  Antibody-antigen interactions play a crucial role in identifying and
neutralizing harmful foreign molecules. In this paper, we investigate the
optimal representation for predicting the binding sites in the two molecules
and emphasize the importance of geometric information. Specifically, we compare
different geometric deep learning methods applied to proteins' inner (I-GEP)
and outer (O-GEP) structures. We incorporate 3D coordinates and spectral
geometric descriptors as input features to fully leverage the geometric
information. Our research suggests that surface-based models are more efficient
than other methods, and our O-GEP experiments have achieved state-of-the-art
results with significant performance improvements.
