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

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Toward better drug discovery with knowledge graph

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

DOI 10.1016/j.sbi.2021.09.003

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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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Authors: Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y.
Affiliations: Hunan University (CN)
Anhui University (CN)
Corresponding author: Liu, Yuansheng
ORCID: Zeng, Xiangxiang
Liu, Yuansheng
Fu, Xiangzheng
Su, Yansen
Venue: Current Opinion in Structural Biology
Publisher: Elsevier BV
Published: 2022-02
Volume: 72
Pages: 114-126
ISSN: 0959-440X (ISSN-L)
DOI: 10.1016/j.sbi.2021.09.003
OpenAlex: W3207365242
Cited by: 229
References: 101
Citations by year: 2026: 20 · 2025: 62 · 2024: 53 · 2023: 64 · 2022: 28
FWCI: 2.85
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Health Sciences › Medicine › Health Informatics › 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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Toward better drug discovery with knowledge graph

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