Representation Learning on Biomolecular Structures using Equivariant Graph Attention

Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in the development of biotherapeutics. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through the propagation of information between nodes leveraging geometrical details, such as directionality in their intermediate layers. In this work, we propose an equivariant GNN that operates with Cartesian coordinates to incorporate directionality and implements a novel attention mechanism, acting as a content and spatial dependent filter. Our proposed message function processes vector features in a geometrically meaningful way by mixing existing vectors and creating new ones based on cross products.