Bridging the Gap: High-Success-Rate AI Protein Design via Preference Optimization & Wet-Lab Validation

• Optimized AI generation achieves higher hit rates, nearly tripling computational design success and drastically slashing early-stage trial-and-error costs
• Wet-lab results confirm that our AI-designed sequences successfully fold and exhibit real catalytic activity, providing validated functional enzymes
• Sequence optimization significantly boosts antibody expression, thus improving production yields and directly derisking downstream manufacturing bottlenecks

Speakers:

Fanglei Xue

Post-Doctorate, Baker Lab

University of Washington