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)


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.


Knowledge graph Embedding Side effect prediction 


  1. 1.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems \(26\) (2013)Google Scholar
  2. 2.
    Cheng, F., Zhao, Z.: Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc. 21(e2), e278–e286 (2014)CrossRefGoogle Scholar
  3. 3.
    Fishman, D.A., Liu, Y., Ellerbroek, S.M., Stack, M.S.: Lysophosphatidic acid promotes matrix metalloproteinase (MMP) activation and MMP-dependent invasion in ovarian cancer cells. Cancer Res. 61(7), 3194–3199 (2001)Google Scholar
  4. 4.
    García-Durán, A., Niepert, M.: KBLRN: end-to-end learning of knowledge base representations with latent, relational, and numerical features. In: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (2018)Google Scholar
  5. 5.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)CrossRefGoogle Scholar
  6. 6.
    Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The SIDER database of drugs and side effects. Nucl. Acids Res. 44(D1), D1075–D1079 (2016)CrossRefGoogle Scholar
  7. 7.
    Manicone, A.M., McGuire, J.K.: Matrix metalloproteinases as modulators of inflammation. Semin. Cell Dev. Biol. 19(1), 34–41 (2008)CrossRefGoogle Scholar
  8. 8.
    Munshi, H.G., Wu, Y.I., Ariztia, E.V., Stack, M.S.: Calcium regulation of matrix metalloproteinase-mediated migration in oral squamous cell carcinoma cells. J. Biol. Chem. 277(44), 41480–41488 (2002)CrossRefGoogle Scholar
  9. 9.
    Sridhar, D., Fakhraei, S., Getoor, L.: A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics 32(20), 3175–3182 (2016)CrossRefGoogle Scholar
  10. 10.
    Szklarczyk, D., Santos, A., von Mering, C., Jensen, L.J., Bork, P., Kuhn, M.: STITCH 5: augmenting protein-checical interaction networks with tissue and affinity data. Nucl. Acids Res. 44, D380–D384 (2016)CrossRefGoogle Scholar
  11. 11.
    Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31 (2012)CrossRefGoogle Scholar
  12. 12.
    Yang, B., tau Yih, S.W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations (2015)Google Scholar
  13. 13.
    Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., Li, X.: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 18, 18 (2017)CrossRefGoogle Scholar
  14. 14.
    Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), 457–466 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NEC Laboratories EuropeHeidelbergGermany

Personalised recommendations