Machine learning (ML) and deep learning (DL) in vaccine target selection, design, development and characterization

Comprehensive Summary

This review article examines the growing role of artificial intelligence, particularly machine learning and deep learning, in modern vaccine development. The authors describe how ML and DL tools support key stages of rational vaccine design, including vaccine target selection, epitope prediction, antigenicity assessment, immunogen structure design, immune system modeling, vaccine formulation optimization, and in silico clinical trials. The review also highlights applications of AI in vaccine safety evaluation, allergenicity prediction, and integration of omics data. While emphasizing the promise of AI-driven vaccine development, the authors acknowledge challenges related to limited and biased data availability.

Outcomes and Implications

The integration of ML and DL into vaccine development has significant public health implications by enabling faster, safer, and more cost-effective vaccine design. These technologies can accelerate responses to emerging infectious diseases and pandemics, improve vaccine efficacy, and reduce reliance on time-consuming trial-and-error methods. Despite data limitations, continued advancements in AI-driven approaches could transform global vaccine development and strengthen preparedness for future public health threats.