AI-Enabled Generative Design of Immune Cells and Receptors for Programmable Immunity

Abstract

Recent advances in generative artificial intelligence have begun to redefine the practice of cell and immune engineering. By learning the statistical and structural grammar of biological systems, generative models can now design T-cell receptors, chimeric antigen receptors, and synthetic immune circuits that meet complex objectives of affinity, stability, and specificity. When integrated into automated design–build–test–learn pipelines, these models enable continuous cycles of hypothesis generation, experimental validation, and model refinement, creating a closed feedback loop between computation and biology. This review examines how AI-driven generative design is transforming immunoengineering across multiple scales, from molecular recognition to cellular phenotype and clinical translation. It discusses the foundational architectures that support generative modeling in biology, the emergence of adaptive biofoundries that link digital design to manufacturing, and the translational pathways through which programmable immune cells may enter clinical practice. The review also explores the ethical and regulatory dimensions of algorithmic biology, emphasizing the need for transparency, equitable access, and anticipatory governance. Together, these developments signal the rise of a new paradigm, programmable immunity, in which biological design, therapeutic discovery, and ethical responsibility evolve within a single, intelligent framework.