Knowledge-driven drug repurposing using a comprehensive drug knowledge graph

Yongjun Zhu, Chao Che, Bo Jin, Ningrui Zhang, Chang Su, Fei Wang

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

Original languageEnglish
Pages (from-to)2737-2750
Number of pages14
JournalHealth Informatics Journal
Issue number4
Publication statusPublished - 2020 Dec

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant No. NRF-2019S1A5A8033338).

Publisher Copyright:
© The Author(s) 2020.

All Science Journal Classification (ASJC) codes

  • Health Informatics


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