Chai Discovery builds frontier AI foundation models to predict and reprogram biochemical molecular interactions, transforming drug discovery from empirical screening of billions of candidate sequences into deterministic computational design. The company's platform achieves over 85% success rates in designing molecules that meet drug-like properties - a fundamental shift from traditional approaches that require years of wet-lab iteration and billions in capital. Founded by researchers who co-invented protein language modeling and built state-of-the-art folding algorithms, Chai Discovery has shipped Chai-1 and Chai-2, breakthrough models for computational molecular design now deployed in production pharmaceutical workflows.
The technical stack spans protein language modeling, protein folding algorithms, computational antibody design, and molecular interaction prediction. The platform handles previously undruggable targets, including GPCR agonist design with minimal experimental screening - a capability that addresses targets accounting for roughly 30% of marketed drugs but historically requiring extensive trial-and-error optimization. Design precision operates at atomic resolution, enabling drug-like antibody engineering with explicit control over molecular properties rather than stochastic library screening.
Chai Discovery is backed by OpenAI, Thrive Capital, and General Catalyst, and maintains active partnerships with pharmaceutical companies including Eli Lilly. The company operates from the US under CEO Joshua Meier, deploying models that compress multi-year discovery timelines into computational workflows. For engineers, the inference challenge involves running large-scale protein structure prediction and molecular design models in production environments where latency and throughput directly gate pharmaceutical R&D cycles, with evaluation rigor defined by experimental validation rates rather than benchmark metrics.