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
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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“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]
Citations
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Cited by
Sorted by citations
Zhang, B., Shi, H., & Wang, H. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s410301 · Cited by 347
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
“…to characterize drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects and diseases 6,38–40, As with proteins, small molecules are modelled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
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Learning spatial structures of proteins improves protein–protein interaction prediction
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Self-citationComprehensive evaluation of deep and graph learning on drug–drug interactions prediction
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Self-citation
Metadata
- 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
- 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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