Despite breakthroughs in protein design enabled by artificial intelligence, reliably designing

functional antibodies from scratch has remained an elusive challenge. Recent works show promise but

still require large-scale experimental screening of thousands to millions of designs to reliably identify

hits. In this work, we introduce Chai-2, a multimodal generative model that achieves a 16% hit rate in

fully de novo antibody design, representing an over 100-fold improvement compared to previous computational methods. We prompt Chai-2 to design ≤20 antibodies or nanobodies to 52 diverse targets,

completing the workflow from AI design to wet-lab validation in under two weeks. Crucially, none of

these targets have a preexisting antibody or nanobody binder in the Protein Data Bank. Remarkably,

in just a single round of experimental testing, we find at least one successful hit for 50% of targets,

often with strong affinities and favorable drug-like profiles. Beyond antibody design, Chai-2 achieves

a 68% wet-lab success rate in miniprotein design – routinely yielding picomolar binders. The high

success rate of Chai-2 enables rapid experimental validation and characterization of novel antibodies

in under two weeks, paving the way toward a new era of rapid and precise atomic-level molecular

engineering.