Gilad Kaplan

The paper develops a sequence-structure fusion model to predict antibody self-association using CSI-BLI data, achieving high performance in distinguishing high and low self-interacting clones.

This paper introduces LAffAb, a method to co-optimize antibody affinity and stability through combinatorial mutation design, achieving significant affinity gains while maintaining developability.

This study compares sequence and structure-based models for predicting antibody developability properties using SEC assays, identifying the most effective in silico approach.

This study uses DeepAb to design antibody variants with enhanced stability and affinity without antigen information, showing 91% and 94% improvements in thermal/colloidal stability and affinity, respectively.