Rebecca Croasdale-Wood

GenCDR uses LLaMa-based models to generate antibody CDRs with framework context, achieving high recovery and developable designs through alignment-driven training.

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.

p-IgGen generates paired heavy-light chain antibodies with biophysical properties similar to clinical-stage therapeutics.

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.