Skip to main content

Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory

Part of the Studies in Computational Intelligence book series (SCI,volume 1016)


The active global SARS-CoV-2 pandemic caused more than 167 million cases and 3.4 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process and despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of candidate drugs, 32 of which currently being in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware—we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.


  • Drug repurposing
  • Knowledge graphs
  • Link prediction
  • Collaborative filtering
  • Non-votile memory
  • NVM
  • MCAS
  • Python
  • PyMM

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-93413-2_61
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   309.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-93413-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   399.99
Price excludes VAT (USA)
Hardcover Book
USD   399.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.


  1. 1.

    Our implementation of the experiments and the model can be found here:

  2. 2.

    To precisely define the cosine similarity between two given drugs \(i,j\), let \(\hat{s}_{*i},\hat{s}_{*j}\) be their prediction scores along the disease dimension. Then their similarity is defined as \(\hat{s}_{*i}\cdot \hat{s}_{*j}\).


  1. Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673–683 (2004)

    Google Scholar 

  2. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NeurIPS), pp. 2787–2795 (2013)

    Google Scholar 

  3. Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)

    Google Scholar 

  4. Doshi, S., Prabhakar Chepuri, S.: Dr-COVID: graph neural networks for SARS-CoV-2 drug repurposing. CoRR (2020)

    Google Scholar 

  5. Frasca, F., Rossi, E., Eynard, D., Chamberlain, B., Bronstein, M.: Federico Monti. Scalable inception graph neural networks. CoRR, SIGN (2020)

    Google Scholar 

  6. Morselli Gysi, D., et al.: Network medicine framework for identifying drug repurposing opportunities for COVID-19. CoRR (2020)

    Google Scholar 

  7. Ioannidis, V.N., et al.: DRKG - drug repurposing knowledge graph for COVID-19 (2020).

  8. Izraelevitz, J., et al.: Basic performance measurements of the intel optane DC persistent memory module. CoRR, abs/1903.05714 (2019).

  9. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  10. Kipf, T.N., Welling. M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  11. Kißig, O., Taraz, M., Cohen, S., Doskoč, V., Friedrich, T.: Drug repurposing for multiple COVID strains using collaborative filtering. In: ICLR Workshop on Machine Learning for Preventing and Combating Pandemics (MLPCP@ICLR) (2021)

    Google Scholar 

  12. Kißig, O., Taraz, M., Cohen, S., Friedrich, T.: Drug repurposing using link prediction on knowledge graphs. In: ICML Workshop on Computational Biology (CompBio@ICML) (2021)

    Google Scholar 

  13. Nicola, M., et al.: The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 78, 185–1939 (2020)

    Google Scholar 

  14. Pandey, P.: COVID-19 clinical trials dataset (2021). Accessed 19 Feb 2021

  15. Pearson, K.: F.R.S. LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Phil. Mag. J. Sci. 2, 559–572 (1901)

    Google Scholar 

  16. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  17. Munir Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web (2001)

    Google Scholar 

  18. Scarselli, F., Gori, M., Chung Tsoi, A., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Google Scholar 

  19. Shah, B., Modi, P., Sagar, S.R.: In silico studies on therapeutic agents for COVID-19: drug repurposing approach. Life Sci. 252(3), 117652 (2020)

    Google Scholar 

  20. Waddington, D., Dickey, C., Hershcovitch, M., Seshadri, S.: An architecture for memory centric active storage (MCAS). arXiv preprint arXiv:2103.00007 (2021)

  21. Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)

  22. Wishart, D.S., et al.: Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018)

    Google Scholar 

  23. Wood, A., Hershcovitch, M., Waddington, D., Cohen, S., Chin, P.: Non-volatile memory accelerated posterior. In: IEEE High Performance Extreme Computing Conference (2021)

    Google Scholar 

  24. Wood, A., et al.: Non-volatile memory accelerated geometric multi-scale resolution analysis. In: IEEE High Performance Extreme Computing Conference (2021)

    Google Scholar 

  25. World Health Organization: International Clinical Trials Registry Platform (ICTRP) (2021). Accessed 19 Feb 2021

  26. Ye, C., Swiers, R., Bonner, S., Barrett, I.P.: Predicting potential drug targets using tensor factorisation and knowledge graph embeddings. CoRR, abs/2105.10578

  27. Zheng, D., et al.: DGL-KE: training knowledge graph embeddings at scale. In: SIGIR Conference on Research and Development in Information Retrieval (2020)

    Google Scholar 

  28. Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34, i457–i466 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Andrew Wood .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Cohen, S. et al. (2022). Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93412-5

  • Online ISBN: 978-3-030-93413-2

  • eBook Packages: EngineeringEngineering (R0)