Papers › Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2...
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
Hantao Lou, Jian-Qing Zheng, Xiaohang Leo Fang, Zhu Liang, Meihan Zhang, Yu Chen, Chunmei Wang, Xuetao Cao
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (ACNN) | Accuracy (5-fold) | 80.4% | #1 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (ACNN) | NPV (5-fold) | 82.4% | #1 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (ACNN) | Precision (5-fold) | 80.1% | #1 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (ACNN) | Recall (5-fold) | 71.0% | #1 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (ACNN) | Selectivity (5-fold) | 87.9% | #1 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (CNN) | Accuracy (5-fold) | 76.8% | #2 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (CNN) | NPV (5-fold) | 74.9% | #2 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (CNN) | Precision (5-fold) | 81.7% | #2 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (CNN) | Recall (5-fold) | 49.4% | #2 of 3 | Archive leaderboard | report |
| Antibody-antigen binding prediction | Antibody sequences against Sars-Cov-2 and Omicron BA.1 | XBCR-net (CNN) | Selectivity (5-fold) | 91.4% | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections