Predicting Polypharmacy Side Effects Based on an Enhanced Domain Knowledge Graph

Abstract

The use of multiple drugs, termed Polypharmacy, is a common prescription for treatment. However, Polypharmacy is likely to cause unknown side effects, which can seriously affect patients’ health. Given the complexity of the Drug-Drug Interactions (DDIs), recent approaches mainly leverage knowledge graphs to predict DDIs, formulated as a multi-relational link prediction problem. The accuracy of such approaches relies on the comprehensibility of the established knowledge graphs. In this paper, we explore the factors of DDIs in-depth and propose an effective DDIs prediction method based on reasonable domain knowledge. Specifically, we established a comprehensive knowledge graph, which takes the interrelationships among drugs, genes, and enzymes into account, on top of a baseline knowledge graph. We then train and apply a Convolutional Neural Network (CNN) to predict the type and probability of DDIs among drug pairs. We verify the impact of different factors on performance. Under the same data conditions as the baseline, the results of which show that the accuracy of our proposal is 12% higher than the baseline approach.