  Geometric deep learning (GDL) has demonstrated huge power and enormous
potential in molecular data analysis. However, a great challenge still remains
for highly efficient molecular representations. Currently, covalent-bond-based
molecular graphs are the de facto standard for representing molecular topology
at the atomic level. Here we demonstrate, for the first time, that molecular
graphs constructed only from non-covalent bonds can achieve similar or even
better results than covalent-bond-based models in molecular property
prediction. This demonstrates the great potential of novel molecular
representations beyond the de facto standard of covalent-bond-based molecular
graphs. Based on the finding, we propose molecular geometric deep learning
(Mol-GDL). The essential idea is to incorporate a more general molecular
representation into GDL models. In our Mol-GDL, molecular topology is modeled
as a series of molecular graphs, each focusing on a different scale of atomic
interactions. In this way, both covalent interactions and non-covalent
interactions are incorporated into the molecular representation on an equal
footing. We systematically test Mol-GDL on fourteen commonly-used benchmark
datasets. The results show that our Mol-GDL can achieve a better performance
than state-of-the-art (SOTA) methods. Source code and data are available at
https://github.com/CS-BIO/Mol-GDL.
