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.

本文研究了在识别和中和有害外来分子中抗体 - 抗原相互作用中的最佳表示，强调几何信息的重要性，并比较了应用于蛋白质内部（I-GEP）和外部（O-GEP）结构的不同几何深度学习方法。我们将三维坐标和谱几何描述符作为输入特征，充分利用几何信息。研究结果表明，基于表面的模型比其他方法更高效，并且我们的 O-GEP 实验在性能上取得了显著的改进和最新成果。