The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Identifying drug–target interactions based on graph convolutional network and deep neural network
Abstract
Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.
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Cites
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
Drug–target interaction prediction: databases, web servers and computational models
Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv066 · Cited by 658
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584
Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design
Veselinovic, A., Veselinovic, J., Zivkovic, J., & Nikolic, G. (2015) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026615666150506151533 · Cited by 82
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Discovery of senolytics using machine learning
Smer-Barreto, V., Quintanilla, A., Elliott, R. J. R., Dawson, J. C., Sun, J., Campa, V. M., Lorente-Macías, Á., Unciti-Broceta, A., Carragher, N. O., Acosta, J. C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39120-1 · Cited by 139
Drug–target interaction predication via multi-channel graph neural networks
Li, Y., Qiao, G., Wang, K., & Wang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab346 · Cited by 112
Wang, X., Liu, M., Zhang, Y., He, S., Qin, C., Li, Y., & Lu, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab289 · Cited by 16
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Metadata
- Authors
- Zhao, T., Hu, Y., Valsdottir, L. R., Zang, T., & Peng, J.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-04
- Volume
- 22
- Issue
- 2
- Pages
- 2141-2150
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W3021338900
- Cited by
- 332
- References
- 6
- FWCI
- 19.89
- Type
- journal article
- Language
- en
- Open access
- closed
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