PREreviews of “Chai-1: Decoding the molecular interactions of life”

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Chai-1: Decoding the molecular interactions of life

by Chai Discovery, Jacques Boitreaud, Jack Dent, Matthew McPartlon, Joshua Meier, Vinicius Reis, Alex Rogozhnikov, and Kevin Wu

Posted: October 11, 2024
Server: bioRxiv
DOI: 10.1101/2024.10.10.615955

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.

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PREreview by Phyu Sin (Jessica) M. Myat et al.

Authored by Phyu Sin (Jessica) M. Myat, Stephanie Wankowicz, Morad C. Malek, and Alexandria Haynes

Summary
Much progress continues to be made in deep learning structure prediction methods, such as AlphaFold 3 (AF3), RoseTTAFold All-Atom, and Boltz-1. Here, the authors provide a new open-source structure prediction model called Chai-1 that is claimed to perform better than AF3 and RoseTTAFold…

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PREreviews of Chai-1: Decoding the molecular interactions of life

1 PREreview