Chai Discovery Lands $70M for Generative Antibody Design

According to Chai Discovery’s co-founders, a biotech firm spent over three years and $5 million trying to generate an antibody for a difficult molecular target. The search involved traditional wet-lab screening and failed to yield results. This year, Chai Discovery claims it solved the same problem in just two weeks using Chai-2, a Gen AI model capable of designing antibodies from scratch with a reported experimental hit rate exceeding 15%.

Founded in 2024, Chai Discovery is developing AI foundation models that simulate and reprogram the interactions between biochemical molecules: an effort aimed at transforming biology into a deterministic design process. The company has now raised $70 million in Series A funding led by Menlo Ventures and its Anthology Fund, a joint initiative with Anthropic. Other backers include Thrive Capital, OpenAI, and DST Global, bringing the total capital raised to $100 million.

Foundation Models for Molecular Design

Chai’s platform builds on the trajectory of generative AI in protein science, following earlier breakthroughs like AlphaFold and EvolutionaryScale’s ESM-3. Its first release, Chai-1, was an open-source model for molecular structure prediction, which founder and CEO Joshua Meier claimed outperformed benchmarks like AlphaFold on tasks critical to drug discovery.

With Chai-2, the company has moved from prediction to generation. The model produces entirely new antibodies based only on a target antigen and epitope, achieving a hit rate the company says is up to 100 times better than previous AI tools. According to Chai, the hit rate for de novo binders is over 15%, compared to 0.1% in older computational methods, and can reach 68% for miniprotein designs.