The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Predicting Drug–Target Interactions Using Probabilistic Matrix Factorization
Open accessAbstract
Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.
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Cites
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
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
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
Can we rationally design promiscuous drugs?
HOPKINS, A., MASON, J., & OVERINGTON, J. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.01.013 · Cited by 469
Cited by
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
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska-Coleska, Z., & Najarian, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz157 · Cited by 429
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction
Liu, Y., Wu, M., Miao, C., Zhao, P., & Li, X.-L. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004760 · Cited by 391
Machine Learning for Drug-Target Interaction Prediction
Chen, R., Liu, X., Jin, S., Lin, J., & Liu, J. (2018) · Molecules · doi:10.3390/molecules23092208 · Cited by 292
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154
“…on information from 72 compounds to make predictions over a 10,833-compound library is a more imbalanced train and test split than other reported drug-target interaction prediction settings (Cobanoglu et al., 2013; Hie et al., 2018; Luo et al., 2017; O¨zt € urk et al., 2018; Zheng et al., 2013).”
BackgroundMethod
Metadata
- Authors
- Cobanoglu, M. C., Liu, C., Hu, F., Oltvai, Z. N., & Bahar, I.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-12-10
- Volume
- 53
- Issue
- 12
- Pages
- 3399-3409
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W1466286227
- Cited by
- 187
- References
- 4
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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