From Prediction to Synthesis: Generative AI Architectures and Digital Twins for the Future of Vaccines and Immuno-Oncology

Artificial intelligence (AI) and machine learning (ML) have progressively reshaped vaccinology, enabling the transition from empirical antigen discovery toward computa-tionally guided reverse vaccinology. As the field enters 2026, a further conceptual shift is emerging: the use of generative AI not only to predict immune targets from existing pathogens, but to design immunogens de novo to satisfy predefined immunological objectives. This evolution is particularly relevant at the interface of prophylactic vaccines and therapeutic immuno-oncology, were antigen heterogeneity and patient specificity challenge conventional development paradigms. This review critically examines the transition from predictive to generative AI in vaccinology, a framework we refer to as inverse vaccinology, and evaluates its implications across antigen discovery, delivery system optimization, and early clinical development. I synthesized recent advances in deep learning architectures—including graph neural networks, protein language mod-els, and diffusion-based generative systems—alongside emerging applications of digital immune modeling, Bayesian optimization, and AI-guided formulation design. Emphasis is placed on evidence derived from structural biology, immunopeptidomics, and trans-lational vaccine research. Current evidence suggests that AI-enabled integration of an-tigen design with delivery and pharmacokinetic modeling can reduce attrition during preclinical development, particularly for mRNA-based vaccines and personalized neo-antigen strategies. The convergence of immunogen design, lipid nanoparticle engi-neering, and in-silico immune modeling highlights a nascent immuno-pharmacology axis that links molecular optimization to biological exposure and immune activation. While generative AI offers a powerful extension of computational vaccinology, its suc-cessful translation depends on rigorous validation, transparent modeling assumptions, and realistic assessments of biological uncertainty. Rather than replacing experimental vaccinology, inverse vaccinology should be viewed as a design-acceleration framework that narrows the experimental search space and enables more rational, patient-aware vaccine development.