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

ArXi:2605.06720v1 Announce Type: cross Antibody therapeutics are among the most successful modern medicines, yet computationally designing antibodies with desirable binding and developability properties remains challenging. While protein language models (pLMs) have emerged as powerful tools for antibody sequence design, existing approaches largely suffer from two key limitations: they predominantly memorize germline sequences rather than modeling biologically meaningful somatic variation, and they offer limited for flexible classifier-guided conditional generation.