Chai-1
Multimodal foundation model for molecular structure prediction.
About
Chai-1, released September 9, 2024 by Chai Discovery (founded by ex-OpenAI and Meta researchers, backed by OpenAI and Thrive Capital), is a multi-modal foundation model for molecular structure prediction. It folds proteins together with small-molecule ligands, glycans, DNA, and RNA, predicting joint 3D structures from sequence — and can run in single-sequence mode without multiple sequence alignments while preserving most of its performance. The company reports state-of-the-art results across drug-discovery-relevant tasks, with optional experimental restraints boosting performance by double-digit percentage points.
Limitations: the downloadable weights and inference code are released for non-commercial use; commercial drug-discovery use goes through the free web interface. As with all structure predictors, accuracy varies by target class and available evolutionary information.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 63; Trending score 74/100 from researcher assessment.