ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning

Kevin Michalewicz, Mauricio Barahona, Barbara Bravi

Keywords: Antibody, Binding Affinity, Deep Learning, Interpretability, Normal Mode Analysis, Protein Structures

TL;DR: ANTIPASTI is a Deep Learning model that leverages Normal Modes to predict antibody-antigen binding affinity with state-of-the-art accuracy and generalization, while maintaining interpretability and identifying key antibody regions for binding.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1016/j.str.2024.10.001

Submission Number: 23