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Knowledge Graph Completion to Predict Polypharmacy Side Effects

  • Brandon MaloneEmail author
  • Alberto García-Durán
  • Mathias Niepert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)

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.

Keywords

Knowledge graph Embedding Side effect prediction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NEC Laboratories EuropeHeidelbergGermany

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