  Representing and reasoning about 3D structures of macromolecules is emerging
as a distinct challenge in machine learning. Here, we extend recent work on
geometric vector perceptrons and apply equivariant graph neural networks to a
wide range of tasks from structural biology. Our method outperforms all
reference architectures on three out of eight tasks in the ATOM3D benchmark, is
tied for first on two others, and is competitive with equivariant networks
using higher-order representations and spherical harmonic convolutions. In
addition, we demonstrate that transfer learning can further improve performance
on certain downstream tasks. Code is available at
https://github.com/drorlab/gvp-pytorch.
