An integrated AI-driven vaccine design process: a systematic review of workflows from generative design to translational prediction

Clinical Snapshot

Key Findings

• P Value: Not applicable for systematic review

• Effect Size: Not quantified - qualitative synthesis

• Primary Outcome: Identification of integrated AI workflow components for vaccine design

• Nnt Or Sensitivity: Transformer models and Protein Language Models identified as dominant for epitope prediction; AlphaFold2 as standard for structural modeling

• Confidence Interval: Not reported

Clinical Application

High feasibility for research institutions with computational resources; implementation barriers may exist for resource-limited settings Highly relevant for Australian vaccine development initiatives, CSIRO research programs, and TGA regulatory pathways for novel vaccine technologies Vaccine researchers, pharmaceutical companies, public health agencies involved in vaccine development and pandemic preparedness

Abstract

Traditional vaccine development faced significant hurdles, including lengthy timelines and high costs, which hindered rapid responses to pathogens. Although the emergence of AI offered transformative potential, the necessity for a fully integrated workflow was often overlooked in studies focusing on individual tools. This review addressed a critical gap by synthesizing AI technologies across the vaccine design process, focusing on the integrated workflow from antigen discovery to clinical translation. A systematic framework was required to connect disparate tools and ensure seamless transitions. Consequently, this study provided a comprehensive roadmap for pandemic preparedness and vaccine discovery. A systematic analysis based on the PRISMA framework (2015-2024) was conducted, and 19 landmark articles were reviewed.It was demonstrated that the paradigm shift from predictive to generative AI offered unprecedented opportunities for developing novel antigens and adjuvants with superior immunogenicity. Synthesis of the literature revealed rapid progress toward sophisticated deep learning. Transformer models and Protein Language Models emerged as dominant for epitope prediction, while AlphaFold2 became the standard for structural modeling. The advent of generative AI for de novo antigen design represented the leading edge of the discipline. Additionally, AI-enhanced molecular dynamics and digital twin simulations accelerated clinical validation and manufacturing scalability. The "Integrated AI Workflow for Vaccine Design and Development" was emphasized as a comprehensive system and a prerequisite for sustainable innovation. Overall, this analysis served as a strategic roadmap for utilizing AI as a transformative framework for next-generation vaccine discovery and pandemic preparedness.

References

1. 1.Shafaati, M., Nikzadjamnani, F., & Ghorbani, M. (2026). An integrated AI-driven vaccine design process: a systematic review of workflows from generative design to translational prediction. Immunologic Research. https://doi.org/10.1016/j.mimet.2024.106998