[2603.04480] AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2

Quantitative Biology > Quantitative Methods arXiv:2603.04480 (q-bio) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 4 Mar 2026] Title:AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2 Authors:Faisal Bin Ashraf, Animesh Ray, Stefano Lonardi View a PDF of the paper titled AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2, by Faisal Bin Ashraf and 2 other authors View PDF HTML (experimental) 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 this https URL. Subjects: Quantitative Methods (q-bio.QM); Machine L...