Ryan L. Kelly

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

The paper shows IRF2 degradation by IRF1 during immune responses enhances ISG transcription, revealing a regulatory circuit between IRF1 and IRF2 in innate immunity.

This paper introduces EVFI and DeepEVFI, machine learning methods that infer variant fitness from directed evolution data, identifying tighter binders missed by conventional approaches.

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.