  Learning and reasoning about 3D molecular structures with varying size is an
emerging and important challenge in machine learning and especially in drug
discovery. 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 higher-order representations to faithfully express the
geometry of the data, such as directionality in their intermediate layers. In
this work, we propose an equivariant GNN that operates with Cartesian
coordinates to incorporate directionality and we implement a novel attention
mechanism, acting as a content and spatial dependent filter when propagating
information between nodes. We demonstrate the efficacy of our architecture on
predicting quantum mechanical properties of small molecules and its benefit on
problems that concern macromolecular structures such as protein complexes.
