Drug-disease networks and drug repurposing

TL;DR

This paper introduces a novel drug-disease network constructed from multiple data sources and applies network-based link prediction methods, especially graph embedding, to identify promising drug repurposing opportunities with high accuracy.

Contribution

It presents a new integrated drug-disease network and demonstrates the effectiveness of advanced network prediction methods for drug repurposing.

Findings

Prediction performance with AUC above 0.95

Graph embedding methods outperform previous approaches

Average precision nearly a thousand times better than chance

Abstract

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.