Chai Discovery is building a computational design suite that uses diffusion models and scaling laws to achieve high hit rates (15% vs 0.1% prior) and enable de novo molecular design, turnin…

Chai Discovery's models achieve 15% binding success rates versus 0.1% industry standard, enabling zero-shot molecule generation and dramatic acceleration from 9-month discovery cycles to we…

Applying the bitter lesson — scaling compute, data, and model simplicity — to protein design has yielded step-function improvements in antibody binding rates, suggesting drug discovery foll…

The founders apply the 'bitter lesson' to molecular design, emphasizing simplicity, scaling laws, and rigorous evaluation. They believe scaling compute, data, and model size will unlock pre…

Choosing infrastructure/platform model over full-stack drug development creates a flywheel with pharma partners (Eli Lilly, Novartis, Pfizer) that forces model rigor, scales revenue to fund…

Partnering with demanding pharma customers forces model rigor and generalization across diverse targets, creating a more reliable path to impact than full-stack drug development where limit…

Pharma companies like Eli Lilly and Novartis are rigorous validators but adopt AI quickly once models prove effective, driven by patent cliffs and competitive pressure to maintain pipelines…

As models reach sufficient accuracy to design testable molecules, the resulting lab data — both successes and failures — becomes high-quality training data for next-gen models, mirroring th…