Equivariant Graph Neural Networks for Protein Interaction Modeling and Structure-Aware Molecular Design
Equivariant graph neural networks (EGNNs) have revolutionized molecular property prediction by encoding 3D structures while preserving physical symmetry. However, existing EGNNs struggle to capture fine-grained geometric features needed for tasks like understanding protein interactions, and designing molecules that mod...