Yongmei Chen

ConformAb designs antibody sequences that maintain CDR canonical conformations, improving binding affinity without target-specific data.

Property Enhancer optimizes antibody properties using a data-efficient framework, achieving significant affinity gains in both in silico and in vitro tests.

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.