Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion

This research introduces a novel approach for conditional generation of antibody sequences, addressing limitations in current protein language models by better modeling somatic variation and enabling flexible classifier-guided generation. It proposes discrete diffusion fine-tuning and germline absorbing diffusion for improved antibody design.