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

Authors

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

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 28 Aug 2026.

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