  Predicting drug-drug interactions (DDI) is the problem of predicting side
effects (unwanted outcomes) of a pair of drugs using drug information and known
side effects of many pairs. This problem can be formulated as predicting labels
(i.e. side effects) for each pair of nodes in a DDI graph, of which nodes are
drugs and edges are interacting drugs with known labels. State-of-the-art
methods for this problem are graph neural networks (GNNs), which leverage
neighborhood information in the graph to learn node representations. For DDI,
however, there are many labels with complicated relationships due to the nature
of side effects. Usual GNNs often fix labels as one-hot vectors that do not
reflect label relationships and potentially do not obtain the highest
performance in the difficult cases of infrequent labels. In this paper, we
formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for
drugs and one node for a label. We then present CentSmoothie, a hypergraph
neural network that learns representations of nodes and labels altogether with
a novel central-smoothing formulation. We empirically demonstrate the
performance advantages of CentSmoothie in simulations as well as real datasets.
