  The identification of drug-target binding affinity (DTA) has attracted
increasing attention in the drug discovery process due to the more specific
interpretation than binary interaction prediction. Recently, numerous deep
learning-based computational methods have been proposed to predict the binding
affinities between drugs and targets benefiting from their satisfactory
performance. However, the previous works mainly focus on encoding biological
features and chemical structures of drugs and targets, with a lack of
exploiting the essential topological information from the drug-target affinity
network. In this paper, we propose a novel hierarchical graph representation
learning model for the drug-target binding affinity prediction, namely
HGRL-DTA. The main contribution of our model is to establish a hierarchical
graph learning architecture to incorporate the intrinsic properties of
drug/target molecules and the topological affinities of drug-target pairs. In
this architecture, we adopt a message broadcasting mechanism to integrate the
hierarchical representations learned from the global-level affinity graph and
the local-level molecular graph. Besides, we design a similarity-based
embedding map to solve the cold start problem of inferring representations for
unseen drugs and targets. Comprehensive experimental results under different
scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art
models and shows better model generalization among all the scenarios.
