The Redundancy Paradox: Why More Protein Data Doesn't Equal Better AI Drug Design

As AlphaFold3 and similar generative models revolutionize computational drug design, a fundamental challenge has surfaced: nature's billions of protein sequences fold into surprisingly few unique 3D structures. This structural redundancy means that scaling training data through protein sequencing alone—a proven strategy for language models—yields diminishing returns in biomolecular design. The finding could reshape how teams engineer the next generation of AI-powered molecular discovery systems.