GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

Shengtian Sang, Zhihao Yang*, Xiaoxia Liu, Lei Wang*, Hongfei Lin, Jian Wang, Michel Dumontier

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Web of Science)

Abstract

Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, in this paper, we propose a biomedical knowledge graph embedding-based recurrent neural network method called GrEDeL, which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first builds a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for the current traditional drug discovery methods.

Original languageEnglish
Pages (from-to)8404-8415
Number of pages12
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Drug discovery
  • biomedical knowledge graph
  • recurrent neural network
  • deep learning
  • FISH-OIL
  • NEURAL-NETWORKS
  • PREDICTION
  • RAYNAUDS
  • SYSTEMS

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