jianqingzheng/XBCR-net
[Cell Research] Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
This project helps researchers and scientists in immunology and infectious disease by predicting how likely an antibody is to bind to a specific antigen, such as SARS-CoV-2 variants. You provide the amino acid sequences of an antibody's heavy and light chains, along with the antigen's sequence. The output indicates the binding probability, helping to identify potent antibodies. It's designed for immunologists, virologists, and drug discovery scientists.
Use this if you need to rapidly evaluate the potential binding affinity of newly designed or discovered antibodies to target antigens, particularly for viral research.
Not ideal if you are looking for a tool to design new antibody sequences from scratch, as this focuses on predicting binding for existing sequences.
Stars
32
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 31, 2025
Commits (30d)
0
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