Isidro Hötzel

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.

The paper identifies multiple RBD transfer events in retroviruses through phylogenetic analysis and structural modeling, revealing an endogenous alpharetroviral-like Env with an IgV domain fold acquired from mammalian genes.