  The discovery of drug-target interactions (DTIs) plays a crucial role in
pharmaceutical development. The deep learning model achieves more accurate
results in DTI prediction due to its ability to extract robust and expressive
features from drug and target chemical structures. However, existing deep
learning methods typically generate drug features via aggregating molecular
atom representations, ignoring the chemical properties carried by motifs, i.e.,
substructures of the molecular graph. The atom-drug double-level molecular
representation learning can not fully exploit structure information and fails
to interpret the DTI mechanism from the motif perspective. In addition,
sequential model-based target feature extraction either fuses limited
contextual information or requires expensive computational resources. To tackle
the above issues, we propose a hierarchical graph representation learning-based
DTI prediction method (HiGraphDTI). Specifically, HiGraphDTI learns
hierarchical drug representations from triple-level molecular graphs to
thoroughly exploit chemical information embedded in atoms, motifs, and
molecules. Then, an attentional feature fusion module incorporates information
from different receptive fields to extract expressive target features.Last, the
hierarchical attention mechanism identifies crucial molecular segments, which
offers complementary views for interpreting interaction mechanisms. The
experiment results not only demonstrate the superiority of HiGraphDTI to the
state-of-the-art methods, but also confirm the practical ability of our model
in interaction interpretation and new DTI discovery.
