Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Prachayasittikul, Veda | - |
dc.contributor.author | Worachartcheewan, Apilak | - |
dc.contributor.author | Shoombuatong, Watshara | - |
dc.contributor.author | Prachayasittikul, Virapong | - |
dc.contributor.author | Nantasenamat, Chanin | - |
dc.date.accessioned | 2016-06-06T10:50:32Z | - |
dc.date.available | 2016-06-06T10:50:32Z | - |
dc.date.issued | 2015-08-19 | - |
dc.identifier.issn | 1611-2156 | - |
dc.identifier.uri | http://hdl.handle.net/2003/35041 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-17089 | - |
dc.description.abstract | P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 noninhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance. | en |
dc.language.iso | en | - |
dc.relation.ispartofseries | EXCLI Journal;Vol. 14, 2015 | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | P-glycoprotein | en |
dc.subject | ADMET | en |
dc.subject | multidrug resistance | en |
dc.subject | QSAR | en |
dc.subject | data mining | en |
dc.subject.ddc | 610 | - |
dc.title | Classification of P-glycoprotein-interacting compounds using machine learning methods | en |
dc.type | Text | - |
dc.identifier.doi | 10.17179/excli2015-374 | - |
dc.type.publicationtype | article | - |
dcterms.accessRights | open access | - |
eldorado.dnb.zdberstkatid | 2132560-1 | - |
Appears in Collections: | Original Articles |
Files in This Item:
File | Description | Size | Format | |
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Nantasenamat_19082015_proof.pdf | DNB | 3.51 MB | Adobe PDF | View/Open |
Nantasenamat_Supplementary_material_19082015_proof.pdf | DNB | 1.27 MB | Adobe PDF | View/Open |
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