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

Faisal Bin Ashraf, Animesh Ray, Stefano Lonardi
3/4/2026
q-bio.QMcs.LG

Official code: ucrbioinfo/AbAffinity (1 star, last commit 2025-03-13).

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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Code Implementations(1)

ucrbioinfo/AbAffinityOfficial100%

A Large Languge Model to predict binding affinity of scFv antibody sequence against SARS-CoV-2. It takes input of antibody heavy and light chain sequence, and predicts the binding affinity against a common peptide in SARS-CoV-2.

10Shell, PythonJan 2, 20251 years ago

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Cite this paper

@article{ashraf2026abaffinity,
  title  = {AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2},
  author = {Faisal Bin Ashraf and Animesh Ray and Stefano Lonardi},
  year   = {2026},
  eprint = {2603.04480},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.04480v1}
}

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