OpenAI-backed Chai Discovery wins $130 million, claiming a “100x improvement” over previous computational design methods.
AI-native drug discovery platform Chai Discovery raised $130 million in Series B funding that values the San Francisco-based company at $1.3 billion. Co-led by Oak HC/FT and General Catalyst, with participation from existing backers including OpenAI, Thrive Capital, Menlo Ventures and Dimension, the round brings the company’s total raised to over $225 million, less than two years later. came out of stealth.
Just a few months ago, Chai announced a $70 million, Series A and unveiled Chai-2, a generative platform designed to replace the slow, iterative experimentation that dominates drug discovery with a computational design process that can move directly from a biological target to a viable drug candidate. The platform leverages a zero-shot model, generating new antibody sequences from scratch based solely on a target, without relying on known examples or extensive screening. According to the company, this approach has produced double-digit experimental success rates in antibody design – said to be a “100-fold improvement” over previous computational methods.
“We stand on the precipice of a new era for the biopharmaceutical industry,” said Josh Meier, CEO of Chai.
The idea that biology can be treated as an information problem is central to Chai’s approach. The company builds large-scale AI models to predict and reprogram interactions between biochemical molecules, including proteins, antibodies, nucleic acids and small molecules. These interactions underlie almost all biological processes and the ability to precisely target them opens the door to therapies targeting complex pathways that have historically resisted conventional drug development.
Many of the most pressing unmet needs in medicine today relate to aging and age-related chronic diseases, whose targets are often complex, poorly understood, or embedded in complex biological networks. Neurodegeneration, metabolic diseases, fibrosis, and immune dysfunction all involve molecular interactions that have proven difficult to modulate safely and precisely. By enabling the rational design of novel antibodies and other biologics against such targets, Chai’s platform could potentially support the development of next-class therapies aimed at altering the underlying biology of aging rather than simply managing symptoms.
Chai reports that his newest models can design molecules with the properties expected of real drugs, including stability and manufacturability. These statements were recently explored in a preprint describing the application of Chai-2 to full-length monoclonal antibodies. In this work, the majority of designed antibodies demonstrated developability profiles comparable to those of approved therapeutics, and the experimentally determined structures closely matched the atomic-level predictions of the models.
“We are impressed by the speed of progress on the models: what seemed like five-year-old problems only a few months ago are now resolved in a matter of weeks,” Meier said. “Our latest models can design molecules with the properties we would expect from real drugs, and tackle difficult targets that have been out of reach. These models will spark a new wave of first-in-class, best-in-class therapeutics, and early adopters in pharmaceuticals will be the big winners.”
The new funding will be used to accelerate research and product development and expand commercialization efforts. Chai said his vision is to develop a “computer-aided design suite” for molecules, analogous to the role CAD software plays in engineering and manufacturing.
Ultimately, Chai says his approach can “significantly reduce the time needed for first-in-human studies, tackle hard-to-treat and ‘undruggable’ targets, and accelerate the overall time to market.” » If its models continue to deliver on their promise, the ability to design drug-like molecules quickly and reliably could reshape the way therapies for aging and chronic diseases are designed.