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bioRxiv · 10.64898/2026.08.24.745914

AntiSite: Modality Dropout Enables Antibody Paratope Prediction With or Without Structure From a Single Model

Papadopoulos, A. M.·Alvarez, F.·Daras, P.
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Abstract

Summary: Reliable paratope identification is central to understanding antibody antigen recognition and advancing therapeutic antibody discovery. AntiSite is a unified antibody paratope prediction framework that combines protein language-model sequence embeddings with structure-derived molecular-surface features and, through modality dropout, trains a single checkpoint to predict both with and without a structure. This lets one model support sequence-only inference when no structure is available and structure-aware inference when an antibody structure is provided. Availability and implementation: Source code, trained models and evaluation scripts are freely available at https://github.com/aggelos-michael-papadopoulos/AntiSite. Processed benchmark structures and corrected split metadata are archived on Zenodo at https://doi.org/10.5281/zenodo.21705412.

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Papadopoulos, A. M., Alvarez, F., Daras, P.. 2026-08-27. AntiSite: Modality Dropout Enables Antibody Paratope Prediction With or Without Structure From a Single Model. https://doi.org/10.64898/2026.08.24.745914

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