  Background: Discovering potential drug-drug interactions (DDIs) is a
long-standing challenge in clinical treatments and drug developments. Recently,
deep learning techniques have been developed for DDI prediction. However, they
generally require a huge number of samples, while known DDIs are rare.
  Methods: In this work, we present KnowDDI, a graph neural network-based
method that addresses the above challenge. KnowDDI enhances drug
representations by adaptively leveraging rich neighborhood information from
large biomedical knowledge graphs. Then, it learns a knowledge subgraph for
each drug-pair to interpret the predicted DDI, where each of the edges is
associated with a connection strength indicating the importance of a known DDI
or resembling strength between a drug-pair whose connection is unknown. Thus,
the lack of DDIs is implicitly compensated by the enriched drug representations
and propagated drug similarities.
  Results: We evaluate KnowDDI on two benchmark DDI datasets. Results show that
KnowDDI obtains the state-of-the-art prediction performance with better
interpretability. We also find that KnowDDI suffers less than existing works
given a sparser knowledge graph. This indicates that the propagated drug
similarities play a more important role in compensating for the lack of DDIs
when the drug representations are less enriched.
  Conclusions: KnowDDI nicely combines the efficiency of deep learning
techniques and the rich prior knowledge in biomedical knowledge graphs. As an
original open-source tool, KnowDDI can help detect possible interactions in a
broad range of relevant interaction prediction tasks, such as protein-protein
interactions, drug-target interactions and disease-gene interactions,
eventually promoting the development of biomedicine and healthcare.
