Inverse vaccinology is presented as a framework that uses generative AI to move from prediction of immune targets toward de novo immunogen design for predefined immunological objectives.
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inverse vaccinology
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Inverse vaccinology has implications across antigen discovery, delivery system optimization, and early clinical development.
Quoted textsource-backed
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.
Generative AI can be used not only to predict immune targets from existing pathogens but also to design immunogens de novo to satisfy predefined immunological objectives.
Quoted textsource-backed
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
AI and ML have reshaped vaccinology by enabling a transition from empirical antigen discovery toward computationally guided reverse vaccinology.
Quoted textsource-backed
Artificial intelligence (AI) and machine learning (ML) have progressively reshaped vaccinology, enabling the transition from empirical antigen discovery toward computationally guided reverse vaccinology.
Successful translation of generative AI in vaccinology depends on rigorous validation, transparent modeling assumptions, and realistic assessments of biological uncertainty.
Quoted textsource-backed
its successful translation depends on rigorous validation, transparent modeling assumptions, and realistic assessments of biological uncertainty
Inverse vaccinology should be viewed as a design-acceleration framework that narrows the experimental search space rather than replacing experimental vaccinology.
Quoted textsource-backed
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.
This generative AI shift is particularly relevant at the interface of prophylactic vaccines and therapeutic immuno-oncology because antigen heterogeneity and patient specificity challenge conventional development paradigms.
Quoted textsource-backed
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.