The results show that current LLMs capture partial epitope-related signals but remain limited in antibody-specific sequence grounding, long-context residue localization, and biologically grounded reasoning, so EpiBench provides a diagnostic testbed for measuring and improving sequence-aware biomedical LLMs toward relia...
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
SAASBench provides a framework for evaluating the model's ability to estimate the specificity of a candidate antibody in relevant settings, indicating that strong performance on traditional affinity benchmarks does not automatically translate into reliable antibody specificity estimation in proteome-derived settings.
PatchEpi, a patch-aware geometric deep learning framework for antigen-intrinsic epitope modeling, is introduced and it is demonstrated that accurate epitope prediction benefits primarily from reformulating the task around surface-patch learning.
EpiTune is presented, a b-cell epitope prediction model that fine-tunes the underlying protein language model to deliver best-in-class predictions of linear epitopes and competitive predictions for confirmational epitopes.
The prediction of conformational B-cell epitopes (BCEs) is crucial for vaccine development and therapeutic antibody design. However, reliable identification of BCEs remains challenging because epitope residues are spatially discontinuous and represent only a small fraction of antigen surface residues, leading to severe...
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