Engineered proteins for biologic medicines often fail during expensive, multi-year development cycles. New AI tools now predict protein structures and interactions to identify viable drug candidates faster. MIT Tech Review highlights how these models reduce trial-and-error in the lab. This shift lowers R&D costs for biotech firms while speeding up patient access to therapies.