We’re excited to release Chai-1, a new multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 enables unified prediction of proteins, small molecules, DNA, RNA, covalent modifications, and more.
The model is available for free via a web interface, including for commercial applications such as drug discovery. We are also releasing the model weights and inference code as a software library for non-commercial use.

We tested Chai-1 across a large number of benchmarks, and found that the model achieves a 77% success rate on the PoseBusters benchmark (vs. 76% by AlphaFold3), as well as an Cα LDDT of 0.849 on the CASP15 protein monomer structure prediction set (vs. 0.801 by ESM3-98B).

Chai-1 is the result of a few months of intense work, and yet we are only at the starting line. Our broader mission at Chai Discovery is to transform biology from science into engineering. To that end, we'll be building further AI foundation models that predict and reprogram interactions between biochemical molecules, the fundamental building blocks of life. We’ll have more to share on this soon.

We are grateful for the partnership of Dimension, Thrive Capital, OpenAI, Conviction, Neo, Lachy Groom, and Amplify Partners, as well as Anna and Greg Brockman, Blake Byers, Fred Ehrsam, Julia and Kevin Hartz, Will Gaybrick, David Frankel, R. Martin Chavez, and many others.

This Python package requires Linux, and a GPU with CUDA and bfloat16 support

(we recommend A100/H100, but A10, A30 should work for smaller complexes. Users reported success with consumer-grade RTX 4090).