Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks

Numerous clinical trials have revealed that a serious consequence of polypharmacy is that patients are at high risk of adverse side effects. However, designing clinical trials to determine the frequency of side effects from polypharmacy is both time-consuming and costly. Therefore, the computer-aided prediction of drug side effects is becoming an attractive proposition. Existing methods of drug side effects prediction introduce the target protein of a drug without screening. Although this alleviates the sparsity of the original data to some extent, the blind introduction of proteins as auxiliary information allows a large amount of noisy information to be added, which degrades the model efficiency and acheive sub-opitmal predicition results. To this end, we propose a novel method called DEP-GCN (Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks). Specifically, we design two protein auxiliary pathways directly related to drugs and combine these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviate the sparsity of data and filter out noisy data. Then, to produce accurate drug representations, we distinguish the impact from different drug neighbors and introduce a query-aware attention mechanism to fine-grained determine how much messaging is delivered. Finally, in contrast to approaches limited to predicting the existence or associations of drug side effects, we output the exact frequency of drug side effects occurring via a tensor factorization decoder. Extensive experimental results demonstrate that DEP-GCN significantly outperforms all baseline methods. The further examination provides literature evidence for highly ranked predictions.