EpiBench | 大语言模型能否真正理解抗体表位？

TL;DR - EpiBench is a new zero-shot benchmark (1,609 curated samples, 5 linked tasks) testing whether general LLMs can reason about antibody epitopes directly from antigen/antibody sequences rather than recite biomedical facts; results show current models capture some epitope-like sequence signal but fail at antibody-specific binding assignment.

• Five chained tasks span targetable-region discovery, antibody-specific epitope identification, epitope binning, functional-epitope assessment (blocking/protection/ADCC), and mutation-driven escape prediction; labels derive from antibody–antigen complex structures, functional B-cell assays, and deep mutational scanning.
• Nine general LLMs were evaluated zero-shot; they do reasonably on generic "which region looks epitope-like" but degrade sharply on antibody-specific localization and escape calls, and task-specific epitope predictors still outperform them where directly comparable.
• Performance drops as antigen sequence length grows — long-context capability does not equal accurate residue-level tracking in long proteins.
• Chain-of-thought gives inconsistent gains; error analysis shows models lean on motifs, physicochemical properties, CDR similarity, and memorized antibody knowledge. One SARS-CoV-2 RBD case applied correct escape knowledge at mis-mapped coordinates, yielding an inverted prediction — positioning EpiBench as a diagnostic tool for sequence-grounding gaps.