@inproceedings{
nori2023evaluating,
title={Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation},
author={Divya Nori and Simon Mathis and Amir Shanehsazzadeh},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=YTrlu38mM4}
}
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.
Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation
Divya Nori Simon Mathis Amir Shanehsazzadeh