Jinsung Noh

T-SCAPE: T-cell Immunogenicity Scoring via Cross-domain Aided Predictive Engine

T-SCAPE predicts T-cell immunogenicity using a multi-domain deep learning framework, achieving high accuracy in predicting T-cell activation and antibody-induced ADA potential.

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.

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.

Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

This paper introduces a deep learning-based method to analyze B cell receptor repertoires in COVID-19 patients and healthy subjects, enabling accurate clustering and classification of immune status changes.