Training Data · Tuesday, August 4, 2026
Chai Discovery has significantly improved the success rate of AI-driven antibody design, increasing it from 0.1% to 15% with their Chai 2 model. This leap in accuracy allows for more robust data collection and analysis of molecular properties. The company focuses on end-to-end generative design, taking an idea from concept to a testable molecule in the lab, differentiating them from competitors who may only assist in finding existing molecules.
“So we really focused in on how do we just make this process more accurate. We got to, uh, with our Chai 2 model about a 15% success rate.”
“So now if you screen out a thousand molecules, you're getting 150 back. And then you can start to get some like interesting statistics on the properties of the molecules.”
“So, you know, we're not just, uh, you know, building a tool that helps you find good molecules. We're actually building a platform that allows you to go from idea to actual molecule in the lab, uh, that can be tested.”