Researchers must navigate a vast protein sequence space to identify optimal antibody designs. Traditional methods for doing so can be both costly and inefficient, requiring the iterative testing of tens to hundreds of thousands of variants. This research showcase highlights a recent study by a research team at the University of Washington, led by Nobel Laureate David Baker, that is a pivotal step toward scalable AI-driven biologics development. The study leveraged artificial intelligence, Multiplexed Gene Fragments, and de novo protein design software to rationally design and validate new variable heavy-chain domain antibodies against disease relevant targets.