Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction.

Identifying the potential side effects for the interested drugs can help reduce harm to patients caused by drugs in clinical use and decrease the risk of drug development failure. Multiple functionally similar drugs often have multiple similar side effects, resulting in the closed relationships among these nodes. However, most of previous methods did not completely encode the features from the biological perspective to mine the complex associations between the drugs and side effects. A prediction model based on interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning, ICAL, was proposed to fuse the global correlations reflected by multiple hypergraphs and to learn the attributes of a pair of drug and side effect nodes enhanced by the channels and attributes. First, we designed a hypergraph architecture where a hyperedge reflects the complex correlations between a single drug (side effect) and all the drugs and side effects, and the entire hypergraph composed of the hyperedges reveals the global correlations of all the drugs and side effects. Two hypergraphs were established based on two types of drug similarities, and each hypergraph implies its specific complex relationships among multiple drugs and side effects. Second, we proposed an interactive hypergraph neural network to enable the learning of global correlation features of drugs and side effects from the two hypergraphs. It propagated the node features across multiple hypergraphs and encoded the context relationships within these hypergraphs. Besides, the attentions at the channel level and at the attribute level were proposed to integrate the semantic correlations among multiple channels and to encode the long-distance dependence within the attributes of a pair of drug and side effect. The experimental results based on cross-validation showed that our new model outperformed seven advanced prediction methods in terms of AUC, AUPR, and recall rates for the top-ranked candidates. The ablation studies showed the effectiveness of global correlation learning, node feature propagation across multiple hypergraphs, and channel and attribute enhanced pairwise attribute learning. The case studies on the candidate side effects related to five drugs further demonstrated ICAL's effective application in discovering the reliable candidates.