Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

TL;DR

This paper introduces a novel embedding approach that integrates multiple heterogeneous data sources, including images, strings, and relational data, to improve the prediction of drug-drug interactions using deep learning.

Contribution

It is the first work to combine diverse data types such as images, strings, and relational data for DDI prediction with deep networks.

Findings

Heterogeneous data integration improves DDI prediction accuracy.

Deep networks effectively combine multiple data sources.

Outperforms state-of-the-art methods on various data types.

Abstract

Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual representation of the drug structures. We present the first work that uses multiple data sources such as drug structure images, drug structure string representation and relational representation of drug relationships as the input. To this effect, we exploit the recent advances in deep networks to integrate these varied sources of inputs in predicting DDIs. Our empirical evaluation against several state-of-the-art methods using standalone different data types for drugs clearly demonstrate the efficacy of combining heterogeneous data in predicting DDIs.