Hyeonuk Woo

Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

This paper evaluates GaluxDesign, an AI platform, for de novo antibody design, showing a 10.5% binder rate and strong performance on benchmark targets.

Deep learning molecular interaction motifs from Receptor structure alone

This paper introduces MotifGen, a deep learning model that predicts molecular interaction motifs from receptor structures, enabling peptide binder design and small-molecule binding site prediction.

Precise, Specific, and Sensitive De Novo Antibody Design Across Multiple Cases

This study designs antibodies without prior information across eight targets, achieving high precision, sensitivity, and specificity, validated by Cryo-EM and comparable to commercial antibodies.

Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies

This study shows that accurate antibody loop structure prediction enables the design of target-specific antibodies without prior structural templates.