Knowledge Graph Completion to Predict Polypharmacy Side Effects
This work addresses a critical healthcare issue for patients and clinicians by improving prediction of adverse drug interactions, though it is incremental as it builds on existing knowledge graph methods.
The paper tackles the problem of predicting polypharmacy side effects, where drug combinations cause side effects not seen with individual drugs, and demonstrates that multi-relational knowledge graph completion achieves state-of-the-art results, with effectiveness linked to well-characterized drug protein targets.
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.