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