Predicting drug side-effects using deep representation learning for graphs
Marinka Žitnik, Jure Leskovec · 2019
Polypharmacy, the use of drug combinations, is common to treat patients with complex or co-existing conditions. However, a major consequence of polypharmacy is a high risk of adverse side effects, which emerge because of drug-drug interactions, in which activity of one drug changes if taken with another drug. We present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon develops a graph convolutional neural network for multirelational link prediction that is designed to handle such multimodal graphs with a large number of edge types. Decagon predicts the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. Decagon models particularly well side effects with a strong molecular basis, while on non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon creates opportunities to use large pharmacogenomic and patient data to flag polypharmacy side effects for follow-up pharmacological analysis. Project website: http://snap.stanford.edu/decagon.