Geometry-Complete Perceptron Networks for 3D Molecular Graphs. (arXiv:2211.02504v1 [cs.LG])

The field of geometric deep learning has had a profound impact on the
development of innovative and powerful graph neural network architectures.
Disciplines such as computer vision and computational biology have benefited
significantly from such methodological advances, which has led to breakthroughs
in scientific domains such as protein structure prediction and design. In this
work, we introduce GCPNet, a new geometry-complete, SE(3)-equivariant graph
neural network designed for 3D graph representation learning. We demonstrate
the state-of-the-art utility and expressiveness of our …

arxiv geometry graphs networks perceptron