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Discovering Protein Drug Targets Using Knowledge Graph Embeddings

Research output: Contribution to journalArticle

Original languageEnglish
Article numberbtz600
Number of pages8
JournalBioinformatics
Early online date1 Aug 2019
DateAccepted/In press - 25 Jul 2019
DateE-pub ahead of print (current) - 1 Aug 2019

Abstract

Motivation
Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates.

Results
We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representaions (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision-recall curves in standard benchmark tests.

Availability
The data, predictions, and models are available at: drugtargets.insight-centre.org

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Oxford University Press at https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btz600/5542390/. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 207 KB, PDF document

    Embargo ends: 1/08/20

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