The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Network-Based Methods for Prediction of Drug-Target Interactions
Open accessAbstract
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
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
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Drug repositioning: identifying and developing new uses for existing drugs
Ashburn, T. T., & Thor, K. B. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1468 · Cited by 3,237
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667
Drug target identification using side-effect similarity
Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008) · Science · doi:10.1126/science.1158140 · Cited by 1,248
Roth, B. L., Sheffler, D. J., & Kroeze, W. K. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1346 · Cited by 1,155
Data-driven prediction of drug effects and interactions
Tatonetti, N. P., Ye, P. P., Daneshjou, R., & Altman, R. B. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003377 · Cited by 936
Similarity-based virtual screening using 2D fingerprints
Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849
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Drug repurposing for cancer therapy
Xia, Y., Sun, M., Huang, H., & Jin, W.-L. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01808-1 · Cited by 443
Artificial Intelligence for Drug Toxicity and Safety
Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 363
Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021) · Nature Communications · doi:10.1038/s41467-021-27137-3 · Cited by 261
Peng, J., Li, J., & Shang, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03677-1 · Cited by 133
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
Thafar, M. A., Olayan, R. S., Albaradei, S., Bajic, V. B., Gojobori, T., Essack, M., & Gao, X. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00552-w · Cited by 70
Metadata
- Authors
- Wu, Z., Li, W., Liu, G., & Tang, Y.
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2018-10-09
- Volume
- 9
- Pages
- 1134-1134
- ISSN
- 1663-9812 (ISSN-L)
- OpenAlex
- W2896646632
- Cited by
- 237
- References
- 18
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- gold
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