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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Drug repurposing for COVID-19 via knowledge graph completion.

Rui Zhang1, Dimitar Hristovski2, Dalton Schutte1

  • 1Institute for Health Informatics and Department of Pharmaceutical Care & Health Systems, University of Minnesota, MN, USA.

Journal of Biomedical Informatics
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel literature-based discovery (LBD) approach using knowledge graphs to identify potential COVID-19 drug repurposing candidates. The method successfully predicted known and novel drugs, offering mechanistic insights for future research.

Keywords:
COVID-19Drug repurposingKnowledge graph completionLiterature-based discoveryText mining

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • COVID-19 drug repurposing is crucial for rapid therapeutic development.
  • Existing literature contains vast knowledge that can be leveraged for drug discovery.
  • Knowledge graph completion methods offer a powerful approach to extract hidden relationships from biomedical literature.

Purpose of the Study:

  • To develop and validate a neural network-based literature-based discovery (LBD) approach for identifying drug repurposing candidates for COVID-19.
  • To construct and analyze a knowledge graph from COVID-19 research literature.
  • To predict novel drug candidates and generate mechanistic hypotheses for their efficacy against COVID-19.

Main Methods:

  • Extracted semantic triples from PubMed and COVID-19 literature using SemRep (SemMedDB).
  • Filtered triples using an accuracy classifier based on a BERT variant (PubMedBERT) for high-quality data.
  • Constructed a knowledge graph and applied five neural knowledge graph completion algorithms (TransE, RotatE, DistMult, ComplEx, STELP).
  • Employed a time-slicing approach for model training and assessment, complemented by a discovery pattern-based analysis.

Main Results:

  • PubMedBERT-based accuracy classifier achieved high performance (F1 = 0.854).
  • TransE model demonstrated superior performance in knowledge graph completion (MR = 0.923, Hits@1 = 0.417).
  • Identified known and novel drug candidates for COVID-19 repurposing, including paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine.
  • Generated plausible mechanistic explanations for the potential therapeutic roles of identified drug candidates.

Conclusions:

  • The LBD approach is effective for discovering COVID-19 drug candidates and generating mechanistic insights.
  • The methodology can be generalized to identify drug candidates for other diseases and clinical questions.
  • The study provides a valuable resource (code and data) for further research in drug repurposing and LBD.