Wei‐Ching Liang

DyAb predicts antibody properties and designs new sequences with high binding affinity using a low-data approach.

The paper shows that blocking integrin αvβ8 suppresses TGFβ in tumor-draining lymph nodes and enhances DC-T cell activation, offering a new immunotherapy strategy distinct from TGFβ ligand blockade.

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

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.