  Learning on 3D structures of large biomolecules is emerging as a distinct
area in machine learning, but there has yet to emerge a unifying network
architecture that simultaneously leverages the graph-structured and geometric
aspects of the problem domain. To address this gap, we introduce geometric
vector perceptrons, which extend standard dense layers to operate on
collections of Euclidean vectors. Graph neural networks equipped with such
layers are able to perform both geometric and relational reasoning on efficient
and natural representations of macromolecular structure. We demonstrate our
approach on two important problems in learning from protein structure: model
quality assessment and computational protein design. Our approach improves over
existing classes of architectures, including state-of-the-art graph-based and
voxel-based methods. We release our code at https://github.com/drorlab/gvp.
