  The success of therapeutic antibodies relies on their ability to selectively
bind antigens. AI-based antibody design protocols have shown promise in
generating epitope-specific designs. Many of these protocols use an inverse
folding step to generate diverse sequences given a backbone structure. Due to
prohibitive screening costs, it is key to identify candidate sequences likely
to bind in vitro. Here, we compare the efficacy of 8 common scoring paradigms
based on open-source models to classify antibody designs as binders or
non-binders. We evaluate these approaches on a novel surface plasmon resonance
(SPR) dataset, spanning 5 antigens. Our results show that existing methods
struggle to detect binders, and performance is highly variable across antigens.
We find that metrics computed on flexibly docked antibody-antigen complexes are
more robust, and ensembles scores are more consistent than individual metrics.
We provide experimental insight to analyze current scoring techniques,
highlighting that the development of robust, zero-shot filters is an important
research gap.
