Gorge: graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction

Determining the side effects of multidrug combinations is a very important issue in drug risk studies. However, designing clinical trials to determine frequencies is often time-consuming and expensive, and previous work has often been limited to using the target protein of a drug without screening. Although this alleviates the sparsity of the raw data to some extent, blindly introducing proteins as auxiliary information can lead to a large amount of noisy information being added, which in turn leads to less efficient models. For this reason, we propose a new method called Gorge (graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction). Specifically, we designed two protein auxiliary pathways directly related to drugs and combined these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviates data sparsity and filters noisy data. Then, we introduce a query-aware attention mechanism that generates different attention pathways for drug entities based on different drug pairs, fine-grained to determine the extent of information delivery. Finally, we output the exact frequency of drug side effects occurring through a tensor factorization decoder, in contrast to most existing methods that can only predict the presence or association of side effects, but not their frequency. We found that Gorge achieves excellent performance on real-world datasets (average AUROC of 0.822 and average AUPR of 0.775), outperforming existing methods. Further examination provides literature evidence for highly ranked predictions.