Boltz is a family of models for biomolecular interaction prediction. Boltz-1 was the first fully open source model to approach AlphaFold3 accuracy. Our latest work Boltz-2 is a new biomolecular foundation model that goes beyond AlphaFold3 and Boltz-1 by jointly modeling complex structures and binding affinities, a critical component towards accurate molecular design. Boltz-2 is the first deep learning model to approach the accuracy of physics-based free-energy perturbation (FEP) methods, while running 1000x faster — making accurate in silico screening practical for early-stage drug discovery.
All the code and weights are provided under MIT license, making them freely available for both academic and commercial uses. For more information about the model, see the Boltz-1 and Boltz-2 technical reports. To discuss updates, tools and applications join our Slack channel.
Note: we recommend installing boltz in a fresh python environment
Install boltz with PyPI (recommended):
pip install boltz -U
or directly from GitHub for daily updates:
git clone https://github.com/jwohlwend/boltz.git
cd boltz; pip install -e .
You can run inference using Boltz with:
boltz predict input_path --use_msa_server
input_path should point to a YAML file, or a directory of YAML files for batched processing, describing the biomolecules you want to model and the properties you want to predict (e.g. affinity). To see all available options: boltz predict --help and for more information on these input formats, see our prediction instructions. By default, the boltz command will run the latest version of the model.
To encourage reproducibility and facilitate comparison with other models, on top of the existing Boltz-1 evaluation pipeline, we will soon provide the evaluation scripts and structural predictions for Boltz-2, Boltz-1, Chai-1 and AlphaFold3 on our test benchmark dataset, and our affinity predictions on the FEP+ benchamark, CASP16 and our MF-PCBA test set.
If you're interested in retraining the model, currently for Boltz-1 but soon for Boltz-2, see our training instructions.
We welcome external contributions and are eager to engage with the community. Connect with us on our Slack channel to discuss advancements, share insights, and foster collaboration around Boltz-2.
Boltz also runs on Tenstorrent hardware thanks to a fork by Moritz Thüning.
Our model and code are released under MIT License, and can be freely used for both academic and commercial purposes.
If you use this code or the models in your research, please cite the following papers:
@article{passaro2025boltz2,
author = {Passaro, Saro and Corso, Gabriele and Wohlwend, Jeremy and Reveiz, Mateo and Thaler, Stephan and Somnath, Vignesh Ram and Getz, Noah and Portnoi, Tally and Roy, Julien and Stark, Hannes and Kwabi-Addo, David and Beaini, Dominique and Jaakkola, Tommi and Barzilay, Regina},
title = {Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction},
year = {2025},
doi = {},
journal = {}
}
@article{wohlwend2024boltz1,
author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},
title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},
year = {2024},
doi = {10.1101/2024.11.19.624167},
journal = {bioRxiv}
}In addition if you use the automatic MSA generation, please cite:
@article{mirdita2022colabfold,
title={ColabFold: making protein folding accessible to all},
author={Mirdita, Milot and Sch{\"u}tze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin},
journal={Nature methods},
year={2022},
}
