{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:59:52Z","timestamp":1775537992703,"version":"3.50.1"},"reference-count":115,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2024YFA0920004"],"award-info":[{"award-number":["2024YFA0920004"]}]},{"name":"Shenzhen Medical Research Fund","award":["B2303001"],"award-info":[{"award-number":["B2303001"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515012510"],"award-info":[{"award-number":["2024A1515012510"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32270937"],"award-info":[{"award-number":["32270937"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82341068"],"award-info":[{"award-number":["82341068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>The B cell receptor (BCR) repertoire captures an individual\u2019s immunological history and antigen-driven evolution within a vast, high-dimensional sequence space. Although bulk and single-cell adaptive immune receptor repertoire sequencing (AIRR-seq) now enables deep profiling of BCR diversity, interpreting these datasets remains challenging due to strong inter-individual heterogeneity, nonlinear sequence\u2013structure\u2013function relationships, dynamic clonal evolution, and the rarity of functionally relevant clones. Artificial intelligence (AI) provides a conceptual and computational framework for addressing these challenges. Here, we summarize how advanced deep learning architectures, including antibody-specific language models, graph neural networks (GNNs), and generative frameworks, uncover clonal topology, structural features, and antigen-binding semantics. We further highlight applications in cancer, infectious disease, and autoimmunity. Finally, we propose a closed-loop framework that integrates multimodal datasets, interpretable AI, and iterative experimental validation to advance predictive immunology and accelerate therapeutic antibody discovery.<\/jats:p>","DOI":"10.3390\/ijms27073296","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T02:06:21Z","timestamp":1775441181000},"page":"3296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven BCR Modeling for Precision Immunology"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1678-1799","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2659-716X","authenticated-orcid":false,"given":"Xusheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4821-6583","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine Learning: Trends, Perspectives, and Prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine Learning for Molecular and Materials Science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/nrd.2017.232","article-title":"Automating Drug Discovery","volume":"17","author":"Schneider","year":"2018","journal-title":"Nat. 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