TT-Boltz

Original Repo | Boltz-1 | Boltz-2

Introduction

TT-Boltz is the Boltz-2 fork that runs on a single Tenstorrent Blackhole or Wormhole.

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.

For an intuitive understanding of AlphaFold 3, I recommend The Illustrated AlphaFold.

Installation

Clone

git clone https://github.com/moritztng/tt-boltz.git
cd tt-boltz

Create Virtual Environment

python3 -m venv env
source env/bin/activate

Build TT-Metal from Source

Don't install tt-nn with ./create_venv.sh.

Tenstorrent Installation Guide

Install TT-NN

pip config set global.extra-index-url https://download.pytorch.org/whl/cpu
pip install setuptools wheel==0.45.1
pip install -r <path-to-tt-metal-repo>/tt_metal/python_env/requirements-dev.txt
pip install -e <path-to-tt-metal-repo>

Install TT-Boltz

pip install -e .

You can ignore the version warnings.

Inference

You can run inference using Boltz with:

boltz predict examples/prot.yaml --use_msa_server --override --accelerator=tenstorrent

Pass --accelerator=tenstorrent to run Boltz-2 on Tenstorrent Blackhole or Wormhole. Boltz-1 is not supported on Tenstorrent hardware anymore.

The 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.

Binding Affinity Prediction

There are two main predictions in the affinity output: affinity_pred_value and affinity_probability_binary. They are trained on largely different datasets, with different supervisions, and should be used in different contexts. The affinity_probability_binary field should be used to detect binders from decoys, for example in a hit-discovery stage. It's value ranges from 0 to 1 and represents the predicted probability that the ligand is a binder. The affinity_pred_value aims to measure the specific affinity of different binders and how this changes with small modifications of the molecule. This should be used in ligand optimization stages such as hit-to-lead and lead-optimization. It reports a binding affinity value as log(IC50), derived from an IC50 measured in μM. More details on how to run affinity predictions and parse the output can be found in our prediction instructions.

Current Runtime

We didn't even start writing low-level code yet and Tenstorrent Blackhole was just released. I'm confident we'll get to 2 minutes.

Hardware	~Minutes
AMD Ryzen 5 8600G	45
Nvidia T4	9
Tenstorrent Wormhole n300	3
Tenstorrent Blackhole p150	1.5
Nvidia RTX 4090	1

Evaluation

To encourage reproducibility and facilitate comparison with other models, we provide the evaluation scripts and predictions for Boltz-1, Chai-1 and AlphaFold3 on our test benchmark dataset as well as CASP15. These datasets are created to contain biomolecules different from the training data and to benchmark the performance of these models we run them with the same input MSAs and same number of recycling and diffusion steps. More details on these evaluations can be found in our evaluation instructions.

Community

• I build in public on X
• Boltz Slack
• Tenstorrent Discord

Cite

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 = {10.1101/2025.06.14.659707},
  journal = {bioRxiv}
}

@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},
}