Vaccines for microbial eye diseases in the era of data science: opportunities and challenges

Background Ocular infectious diseases remain an important cause of preventable visual impairment worldwide, yet vaccine development for eye-specific pathogens has lagged behind systemic infections. This gap reflects the biology of the eye, including immune privilege, mucosal immunity, pathogen diversity, and limited understanding of ocular correlates of protection. Advances in data science, artificial intelligence, genomics, and systems biology are reshaping vaccine research by enabling antigen discovery, immune modeling, and surveillance. However, these approaches have not been integrated into a coherent framework for ocular vaccinology. Purpose of the review This review examines how data science and computational methods may help accelerate ocular vaccine development by addressing the biological, immunological, and translational constraints that have limited progress in the field. Rather than providing a descriptive overview of ocular pathogens and vaccine candidates, it proposes a challenge–solution framework linking ocular immunobiology, computational vaccinology, and translational implementation. Main themes covered The review is organized around three main themes. First, it outlines the biological and immunological barriers that complicate vaccine development for ocular infections, including immune privilege, mucosal immune constraints, antigenic diversity, and poorly defined correlates of protection. Second, it evaluates how computational approaches, including reverse vaccinology, machine learning-based epitope prediction, structural vaccinology, systems immunology, genomic epidemiology, and real-world data analytics, may support antigen discovery, immune modeling, and population-targeted vaccination strategies. Third, it examines the translational barriers that continue to limit clinical implementation, including inadequate ocular disease models, fragmented ophthalmic datasets, challenges in data standardization and interoperability, regulatory uncertainty, and financial constraints. Key conclusions Progress in ocular vaccinology is likely to depend on an integrated, systems-level strategy that combines immunology, ophthalmology, and data science. Computational methods may reduce the cost, time, and uncertainty associated with antigen discovery and candidate prioritization, whereas epidemiological and real-world data may strengthen vaccine targeting, surveillance, and post-implementation evaluation. However, meaningful translation will require ocular-specific immune models, standardized multimodal ophthalmic datasets, improved experimental systems, and iterative validation pipelines that connect in silico prediction with biological and clinical evidence. Collectively, these advances could help shift ocular vaccinology from a largely reactive field toward a more predictive and evidence-based approach to preventing vision-threatening infections.