  Motivation: Identifying drug-target interactions (DTIs) is a key step in drug
repositioning. In recent years, the accumulation of a large number of genomics
and pharmacology data has formed mass drug and target related heterogeneous
networks (HNs), which provides new opportunities of developing HN-based
computational models to accurately predict DTIs. The HN implies lots of useful
information about DTIs but also contains irrelevant data, and how to make the
best of heterogeneous networks remains a challenge. Results: In this paper, we
propose a heterogeneous graph automatic meta-path learning based DTI prediction
method (HampDTI). HampDTI automatically learns the important meta-paths between
drugs and targets from the HN, and generates meta-path graphs. For each
meta-path graph, the features learned from drug molecule graphs and target
protein sequences serve as the node attributes, and then a node-type specific
graph convolutional network (NSGCN) which efficiently considers node type
information (drugs or targets) is designed to learn embeddings of drugs and
targets. Finally, the embeddings from multiple meta-path graphs are combined to
predict novel DTIs. The experiments on benchmark datasets show that our
proposed HampDTI achieves superior performance compared with state-of-the-art
DTI prediction methods. More importantly, HampDTI identifies the important
meta-paths for DTI prediction, which could explain how drugs connect with
targets in HNs.
