jwohlwend/boltz
Official repository for the Boltz biomolecular interaction models
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 422 days ago, with 63 contributors on GitHub.
Use Cases & Benefits
- Provides deep learning models for predicting biomolecular interactions and binding affinities with high accuracy and speed.
- Delivers near physics-based free-energy perturbation accuracy while running 1000x faster, enabling practical early-stage drug discovery.
- Use for screening potential drug candidates by predicting binding probabilities to identify promising molecular binders.
- Use for optimizing lead compounds by accurately estimating binding affinity changes from small molecular modifications.
- Use for academic or commercial research requiring open-source, state-of-the-art biomolecular interaction modeling and prediction.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
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
or directly from GitHub for daily updates:
If you are installing on CPU-only or non-CUDA GPus hardware, remove
[cuda]from the above commands. Note that the CPU version is significantly slower than the GPU version.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
913+39
Open issues
139-19
Compared with peers in Backend
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| jwohlwend/boltz | 4.3K | — | 35 | 422 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
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