This project aims to make AI-designed enzymes more reliable by learning from both successful and unsuccessful designs. The team will combine AI protein-design tools with high-throughput functional testing and structural biology to build large datasets linking enzyme sequence, structure, and activity. These experimental data will be fed back into the AI models to improve successive rounds of design.