  We present a three-dimensional graph convolutional network (3DGCN), which
predicts molecular properties and biochemical activities, based on 3D molecular
graph. In the 3DGCN, graph convolution is unified with learning operations on
the vector to handle the spatial information from molecular topology. The 3DGCN
model exhibits significantly higher performance on various tasks compared with
other deep-learning models, and has the ability of generalizing a given
conformer to targeted features regardless of its rotations in the 3D space.
More significantly, our model also can distinguish the 3D rotations of a
molecule and predict the target value, depending upon the rotation degree, in
the protein-ligand docking problem, when trained with orientation-dependent
datasets. The rotation distinguishability of 3DGCN, along with rotation
equivariance, provides a key milestone in the implementation of
three-dimensionality to the field of deep-learning chemistry that solves
challenging biochemical problems.
