Target identification among known drugs by deep learning from heterogeneous networks
Open accessAbstract
Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.
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Machine learning is increasingly being explored as target identification technology, often by means of knowledge graphs.
“Although novel drug targets are typically identified through genomic, proteomic or phenotypic experiments, machine learning is increasingly being explored as target identification technology (52), often by means of knowledge graphs (53).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [53]
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References 141 · Cited by 357
Research works printed but not linked: 6
- Cao S. S., Lu W. and Xu Q. K., Thirtieth Aaai Conference on Artificial Intelligence, 2016, pp. 1145–1152
- Natarajan N., Rao N. and Dhillon I., 2015 LEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (Camsap), 2015, pp. 37–40
- Blaney S. M. Cole D. E. Balis F. M. Godwin K. Poplack D. G. Cancer Res. 1993 53 725 727 8428353
- O'Boyle N. M. Banck M. James C. A. Morley C. Vandermeersch T. Hutchison G. R. J. Cheminf. 2011 3 33 PMC3198950 21982300
- Koren Y. Bell R. Volinsky C. IEEE Comput. Soc. Press 2009 42 30 37
- Hu X. Wang Y. Hao L.-Y. Liu X. Lesch C. A. Sanchez B. M. Wendling J. M. Morgan R. W. Aicher T. D. Carter L. L. Nat. Chem. Biol. 2015 11 141 10.1038/nchembio.1714 25558972 · DOI
- Authors
- Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., Trapp, B. D., Nussinov, R., Eng, C., Loscalzo, J., & Cheng, F.
- Affiliations
- Hunan University (CN)
- Xiamen University (CN)
- Shanghai Key Laboratory of Regulatory Biology
- East China University of Science and Technology (CN)
- Cleveland Clinic (US)
- Guangzhou University of Chinese Medicine (CN)
- China Pharmaceutical University (CN)
- Brigham and Women's Hospital (US)
- Corresponding author
- Cheng, Feixiong
- ORCID
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2020
- Volume
- 11
- Issue
- 7
- Pages
- 1775-1797
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC8150105
- OpenAlex
- W2945027804
- Cited by
- 357
- References
- 141
- Citations by year
- 2026: 27 · 2025: 77 · 2024: 53 · 2023: 54 · 2022: 60 · 2021: 57 · 2020: 29
- FWCI
- 25.09
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Chemical Science (publishedVersion, cc-by-nc)
- SSRN Electronic Journal (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Foundation for the National Institutes of Health (HHSN261200800001E)
- National Institute of Neurological Disorders and Stroke (R3509730)
- National Heart, Lung, and Blood Institute (HG007690, HL119145, HL61795, K99HL138272, R00HL138272)
- American Heart Association (2017D007382)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug target identification, drug repurposing, heterogeneous networks, topotecan, RORγt, multiple sclerosis, retinoic acid receptor-related orphan receptor
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