Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties

Animal immunization merges with innovative technologies: A new paradigm shift in antibody discovery

“…More recently, immune organoids from human tonsils and other lymphoid tissues have been developed with a potential for the discovery of antigen-specific antibodies, mimicking key germinal center features including somatic hypermutation and affinity maturation. 29 We expect that artificial intelligence (AI)-and machine learning (ML)-based methods [30][31][32] could essentially exploit the best of both worlds of in vivo-and in vitro-generated methods, large-scale naïve and antigen-specific antibody sequence and structure data, [33][34][35] knowledge of immune repertoire and literacy, [36][37][38] help design feature-controlled antibody libraries and developable antibodies, 39,40 which, in turn, would ultimately solve scientific and ethical problems in antibody generation.…”