AI-driven discovery in protein science for immunology and infectious disease research

Artificial Intelligence (AI) is impacting several aspects of modern life with its ability to enhance decision-making, automate complex tasks, and generate human-like content. It is now an indispensable tool in both everyday life and academic inquiry. In particular, the rapid evolution of AI technologies, especially machine learning, deep learning, and natural language processing (NLP), has given rise to large language models (LLMs), which have transformed how we analyze, interpret, and generate text-based, structured data and unstructured data. Among these, Generative AI (GenAI) has become increasingly popular due to its capacity to create content ranging from text and code to protein sequences and molecular structures, all based on patterns found in large training datasets. GenAI tools can assist with literature reviews, writing support, data processing, hypothesis generation, and code or visualization tasks, although outputs require critical oversight to ensure accuracy and relevance. More advanced GenAI applications include the generation of synthetic data and even the design of biological molecules and materials. Within this broader context, the fields of immunology, vaccinology, and infectious diseases research are witnessing a wave of innovation driven by AI. In this review, we explore how these recent advances in GenAI, especially those based on LLMs, are being applied to immunological research, antibody design, vaccine development, infectious diseases research and pandemic preparedness. This review is structured as a scoping review, aiming to map the rapidly evolving applications of GenAI and LLMs in immunology, vaccine development, infectious disease research, and adjacent biomedical fields. Relevant studies were identified through searching PubMed, Google Scholar and preprint archives and included if they introduced, demonstrated, or benchmarked AI-based approaches with clear relevance to immunology and infectious disease, while older preprints without subsequent peer-reviewed publication were excluded. We aim to provide a comprehensive overview of current contributions, emerging tools and models, and future perspectives of GenAI in transforming how we understand and manipulate immune responses and infectious diseases. Therefore, the reported capabilities should be interpreted as indicative of potential rather than definitive performance.