  The prediction of physicochemical properties from molecular structures is a
crucial task for artificial intelligence aided molecular design. A growing
number of Graph Neural Networks (GNNs) have been proposed to address this
challenge. These models improve their expressive power by incorporating
auxiliary information in molecules while inevitably increase their
computational complexity. In this work, we aim to design a GNN which is both
powerful and efficient for molecule structures. To achieve such goal, we
propose a molecular mechanics-driven approach by first representing each
molecule as a two-layer multiplex graph, where one layer contains only local
connections that mainly capture the covalent interactions and another layer
contains global connections that can simulate non-covalent interactions. Then
for each layer, a corresponding message passing module is proposed to balance
the trade-off of expression power and computational complexity. Based on these
two modules, we build Multiplex Molecular Graph Neural Network (MXMNet). When
validated by the QM9 dataset for small molecules and PDBBind dataset for large
protein-ligand complexes, MXMNet achieves superior results to the existing
state-of-the-art models under restricted resources.
