Case Study – Closing the Loop: Scaling Wet-Lab Validation for AI-Designed Antibodies with RushDATA™

As AI-driven antibody discovery advances, researchers can now design hundreds to thousands of antibody candidates in a single cycle. However, experimentally validating these sequences at scale remains a major bottleneck, creating a growing need for faster, standardized wet-lab data generation.

To address this challenge, Biointron developed RushDATA™, a high-throughput platform that bridges AI-driven antibody design and experimental validation. With the capacity to express 10,000+ antibody molecules per day, RushDATA™ delivers sequence-to-data results in as little as 2 days using cell-free expression and 3 days using CHO expression.

In this case study, discover how RushDATA™; enabled the parallel screening of thousands of AI-designed antibody candidates, generating reliable expression, binding, and early developability data in days rather than weeks. By streamlining experimental workflows, the platform helps researchers identify promising candidates faster, make data-driven decisions, and accelerate AI-guided antibody discovery.