{"doi":"10.3390/ijms27073296","title":"AI-Driven BCR Modeling for Precision Immunology","abstract":"<jats:p>The B cell receptor (BCR) repertoire captures an individual’s 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–structure–function 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>","journal":"International Journal of Molecular Sciences","year":2026,"id":641401,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1667683,"name":"Xusheng Zhao","orcid":"0009-0002-2659-716X","position":1,"is_corresponding":false},{"id":237491,"name":"Fan Yang","orcid":"0000-0003-4821-6583","position":2,"is_corresponding":false},{"id":364853,"name":"Tao Liu","orcid":"0000-0002-2756-1161","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"AI-Driven BCR Modeling for Precision Immunology","abstract":"<jats:p>The B cell receptor (BCR) repertoire captures an individual’s 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–structure–function 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>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41977475","pmcid":"PMC13074136","openalex_id":"https://openalex.org/W7149902423","authors":[],"funders":[{"funder_name":"the National Key Research and Development Program of China","grant_id":"2024YFA0920004","title":null},{"funder_name":"Shenzhen Medical Research Fund","grant_id":"B2303001","title":null},{"funder_name":"Guangdong Basic and Applied Basic Research Foundation","grant_id":"2024A1515012510","title":null},{"funder_name":"Natural Science Foundation of China","grant_id":"32270937","title":null},{"funder_name":"Natural Science Foundation of China","grant_id":"82341068","title":null},{"funder_name":"Natural Science Foundation of China","grant_id":"No. 32270937 and 82341068","title":null},{"funder_name":"the National Key Research and Development Program of China","grant_id":"No. 2024YFA0920004","title":null}],"total_grants":7,"fwci":0.0,"citation_percentile":0.32983954,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/1422-0067/27/7/3296/pdf?version=1775384598","host_type":"journal"},{"url":"https://www.mdpi.com/1422-0067/27/7/3296/pdf?version=1775384598","host_type":"publisher"},{"url":"https://www.mdpi.com/1422-0067/27/7/3296/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/ijms27073296","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41977475","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13074136/","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC13074136","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC13074136?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["vaccines and immunoinformatics approaches","Monoclonal and Polyclonal Antibodies Research","Immunotherapy and Immune Responses","Medicine","Computer Science","Biology","Humans","Receptors, Antigen, B-Cell","Artificial Intelligence","Animals","Immunoinformatics","Graph Neural Networks","Deep Learning","Generative Artificial Intelligence","B-Lymphocytes"],"mesh_terms":["Deep Learning","Immunoinformatics","Graph Neural Networks","Generative Artificial Intelligence","Animals","Artificial Intelligence","B-Lymphocytes","Humans","Receptors, Antigen, B-Cell","Neural Networks, Computer","Precision Medicine"],"keywords":["Repertoire","breakpoint cluster region","Profiling (computer programming)","Deep learning","Generative grammar","Systems biology","Immune system","Acquired immune system","Bcr","immunogenetics","Machine Learning","Antibody Repertoire"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T17:43:52.783933Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}