Shuang Wu

This paper develops a machine learning model to predict Trp oxidation risk in antibodies and shows that modulating electrostatic potential reduces oxidation while maintaining binding affinity.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.