Abstract
We introduce Chai-1, a 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 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes.
Competing Interest Statement
The authors are employees of Chai Discovery and may hold shares in Chai Discovery.
Footnotes
This revision corrects spelling of author names. All other content is the same.
3 Values are taken from AlphaFold3’s publicly released PoseBusters predictions; we did not run AF3 ourselves.