AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2

• URL: http://arxiv.org/abs/2603.04480v1
• Date: Wed, 04 Mar 2026 18:09:10 GMT
• Title: AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
• Authors: Faisal Bin Ashraf, Animesh Ray, Stefano Lonardi,
• Abstract summary: Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases.<n>The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies.<n>In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab-Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS-CoV-2 spike protein. Code and model are available at https://github.com/ucrbioinfo/AbAffinity.

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