Cited by 229Computational Drug Discovery MethodsAdvanced Graph Neural NetworksBiomedical Text Mining and Ontologies

Toward better drug discovery with knowledge graph

Zeng, Xiangxiang, Tu, Xinqi, Liu, Yuansheng, Fu, Xiangzheng, Su, Yansen·2022·Current Opinion in Structural Biology

Abstract

Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.

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Crossref · 10.1016/j.sbi.2021.09.003

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  • Graph representation learning has been successful in characterizing drugs at the systems level without patient data to predict interactions with other drugs, protein targets, side effects, and diseases.

    “Graph representation learning has been successful in characterizing drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects, diseases [6, 38, 39, 40, 48, 165].”

    From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [40]

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Authors
Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y.
Affiliations
  • Hunan University (CN)
  • Anhui University (CN)
Corresponding author
Liu, Yuansheng
Venue
Current Opinion in Structural Biology
Publisher
Elsevier BV
Published
2022-02
Volume
72
Pages
114-126
ISSN
0959-440X (ISSN-L)
OpenAlex
W3207365242
Cited by
229
References
101
Citations by year
2026: 20 · 2025: 62 · 2024: 53 · 2023: 64 · 2022: 28 · 2021: 2
FWCI
2.85
Type
journal article
Language
en
Open access
closed
Locations
  • Current Opinion in Structural Biology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Fundamental Research Funds for the Central Universities (531118010626)
  • National Natural Science Foundation of China (61872309)
  • National Key Research and Development Program of China (2021YFE0102100)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
knowledge graphs, drug discovery, drug repurposing, adverse drug reaction prediction, knowledge representation learning, entity embeddings, semantic relations
MeSH terms
Algorithms, Pattern Recognition, Automated, Knowledge, Drug Discovery, Drug Repositioning

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