Chai Discovery is an artificial intelligence biotechnology company applying generative models to molecular design, with a focus on de novo antibody discovery. Founded in 2024 by a team drawn from organizations including OpenAI, Meta FAIR, Stripe, and Google X, the company aims to move computational antibody design beyond binding prediction toward generating real, drug-like therapeutic candidates with atomic precision.
The company's flagship model, Chai-2, is described as a zero-shot generative platform for de novo antibody design. Chai Discovery has reported that Chai-2 can design full-length monoclonal antibodies, rather than only fragments, and that it achieved double-digit experimental success rates in laboratory validation, a substantial improvement over earlier computational approaches. The company has also reported that a large share of designed antibodies met common preclinical selection standards across many target antigens.
Chai Discovery is backed by OpenAI and a syndicate of life-sciences and technology investors. It raised approximately $30 million in seed funding, followed by a $70 million Series A announced in August 2025, and a $130 million Series B announced in December 2025 that valued the company at roughly $1.3 billion and brought total financing above $225 million. The Series B was co-led by Oak HC/FT and General Catalyst, with participation from Thrive Capital, OpenAI, and others.
The company's positioning centers on shortening the path from target to validated antibody candidate, which is one of the most expensive and time-consuming stages of therapeutic development. By generating candidates computationally and validating experimentally, Chai Discovery seeks to compress discovery timelines and expand the range of targets that can be addressed.
As an early-stage research-driven company, Chai Discovery operates in a domain where laboratory validation, regulatory pathways, and clinical outcomes ultimately determine impact. Organizations evaluating the company should weigh the strength of its reported experimental results against the long timelines and uncertainties inherent in drug discovery.