Building Fit-for-Purpose Datasets for AI-Driven Enzyme Engineering: Balancing Data Quality, Accessibility & Industrial Relevance

• Identify the key barriers to generating and sharing high-quality enzyme engineering datasets, including standardization, IP constraints, and experimental variability
• Explore approaches for creating robust, industrially relevant datasets that improve model predictability, benchmarking, and decision-making
• Discuss how organizations can leverage automation, high-throughput experimentation, and collaborative data initiatives to accelerate AI-enabled enzyme engineering

Speakers:

Kui Chan

Principal Scientist

Cyrus Biotechnology Inc.