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.

翻译：药物-药物相互作用（DDI）预测问题旨在利用药物信息及已知的多对药物副作用（非预期结果），来预测一对药物可能产生的副作用。该问题可被建模为DDI图中每对节点的标签（即副作用）预测任务，其中节点代表药物，边代表具有已知标签的相互作用药物。当前最先进的方法采用图神经网络（GNN），通过利用图中的邻居信息学习节点表示。然而，对于DDI而言，由于副作用的特性，存在大量具有复杂关联关系的标签。常规GNN通常将标签固定为独热向量，这无法反映标签间的关联关系，且在低频标签等困难场景下可能无法达到最佳性能。本文中，我们将DDI建模为超图，其中每条超边包含三个节点：两个药物节点和一个标签节点。我们提出CentSmoothie，一种超图神经网络，通过新颖的中央平滑公式联合学习节点与标签的表示。通过仿真实验和真实数据集验证，我们实证展示了CentSmoothie的性能优势。