Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction.

ABSTRACT: Antibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-based therapeutics. In this study, we present a method of supervised fine-tuning for antibody language models, which improves on pre-trained antibody language model embeddings in binding specificity prediction to SARS-CoV-2 spike protein and influenza hemagglutinin. We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifie

SUBMITTER: Wang M 

PROVIDER: S-EPMC12013870 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature