{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-based-rapid-generation-of","title":"Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant","arxiv_id":null,"date":"2022-09-27","proceeding":"Cell Research 2022 9","authors":["Hantao Lou","Jian-Qing Zheng","Xiaohang Leo Fang","Zhu Liang","Meihan Zhang","Yu Chen","Chunmei Wang","Xuetao Cao"],"abstract":"The COVID-19 pandemic has been ongoing for nearly two and half years, and new variants of concern (VOCs) of SARS-CoV-2 continue to emerge, which urges the development of broadly neutralizing antibodies. Variants such as the delta (B.1.617.2 lineage) and Omicron (BA.1 and BA.2) were reported to exhibit immune evasion to some of the current therapeutic antibodies. The ever-evolving SARS-CoV-2 calls for rapid prediction of antibody binding to new variants and development of broadly neutralizing antibodies. Considering the application of deep learning in antibody engineering and optimization, we wonder whether the broadly reactive antibodies against SARS-CoV-2 variants can be rapidly designed and generated by deep learning. Here we report the development of an Atrous Convolution Neural Network (ACNN) based deep learning framework: cross-reactive B cell receptor network (XBCR-net) that can predict broadly reactive antibodies against SARS-CoV-2 and VOCs directly from single-cell BCR sequences. XBCR-net composes of two parts, the first part extracts the features relevant to the antibody–antigen interaction via three-branch ACNN, and the second part predicts the binding probability of the antibodies to antigens (14 different RBD sequences) by a residual structural Multi-Layer Perceptron. The performance of the ACNN-based XBCR-net prediction on SARS-CoV-2 binding was evaluated, showing significantly higher accuracy, precision and recall value than other frameworks.","url_abs":"https://www.nature.com/articles/s41422-022-00727-6","url_pdf":"https://www.nature.com/articles/s41422-022-00727-6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-based-rapid-generation-of","repo_url":"https://github.com/jianqingzheng/XBCR-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"antibody-antigen-binding-prediction","task_name":"Antibody-antigen binding prediction"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"protein-function-prediction","task_name":"Protein Function Prediction"}],"methods":[{"method_slug":"acnn-block","method_name":"ACNN block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/antibody-antigen-binding-prediction-on","task":"Antibody-antigen binding prediction","dataset":"Antibody sequences against Sars-Cov-2 and Omicron BA.1","model":"XBCR-net (ACNN)","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy (5-fold)":"80.4%","NPV (5-fold)":"82.4%","Precision (5-fold)":"80.1%","Recall (5-fold)":"71.0%","Selectivity (5-fold)":"87.9%"},"uses_additional_data":false},{"leaderboard":"/sota/antibody-antigen-binding-prediction-on","task":"Antibody-antigen binding prediction","dataset":"Antibody sequences against Sars-Cov-2 and Omicron BA.1","model":"XBCR-net (CNN)","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy (5-fold)":"76.8%","NPV (5-fold)":"74.9%","Precision (5-fold)":"81.7%","Recall (5-fold)":"49.4%","Selectivity (5-fold)":"91.4%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}