Knowledge Graph Completion to Predict Polypharmacy Side Effects

TL;DR

This paper presents a knowledge graph completion method for predicting side effects in polypharmacy, offering interpretable results and improved accuracy, especially with well-characterized drug targets.

Contribution

The work introduces a novel application of multi-relational knowledge graph completion to polypharmacy side effect prediction, enhancing interpretability and performance.

Findings

Achieves state-of-the-art results in side effect prediction

More effective with well-characterized drug targets

Provides interpretable predictions for experimental validation

Abstract

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.