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

Variants

• oteam/boltzgen: The complete binder design and evaluation workflow.
• oteam/boltzgen-diverse and oteam/boltzgen-adherence: Independently callable structure generators.
• oteam/boltzgen-structure-small: A Miniformer structure generator.
• oteam/boltzgen-inversefolding: The BoltzIF model for sequence assignment on fixed backbones.

Model Specification

Variant	Representation Size	Trunk	Denoiser / Decoder Blocks	Num Parameters (M)
Diverse / Adherence	384 / 128	64 Pairformer + 4 MSA Blocks	24 token Blocks	482.213
Structure-trained small	384 / 128	12 Miniformer + 3 MSA Blocks	8 token Blocks	136.886
Inverse folding	128 / 128	6 graph encoder Blocks, 30 neighbors	3 graph decoder Blocks	3.069

Links

• Code: ofoldx.models.boltzgen
• Paper: BoltzGen: Toward Universal Binder Design
• Developed by: Hannes Stark, Felix Faltings, MinGyu Choi, Yuxin Xie, Eunsu Hur, Timothy O'Donnell, Anton Bushuiev, Talip Uçar, Saro Passaro, Weian Mao, Mateo Reveiz, Roman Bushuiev, Tally Portnoi, Tomáš Pluskal, Josef Sivic, Karsten Kreis, Arash Vahdat, Shamayeeta Ray, Jonathan T. Goldstein, Andrew Savinov, Jacob A. Hambalek, Anshika Gupta, Diego A. Taquiri-Diaz, Yaotian Zhang, Samuel J. Snyder, A. Katherine Hatstat, Angelika Arada, Nam Hyeong Kim, Haoyu Fan, Ethel Tackie-Yarboi, Dylan Boselli, Lee Schnaider, Chang C. Liu, Gene-Wei Li, Denes Hnisz, David M. Sabatini, William F. DeGrado, Jeremy Wohlwend, Gabriele Corso, Regina Barzilay, and Tommi Jaakkola
• Model type: Constraint-guided all-atom biomolecular diffusion and inverse folding
• Original Repository: HannesStark/boltzgen

Usage

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

pip install ofoldx

Direct Use

Biomolecular Design

You can use this model directly with a pipeline for binder design with the protein-anything protocol:

import ofoldx
from ofoldx.io import read_structure

structure, _ = read_structure("target.cif")
pipeline = ofoldx.pipeline("design", model="oteam/boltzgen")
result = pipeline(structure, protocol="protein-anything", total_len=120, num_candidates=20, seed=7)
best = result.ranked_candidates[0]

The result contains the generated candidates, their evaluation scores, and ranked and diversity-selected views. Screening uses the atoms present in each structure. For single-chain redesign, interface scores do not describe an actual interface.

The other released protocols are peptide-anything, protein-small_molecule, nanobody-anything, antibody-anything and protein-redesign. The default inverse folder is SolubleMPNN, or BoltzIF for protein-small_molecule. Use inverse_fold_model="boltzif" to select BoltzIF or inverse_fold_model=None to retain the generated sequence and coordinates without inverse folding.

RNA and DNA can be fixed context but cannot be generated segments. When inverse folding changes a residue's identity, the new side-chain coordinates remain unavailable until a later structure prediction.

Downstream Use

Partial Workflows

Use design_backbones() for joint sequence and structure generation or inverse_fold() for sequence redesign without evaluation. These operations return candidates in generation order; only the complete design() call provides analysis and ranking. The full and Miniformer structure-generation pipelines and BoltzIF are also independently callable.

See BoltzGenPipeline for protocol and selection options.

Training Details

BoltzGen trains coordinate generation with EDM denoising and inverse folding with a separate structure-conditioned sequence objective.

Training Data

BoltzGen uses experimental structures from the Protein Data Bank and predicted structures from AlphaFold DB and Boltz-1, including protein–ligand, RNA and protein–DNA systems.

Training Procedure

The structure designer learns to denoise cropped structures with sampled design regions and conditioning information. The inverse-folding model learns to assign sequences conditioned on molecular structures. OFoldX provides diffusion_loss for training and fine-tuning.

Citation

@article{stark2025boltzgen,
  author = {Stark, Hannes and Faltings, Felix and Choi, MinGyu and others},
  title = {BoltzGen: Toward Universal Binder Design},
  year = {2025},
  doi = {10.1101/2025.11.20.689494},
  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 BoltzGen 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