Benchmarking Machine Learning Models for Viral Nanobody Epitope Prediction

Abstract Rapid development of therapeutics against emerging viral pathogens increasingly depends on machine learning (ML), both to generate candidate binders and for structural validation. Nanobodies (Nbs), or single-domain antibodies, are promising antiviral therapeutics, but accurate in silico prediction of viral epitopes remains challenging. Here we benchmark three ML-based structure prediction models, Boltz-2, Chai-1 and AlphaFold3 (AF3), against a curated data set of viral antigen-nanobody complexes deposited after the AF3 training cutoff. AF3 outperformed the other models: success (interface DockQ > 0.23) rose from 33% with a single seed to 50% and 65% with 10 and 100 seeds-per-target, respectively, compared with 18% for Boltz-2 and 30% for Chai-1. Using an expanded data set, we evaluated confidence metrics and found that a strict ipTM cutoff of 0.8, or a combination of ipTM ≥ 0.6 with a CDR3 loop pLDDT ≥ 60, reliably excluded incorrect predictions while retaining most successful ones. We further establish best practices for two AF3-specific capabilities, the choice of antigen multimeric state and the inclusion of N-glycosylation, both of which can be decisive for epitope success depending on the target. Finally, we apply these guidelines to two design problems: ranking a library of VEEV-neutralizing nanobodies and mapping the epitope of the broadly neutralizing nanobody CC3 across homologous alphavirus antigens. These results provide a practical framework for ML-based viral nanobody epitope prediction and design.