Boltz-1: Democratizing Biomolecular Interaction Modeling

Boltz-1 is an open-source model which predicts the 3D structure of proteins, rna, dna and small molecules; it handles modified residues, covalent ligands and glycans, as well as condition the generation on pocket residues. For more information about the model, see our [technical report](https://gcorso.github.io/assets/boltz1.pdf). ## Installation Install boltz with PyPI (recommended): ``` pip install boltz ``` or directly from GitHub for daily updates: ``` git clone https://github.com/jwohlwend/boltz.git cd boltz; pip install -e . ``` > Note: we recommend installing boltz in a fresh python environment ## Inference You can run inference using Boltz-1 with: ``` boltz predict input_path ``` Boltz currently accepts three input formats: 1. Fasta file, for most use cases 2. A comprehensive YAML schema, for more complex use cases 3. A directory containing files of the above formats, for batched processing To see all available options: `boltz predict --help` and for more informaton on these input formats, see our [prediction instructions](docs/prediction.md). ## Training If you're interested in retraining the model, see our [training instructions](docs/training.md). ## Contributing We welcome external contributions and are eager to engage with the community. Connect with us on our [Slack channel](https://boltz-community.slack.com/archives/C0818M6DWH2) to discuss advancements, share insights, and foster collaboration around Boltz-1. ## Coming very soon - [ ] Pocket conditioning support - [ ] More examples - [ ] Full data processing pipeline - [ ] Colab notebook for inference - [ ] Confidence model checkpoint - [ ] Support for custom paired MSA - [ ] Kernel integration ## License Our model and code are released under MIT License, and can be freely used for both academic and commercial purposes.