Chai-1: Decoding the molecular interactions of life

By Jacques Boitreaud, Jack Dent, Matt McPartlon, Joshua Meier, Vinicius Reis, Alex Rogozhnikov, Kevin Wu. bioRxiv, 2024

394 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/d6352ba03f73056e7b0dbfab3adab6f367866827

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Citations in the corpus

• Boltz-1 Democratizing Biomolecular Interaction Modeling (2024, positive): “Baselines We evaluate our performance against AlphaFold3 [Abramson et al., 2024] and Chai-1 [Chai et al., 2024], current state-of-the-art biomolecular structure prediction models that were released under an exclusive commercial license and do not have training code and pipelines available.”