Poster
in
Workshop: Generative AI and Biology (GenBio@NeurIPS2023)
Guiding diffusion models for antibody sequence and structure co-design with developability properties
Amelia Villegas-Morcillo · Jana M. Weber · Marcel Reinders
Keywords: [ Developability Properties ] [ Conditioned Diffusion Models ] [ generative modeling ] [ antibody design ]
Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a novel approach that integrates property guidance into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-conditioned diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications.