Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network

Understand

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.