QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses
A quantitative structure‐property relationship (QSPR) study was performed for the prediction of the Setschenow constants (Ksalt) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple...
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| Veröffentlicht in: | Journal of computational chemistry 2011-11, Vol.32 (15), p.3241-3252 |
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| creator | Xu, Jie Wang, Lei Wang, Luoxin Shen, Xiaolin Xu, Weilin |
| description | A quantitative structure‐property relationship (QSPR) study was performed for the prediction of the Setschenow constants (Ksalt) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R2 = 0.8680, while ANN and SVM higher values of R2 = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011 |
| doi_str_mv | 10.1002/jcc.21907 |
| format | Article |
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The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R2 = 0.8680, while ANN and SVM higher values of R2 = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants. © 2011 Wiley Periodicals, Inc. 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Comput. Chem</addtitle><description>A quantitative structure‐property relationship (QSPR) study was performed for the prediction of the Setschenow constants (Ksalt) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R2 = 0.8680, while ANN and SVM higher values of R2 = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011</description><subject>Analytical chemistry</subject><subject>artificial neural network</subject><subject>Chemical compounds</subject><subject>Methods</subject><subject>Molecular chemistry</subject><subject>Molecular structure</subject><subject>multiple linear regression</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Organic Chemicals - chemistry</subject><subject>QSPR</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Setschenow constant</subject><subject>Support Vector Machine</subject><issn>0192-8651</issn><issn>1096-987X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10F1P2zAUBmALbYLycbE_MFm7QUgE7NjxxyWrGNsohVLYuLOMY7N0adzlJGL993MpcDEJydKRjp7zynoR-kDJESUkP545d5RTTeQGGlCiRaaVvHuHBoTqPFOioFtoG2BGCGGF4JtoK6eKScH4AN1NplfXGLq-XOIY8NR34H75Jj5iFxvobNPBah_bB9tULi3ni9g3JeAequYBX4yuD_HJeHyIbVPi6Y-LNG29BA-76H2wNfi957mDbr-c3gy_ZqPLs2_Dk1HmmNIyY5QrTYpQlMW9Epo6p0NuC2s5l0Jor6gITChqCaUheO6ZIFRZWToXmCwZ20H769xFG__0Hjozr8D5uraNjz0YpbmispB5kp_-k7PYt-m7KyTy9DhP6GCNXBsBWh_Moq3mtl0aSsyqbJPKNk9lJ_vxObC_n_vyVb60m8DxGjxWtV--nWS-D4cvkdn6ooLO_329sO1vIySThfk5PjPnkyv1eTJSZsL-Af5vlXg</recordid><startdate>20111130</startdate><enddate>20111130</enddate><creator>Xu, Jie</creator><creator>Wang, Lei</creator><creator>Wang, Luoxin</creator><creator>Shen, Xiaolin</creator><creator>Xu, Weilin</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>7X8</scope></search><sort><creationdate>20111130</creationdate><title>QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses</title><author>Xu, Jie ; Wang, Lei ; Wang, Luoxin ; Shen, Xiaolin ; Xu, Weilin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3897-3148905f5d5b8691cc9f2a5aa447669e816f3681a011ffe4e36018a7dccf37d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Analytical chemistry</topic><topic>artificial neural network</topic><topic>Chemical compounds</topic><topic>Methods</topic><topic>Molecular chemistry</topic><topic>Molecular structure</topic><topic>multiple linear regression</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Organic Chemicals - chemistry</topic><topic>QSPR</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Setschenow constant</topic><topic>Support Vector Machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Jie</creatorcontrib><creatorcontrib>Wang, Lei</creatorcontrib><creatorcontrib>Wang, Luoxin</creatorcontrib><creatorcontrib>Shen, Xiaolin</creatorcontrib><creatorcontrib>Xu, Weilin</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of computational chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Jie</au><au>Wang, Lei</au><au>Wang, Luoxin</au><au>Shen, Xiaolin</au><au>Xu, Weilin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses</atitle><jtitle>Journal of computational chemistry</jtitle><addtitle>J. Comput. Chem</addtitle><date>2011-11-30</date><risdate>2011</risdate><volume>32</volume><issue>15</issue><spage>3241</spage><epage>3252</epage><pages>3241-3252</pages><issn>0192-8651</issn><eissn>1096-987X</eissn><coden>JCCHDD</coden><abstract>A quantitative structure‐property relationship (QSPR) study was performed for the prediction of the Setschenow constants (Ksalt) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R2 = 0.8680, while ANN and SVM higher values of R2 = 0.8898 and 0.9302, respectively. 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| subjects | Analytical chemistry artificial neural network Chemical compounds Methods Molecular chemistry Molecular structure multiple linear regression Neural networks Neural Networks (Computer) Organic Chemicals - chemistry QSPR Quantitative Structure-Activity Relationship Setschenow constant Support Vector Machine |
| title | QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses |
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