Chai-1

Chai-1 is a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of benchmarks. Chai-1 enables unified prediction of proteins, small molecules, DNA, RNA, glycosylations, and more.

For more information on the model's performance and capabilities, see our technical report.

Installation

# version on pypi:
pip install chai_lab==0.3.1

# newest available version (updates daily to test features that weren't released yet):
pip install git+https://github.com/chaidiscovery/chai-lab.git

This Python package requires Linux, and a GPU with CUDA and bfloat16 support. We recommend using an A100 80GB or H100 80GB chip, but A10s and A30s should work for smaller complexes. Users have also reported success with consumer-grade RTX 4090.

Running the model

The model accepts inputs in the FASTA file format, and allows you to specify the number of trunk recycles and diffusion timesteps via the chai_lab.chai1.run_inference function. By default, the model generates five sample predictions, and uses embeddings without MSAs or templates.

The following script demonstrates how to provide inputs to the model, and obtain a list of PDB files for downstream analysis:

python examples/predict_structure.py

For more advanced use cases, we also expose the chai_lab.chai1.run_folding_on_context, which allows users to construct an AllAtomFeatureContext manually. This allows users to specify their own templates, MSAs, embeddings, and constraints. We currently provide an example of how to construct an embeddings context as well as an MSA context, and will be releasing helper methods to build template contexts soon.

Where are downloaded weights stored?

By default, weights are automatically downloaded and stored in /downloads (usually that's within site-packages). In cases where you want to control the download location (e.g. on a mounted drive in Docker), you can use the CHAI_DOWNLOADS_DIR envvar to control the download location. For example: ```bash CHAI_DOWNLOADS_DIR=/tmp/downloads python ./examples/predict_structure.py ```

How can MSAs be provided to Chai-1?

Chai-1 supports MSAs provided as an `aligned.pqt` file. This file format is similar to an `a3m` file, but has additional columns that provide metadata like the source database and sequence pairing keys. We provide code to convert `a3m` files to `aligned.pqt` files. For more information on how to provide MSAs to Chai-1, see [this documentation](examples/msas/README.md).

⚡ Try it online

We provide a web server so you can test the Chai-1 model right from your browser, without any setup.

Using experimental restraints

Chai-1 uniquely offers the ability to fold complexes with user-specified "restraints" as inputs. These restraints specify inter-chain contacts at various resolutions that are used to guide Chai-1 in folding the complex. See restraints documentation for details.

💬 Feedback

Found a 🐞? Please report it in GitHub issues.

We welcome community testing and feedback. To share observations about the model's performance, please reach via GitHub discussions, or via email.

🛠️ Development

We use devcontainers in development, which helps us ensure we work in identical environments. We recommend working inside a devcontainer if you want to make a contribution to this repository.

Devcontainers work on local Linux setup, and on remote machines over an SSH connection.

Status

Since this is an initial release, we expect to make some breaking changes to the API and are not guaranteeing backwards compatibility. We recommend pinning the current version in your requirements, i.e.:

chai_lab==0.3.1

Citations

If you find Chai-1 useful in your research or use any structures produced by the model, we ask that you cite our technical report:

@article{Chai-1-Technical-Report,
    title        = {Chai-1: Decoding the molecular interactions of life},
    author       = {{Chai Discovery}},
    year         = 2024,
    journal      = {bioRxiv},
    publisher    = {Cold Spring Harbor Laboratory},
    doi          = {10.1101/2024.10.10.615955},
    url          = {https://www.biorxiv.org/content/early/2024/10/11/2024.10.10.615955},
    elocation-id = {2024.10.10.615955},
    eprint       = {https://www.biorxiv.org/content/early/2024/10/11/2024.10.10.615955.full.pdf}
}

Licence

See LICENSE.md.

To discuss commercial use of our models, reach us via email.

chaidiscovery / chai-lab

readme