  Motivation: Predicting Drug-Target Interaction (DTI) is a well-studied topic
in bioinformatics due to its relevance in the fields of proteomics and
pharmaceutical research. Although many machine learning methods have been
successfully applied in this task, few of them aim at leveraging the inherent
heterogeneous graph structure in the DTI network to address the challenge. For
better learning and interpreting the DTI topological structure and the
similarity, it is desirable to have methods specifically for predicting
interactions from the graph structure.
  Results: We present an end-to-end framework, DTI-GAT (Drug-Target Interaction
prediction with Graph Attention networks) for DTI predictions. DTI-GAT
incorporates a deep neural network architecture that operates on
graph-structured data with the attention mechanism, which leverages both the
interaction patterns and the features of drug and protein sequences. DTI-GAT
facilitates the interpretation of the DTI topological structure by assigning
different attention weights to each node with the self-attention mechanism.
Experimental evaluations show that DTI-GAT outperforms various state-of-the-art
systems on the binary DTI prediction problem. Moreover, the independent study
results further demonstrate that our model can be generalized better than other
conventional methods.
  Availability: The source code and all datasets are available at
https://github.com/Haiyang-W/DTI-GRAPH
