Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models.
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| Title: | Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models. |
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| Authors: | Yuan, Jintao1,2, Yu, Shuling3 yushuling@henu.edu.cn, Gao, Shufang3, Gan, Ying3, Zhang, Yi3, Zhang, Ting2, Wang, Yali3, Yang, Liu3,4, Shi, Jiahua3 sjiahua@henu.edu.cn, Yao, Wu1 yaowu@zzu.edu.cn |
| Source: | Chemometrics & Intelligent Laboratory Systems. Aug2016, Vol. 156, p166-173. 8p. |
| Subjects: | Triazole derivatives, Multilayer perceptrons, QSAR models, Support vector machines, Artificial neural networks |
| Abstract: | Quantitative structure–activity relationship (QSAR) studies were performed in this work to predict the pIC 50 of non-glycoside sodium-dependent glucose cotransporter-2 (SGLT2) inhibitors (46 triazole derivatives). Four descriptors were selected from the pool of DRAGON descriptors using the enhanced replacement method. Three nonlinear regression methods—multilayer perceptron neural network (MLP NN), support vector machine (SVM), and projection pursuit regression (PPR)—were then used to build the QSAR models. The performance of the obtained models was assessed through the cross-validation and external validation of the test set. PPR produced a better model than MLP NN and SVM with coefficients of determination of 0.962 and 0.871 and root mean square errors of 0.162 and 0.471 for the training and test sets, respectively. This study developed simple and efficient approaches to predict the pIC 50 of triazole derivatives and provided some insights into their structural features for the development of more potential SGLT2 inhibitors. [ABSTRACT FROM AUTHOR] |
| Copyright of Chemometrics & Intelligent Laboratory Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 116521512 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Jintao%22">Yuan, Jintao</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Shuling%22">Yu, Shuling</searchLink><relatesTo>3</relatesTo><i> yushuling@henu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Shufang%22">Gao, Shufang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Gan%2C+Ying%22">Gan, Ying</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yi%22">Zhang, Yi</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Ting%22">Zhang, Ting</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yali%22">Wang, Yali</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Liu%22">Yang, Liu</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Shi%2C+Jiahua%22">Shi, Jiahua</searchLink><relatesTo>3</relatesTo><i> sjiahua@henu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yao%2C+Wu%22">Yao, Wu</searchLink><relatesTo>1</relatesTo><i> yaowu@zzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Chemometrics+%26+Intelligent+Laboratory+Systems%22">Chemometrics & Intelligent Laboratory Systems</searchLink>. Aug2016, Vol. 156, p166-173. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Triazole+derivatives%22">Triazole derivatives</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22QSAR+models%22">QSAR models</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Quantitative structure–activity relationship (QSAR) studies were performed in this work to predict the pIC 50 of non-glycoside sodium-dependent glucose cotransporter-2 (SGLT2) inhibitors (46 triazole derivatives). Four descriptors were selected from the pool of DRAGON descriptors using the enhanced replacement method. Three nonlinear regression methods—multilayer perceptron neural network (MLP NN), support vector machine (SVM), and projection pursuit regression (PPR)—were then used to build the QSAR models. The performance of the obtained models was assessed through the cross-validation and external validation of the test set. PPR produced a better model than MLP NN and SVM with coefficients of determination of 0.962 and 0.871 and root mean square errors of 0.162 and 0.471 for the training and test sets, respectively. This study developed simple and efficient approaches to predict the pIC 50 of triazole derivatives and provided some insights into their structural features for the development of more potential SGLT2 inhibitors. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Chemometrics & Intelligent Laboratory Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.chemolab.2016.06.002 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 166 Subjects: – SubjectFull: Triazole derivatives Type: general – SubjectFull: Multilayer perceptrons Type: general – SubjectFull: QSAR models Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuan, Jintao – PersonEntity: Name: NameFull: Yu, Shuling – PersonEntity: Name: NameFull: Gao, Shufang – PersonEntity: Name: NameFull: Gan, Ying – PersonEntity: Name: NameFull: Zhang, Yi – PersonEntity: Name: NameFull: Zhang, Ting – PersonEntity: Name: NameFull: Wang, Yali – PersonEntity: Name: NameFull: Yang, Liu – PersonEntity: Name: NameFull: Shi, Jiahua – PersonEntity: Name: NameFull: Yao, Wu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 01697439 Numbering: – Type: volume Value: 156 Titles: – TitleFull: Chemometrics & Intelligent Laboratory Systems Type: main |
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