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.
# current version (updates daily):
pip install git+https://github.com/chaidiscovery/chai-lab.git
# version on pypi:
pip install chai_lab==0.2.0This 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.
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.pyFor 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:
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.
We provide a web server so you can test the Chai-1 model right from your browser, without any setup.
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.
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.
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==0.2.0
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}
}
See LICENSE.md.
To discuss commercial use of our models, reach us via email.