A network inference method for large-scale unsupervised identification of novel drug-drug interactions

TL;DR

This paper introduces a novel network inference algorithm that predicts uncharacterized drug-drug interactions using only reported interaction data, applicable to various interaction types and scalable to large datasets.

Contribution

The proposed method uniquely predicts drug interactions without requiring pharmacological data, enabling large-scale and diverse interaction discovery.

Findings

Accurately predicts drug interactions in small and large datasets.

Effective in identifying interactions of new drugs during discovery.

Handles various interaction types without biochemical information.

Abstract

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.