  Deep learning for molecular science has so far mainly focused on 2D molecular
graphs. Recently, however, there has been work to extend it to 3D molecular
geometry, due to its scientific significance and critical importance in
real-world applications. The 3D distance-geometric graph representation (DG-GR)
adopts a unified scheme (distance) for representing the geometry of 3D graphs.
It is invariant to rotation and translation of the graph, and it reflects
pair-wise node interactions and their generally local nature, particularly
relevant for 3D molecular geometry. To facilitate the incorporation of 3D
molecular geometry in deep learning for molecular science, we adopt the new
graph attention network with dynamic attention (GATv2) for use with DG-GR and
propose the 3D distance-geometric graph attention network (DG-GAT). GATv2 is a
great fit for DG-GR since the attention can vary by node and by distance
between nodes. Experimental results of DG-GAT for the ESOL and FreeSolv
datasets show major improvement (31% and 38%, respectively) over those of the
standard graph convolution network based on 2D molecular graphs. The same is
true for the QM9 dataset. Our work demonstrates the utility and value of DG-GAT
for deep learning based on 3D molecular geometry.
