Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors
© 2017 Informa UK Limited, trading as Taylor & Francis Group. P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task...
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th-mahidol.420382019-03-14T15:03:04Z Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors V. Prachayasittikul A. Worachartcheewan A. P. Toropova A. A. Toropov N. Schaduangrat V. Prachayasittikul C. Nantasenamat Mahidol University Istituto di Ricerche Farmacologiche Mario Negri Biochemistry, Genetics and Molecular Biology Chemical Engineering © 2017 Informa UK Limited, trading as Taylor & Francis Group. P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors. 2018-12-21T06:56:40Z 2019-03-14T08:03:04Z 2018-12-21T06:56:40Z 2019-03-14T08:03:04Z 2017-01-02 Article SAR and QSAR in Environmental Research. Vol.28, No.1 (2017), 1-16 10.1080/1062936X.2016.1264468 1029046X 1062936X 2-s2.0-85008385164 https://repository.li.mahidol.ac.th/handle/123456789/42038 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008385164&origin=inward |
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Biochemistry, Genetics and Molecular Biology Chemical Engineering V. Prachayasittikul A. Worachartcheewan A. P. Toropova A. A. Toropov N. Schaduangrat V. Prachayasittikul C. Nantasenamat Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
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© 2017 Informa UK Limited, trading as Taylor & Francis Group. P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors. |
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Mahidol University |
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Mahidol University V. Prachayasittikul A. Worachartcheewan A. P. Toropova A. A. Toropov N. Schaduangrat V. Prachayasittikul C. Nantasenamat |
format |
Article |
author |
V. Prachayasittikul A. Worachartcheewan A. P. Toropova A. A. Toropov N. Schaduangrat V. Prachayasittikul C. Nantasenamat |
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V. Prachayasittikul |
title |
Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
title_short |
Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
title_full |
Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
title_fullStr |
Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
title_full_unstemmed |
Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors |
title_sort |
large-scale classification of p-glycoprotein inhibitors using smiles-based descriptors |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/42038 |
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