Chai Discovery unveils Chai-3, advancing AI drug design capabilities

Designing a drug used to be a process measured in years and billions of dollars. Chai Discovery thinks it can compress meaningful chunks of that timeline into weeks. The San Francisco-based biotech company has released Chai-3, a generative AI model for molecular design that roughly doubles the success rate of its predecessor in creating antibodies that actually work as therapeutics.

Where Chai-2 produced viable binding affinities at a rate that left significant room for improvement, Chai-3 hits about 50% effectiveness for therapeutic candidates. In a field where single-digit success rates are considered normal at the discovery stage, that jump is substantial.

From model upgrade to Pfizer’s front door

The model’s capabilities didn’t stay theoretical for long. In early June 2026, Pfizer signed a licensing agreement to gain early access to Chai-3, along with a custom version of the model trained on Pfizer’s proprietary data.

Pfizer isn’t the only big name in Chai Discovery’s orbit. The company announced a deal with Eli Lilly in January 2026 focused on biologics discovery, and it has also established a partnership with Novartis.

Chai-3 shows particular improvement in generating multi-specific molecules, which are proteins engineered to bind to more than one target simultaneously. These are among the most technically challenging biologics to design, and they represent a growing share of the therapeutic pipeline across the industry.

Follow the money

On July 14, 2026, Chai Discovery closed a $400 million Series C funding round at a $3.8 billion valuation. Index Ventures led the round, with Kleiner Perkins and Sequoia among the participants.

The fundraise represents a significant step up from the company’s previous $130 million Series B, which closed in December 2025. In roughly seven months, Chai Discovery tripled the size of its raise.

Chai Discovery was founded in 2024 by Joshua Meier, Jack Dent, Matthew McPartlon, and Jacques Boitreaud. The founding team brings experience from a mix of tech and biotech organizations, including OpenAI, Absci, and Stripe.

What the compressed timeline actually means

The core claim from Chai Discovery is that its models can shrink the discovery phase for antibody candidates from months to weeks. The 50% effectiveness rate for therapeutic-grade binding affinities is particularly notable in context. Traditional computational methods and even earlier generations of AI-assisted tools have typically produced hit rates that are far lower. Doubling the success rate from one model generation to the next suggests that the underlying approach, using generative AI techniques to design molecules from scratch rather than screening existing libraries, is scaling in a meaningful way.

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