  Much of the recent work on learning molecular representations has been based
on Graph Convolution Networks (GCN). These models rely on local aggregation
operations and can therefore miss higher-order graph properties. To remedy
this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are
explicitly built on longer-range dependencies in graph-structured data.
Specifically, we use path features in molecular graphs to create global
attention layers. We compare our PAGTN model against the GCN model and show
that our model consistently outperforms GCNs on molecular property prediction
datasets including quantum chemistry (QM7, QM8, QM9), physical chemistry (ESOL,
Lipophilictiy) and biochemistry (BACE, BBBP).
