  Molecular structure-property relationships are key to molecular engineering
for materials and drug discovery. The rise of deep learning offers a new viable
solution to elucidate the structure-property relationships directly from
chemical data. Here we show that the performance of graph convolutional
networks (GCNs) for the prediction of molecular properties can be improved by
incorporating attention and gate mechanisms. The attention mechanism enables a
GCN to identify atoms in different environments. The gated skip-connection
further improves the GCN by updating feature maps at an appropriate rate. We
demonstrate that the resulting attention- and gate-augmented GCN could extract
better structural features related to a target molecular property such as
solubility, polarity, synthetic accessibility and photovoltaic efficiency
compared to the vanilla GCN. More interestingly, it identified two distinct
parts of molecules as essential structural features for high photovoltaic
efficiency, and each of them coincided with the areas of donor and acceptor
orbitals for charge-transfer excitations, respectively. As a result, the new
model could accurately predict molecular properties and place molecules with
similar properties close to each other in a well-trained latent space, which is
critical for successful molecular engineering.
