  Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is
one of the most important tasks in drug development and medical practice.
Recently graph neural networks have achieved great success in this task by
modeling drugs as nodes and drug-drug interactions as links and casting DDI
predictions as link prediction problems. However, correlations between link
labels (e.g., DDI types) were rarely considered in existing works. We propose
the graph energy neural network (GENN) to explicitly model link type
correlations. We formulate the DDI prediction task as a structure prediction
problem and introduce a new energy-based model where the energy function is
defined by graph neural networks. Experiments on two real-world DDI datasets
demonstrated that GENN is superior to many baselines without consideration of
link type correlations and achieved $13.77\%$ and $5.01\%$ PR-AUC improvement
on the two datasets, respectively. We also present a case study in which \mname
can better capture meaningful DDI correlations compared with baseline models.
