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

The DOI matches its Crossref record; 93 of the 101 references it cites were found in a registry or an index.

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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Crossref · 10.1016/j.sbi.2021.09.003

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Research works printed but not linked: 10

  1. A. Hogan, E. Blomqvist, M. Cochez, C. d'Amato, G. de Melo, C. Gutierrez, J. E. L. Gayo, S. Kirrane, S. Neumaier, A. Polleres, et al., Knowledge graphs, arXiv preprint arXiv:2003.02320. · arXiv
  2. C. Su, Y. Hou, W. Guo, F. Chaudhry, G. Ghahramani, H. Zhang, F. Wang, Cbkh: the cornell biomedical knowledge hub, medRxiv.
  3. L. Chao, J. He, T. Wang, W. Chu, Pairre: knowledge graph embeddings via paired relation vectors, arXiv preprint arXiv:2011.03798. · arXiv
  4. I. Balažević, C. Allen, T. M. Hospedales, Tucker: tensor factorization for knowledge graph completion, arXiv preprint arXiv:1901.09590. · arXiv
  5. S. M. Kazemi, D. Poole, Simple embedding for link prediction in knowledge graphs, arXiv preprint arXiv:1802.04868. · arXiv
  6. H. N. Tran, A. Takasu, Multi-partition embedding interaction with block term format for knowledge graph completion, arXiv preprint arXiv:2006.16365. · arXiv
  7. T. Ma, J. Shang, C. Xiao, J. Sun, GENN: predicting correlated drug-drug interactions with graph energy neural networks, arXiv preprint arXiv:1910.02107. · arXiv
  8. Y. Wang, Y. Min, X. Chen, J. Wu, Multi-view graph contrastive representation learning for drug-drug interaction prediction, arXiv preprint arXiv:2010.11711. · arXiv
  9. Q. Wang, M. Li, X. Wang, N. Parulian, G. Han, J. Ma, J. Tu, Y. Lin, H. Zhang, W. Liu, et al., COVID-19 literature knowledge graph construction and drug repurposing report generation, arXiv preprint arXiv:2007.00576. · arXiv
  10. S. Zhao, B. Qin, T. Liu, F. Wang, Biomedical knowledge graph refinement with embedding and logic rules, arXiv preprint arXiv:2012.01031. · arXiv

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Metadata

Authors
Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y.
Affiliations
  • Hunan University (CN)
  • Anhui University (CN)
Corresponding author
Liu, Yuansheng
ORCID
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
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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