Abeer Abd Elhadi
The therapeutic antibody repertoire has historically been discovered—never designed. That boundary began to dissolve in the early 2020s when generative artificial intelligence (AI) models started fabricating antibody structures and sequences that match or outperform the best products of natural immunity. This review examines how two generative paradigms—diffusion models (e.g., RFdiffusion, Chroma) and protein language models (PLMs) (e.g., ESM2, ESM3, IgLM, ProGen)—are now routinely creating nanobodies, epitope-focused immunogens, and antibody scaffolds with properties that are often unobtainable by conventional immunization or library screening alone. We dissect realistic, peer-reviewed case studies in which AI-conceived candidates have demonstrated picomolar affinities, high thermal stability (often exceeding 80 °C), and atomic-level structural accuracy validated by cryo-electron microscopy. We benchmark these designed molecules against evolution's finest and discuss remaining bottlenecks in immunogenicity prediction, manufacturing, and regulatory oversight. The review frames a fundamental transition: from immune repertoire mining to genuine immune repertoire design—an epoch in which in silico immunity becomes a therapeutic reality.