Adjuvants have been indispensable in vaccinology, enhancing immunogenicity, shaping adaptive immune polarization, and extending the durability of protective responses. Classical adjuvants, including alum, oil-in-water emulsions, liposomal formulations, and toll-like receptor agonists, function by fueling innate immunity, promoting antigen presentation, and modulating cytokine milieus. Yet, these compounds face persistent limitations such as reactogenicity, species-specific responses, manufacturing complexity, and regulatory barriers. Artificial intelligence (AI) and core subfields such as machine learning are revolutionizing vaccine design by enhancing antigen engineering, delivery system optimization, immunogenicity modeling, and in silico screening of novel immune potentiators. Here, I propose a speculative yet testable hypothesis: AI-driven design of potent antigens may progressively replace classical adjuvants. By embedding adjuvant-like functions directly into computationally designed antigens, AI-designed vaccines could replicate or excel the mechanistic roles of traditional adjuvants. This perspective critically explores current AI applications in vaccinology, discusses key challenges in the field, including biological complexity, data limitations, safety, and regulatory and ethical considerations, and outline the spectrum of adjuvant replacement-from augmentation to full substitution. Finally, a roadmap is proposed for transitioning toward AI-driven adjuvant surrogates. Notably, this work highlights a potential paradigm shift in vaccine design, suggesting that future advances will lead to a gradual, stepwise transition from the use of traditional adjuvants to computationally guided antigens and delivery platforms capable of outperforming classical adjuvant functions, thus creating a new vaccine era without exogenous chemical additives.
Jose Marchan (Wed,) studied this question.