  Predicting molecular properties (e.g., atomization energy) is an essential
issue in quantum chemistry, which could speed up much research progress, such
as drug designing and substance discovery. Traditional studies based on density
functional theory (DFT) in physics are proved to be time-consuming for
predicting large number of molecules. Recently, the machine learning methods,
which consider much rule-based information, have also shown potentials for this
issue. However, the complex inherent quantum interactions of molecules are
still largely underexplored by existing solutions. In this paper, we propose a
generalizable and transferable Multilevel Graph Convolutional neural Network
(MGCN) for molecular property prediction. Specifically, we represent each
molecule as a graph to preserve its internal structure. Moreover, the
well-designed hierarchical graph neural network directly extracts features from
the conformation and spatial information followed by the multilevel
interactions. As a consequence, the multilevel overall representations can be
utilized to make the prediction. Extensive experiments on both datasets of
equilibrium and off-equilibrium molecules demonstrate the effectiveness of our
model. Furthermore, the detailed results also prove that MGCN is generalizable
and transferable for the prediction.
