Antibody Composition Optimization with Machine-Learned Variant Selection

The patent applies preliminary action by using machine learning models to predict and prioritize mutations before experimental testing. The system analyzes sequence data and structural models to identify promising variants, then validates only those predictions experimentally. This approach prevents wasting resources on random mutations that are unlikely to be functional, as the ML model filters variants based on predicted activity and stability.

The patent replaces the mechanical brute-force search approach with an information-based machine learning system. Instead of randomly generating and testing all possible variants, the system uses trained ML models to predict which mutations are likely to improve protein function. This substitution of mechanical random sampling with intelligent prediction dramatically reduces the search space while maintaining high productivity in generating functional variants.

The patent introduces machine learning models as an intermediary between sequence data and experimental validation. The ML models process sequence information and structural predictions to generate ranked lists of candidate variants. This intermediary layer translates complex biological data into actionable predictions, improving efficiency while managing complexity through modular architecture where the ML system handles prediction and experimental systems handle validation.

The patent uses computational models to create virtual copies of protein variants before physical experimentation. The ML system generates predicted structures and activity profiles for many variants in silico, allowing researchers to evaluate numerous candidates computationally before synthesizing and testing only the most promising ones physically. This copying approach reduces experimental complexity by pre-filtering variants through computational analysis.