Data Sheet 1_AI-driven discovery in protein science for immunology and infectious disease research.pdf

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.

人工智能（Artificial Intelligence）凭借其强化决策、自动化复杂任务以及生成类人内容的能力，正深刻影响现代生活的诸多维度。如今，它已成为日常生活与学术研究中不可或缺的工具。尤为值得关注的是，人工智能技术的快速演进，尤其是机器学习（machine learning）、深度学习（deep learning）与自然语言处理（natural language processing，NLP）领域的进展，催生了大语言模型（large language models，LLMs），它们彻底改变了我们对文本类结构化数据与非结构化数据的分析、解读与生成方式。在此之中，生成式 AI（Generative AI）愈发受到青睐，因其能够基于大规模训练数据集所习得的模式，生成涵盖文本、代码、蛋白质序列乃至分子结构在内的各类内容。生成式AI工具可辅助文献综述、写作支持、数据处理、假设生成以及代码或可视化任务，但其输出结果需经过严格审核，以确保准确性与相关性。更为进阶的生成式AI应用则涵盖合成数据生成，乃至生物分子与材料的设计。在这一宏观背景下，免疫学（immunology）、疫苗学（vaccinology）与传染病研究领域正迎来由人工智能驱动的创新浪潮。在本综述中，我们将探讨生成式AI的最新进展——尤其是基于大语言模型的相关技术——如何应用于免疫学研究、抗体设计、疫苗开发、传染病研究以及大流行防范工作。本综述采用范围综述（scoping review）的架构，旨在梳理生成式AI与大语言模型在免疫学、疫苗开发、传染病研究及相关生物医学领域中快速发展的应用场景。本综述通过检索PubMed、Google Scholar及预印本数据库筛选相关研究，纳入那些针对与免疫学和传染病明确相关的人工智能方法进行介绍、验证或基准测试的文献；而对于未后续经过同行评议发表的旧预印本，则予以排除。我们旨在全面梳理生成式AI在推动我们对免疫应答与传染病的认知与干预方式变革方面的现有成果、新兴工具与模型，以及未来发展前景。因此，本文所提及的相关能力仅应被视为潜力的体现，而非确定的实际性能。