M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects

Qidong Liu, Enguang Yao, Chaoyue Liu, Xin Zhou, Yafei Li, Mingliang Xu

Abstract: Treating patients with complex diseases or co-existing conditions by polypharmacy (i.e., the use of drug combination) is very common. However, due to drug-drug interactions, polypharmacy often results in unpredictable side effects, which may endanger patients’ life. Moreover, since adverse drug reactions are rare, discovering polypharmacy side effects only from sparse drug-drug interactions remains challenging. Thus, it is necessary to explore the knowledge of polypharmacy side effects from the interaction network with complex relationships. In this paper, we propose a novel multi-modal graph convolutional neural network (M2GCN) for link prediction in multi-modal networks which consist of protein-protein interactions, drug-protein interactions and drug-drug interactions. Specifically, we first propose a propagation strategy to perform graph aggregations on each subgraph. Then we leverage consistency regularization to align the consistency across different subgraphs. Finally, referring to the DistMult method, we use the embeddings obtained above to calculate the probability of side effects between drugs. Experimental results on benchmark dataset show that our method significantly outperforms the compared network embedding models.