注册 English

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

Nasser Goudarzi ,  Mohammad Goodarzi ,  M. Arab Chamjangali ,  M. H. Fatemi

downloadPDF
Citation:  Nasser Goudarzi, Mohammad Goodarzi, M. Arab Chamjangali, M. H. Fatemi. Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds[J]. Chinese Chemical Letters, 2013, 24(10): 904-908. shu

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

    • 通讯作者: Nasser Goudarzi,
摘要: In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection (SPA) strategy, is used as the descriptor selection and model development method. Then, the support vector machine (SVM) and multiple linear regression (MLR) model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.

English

  • 

    1. [1] D.A. Kevin, Overview of capillary electrophoresis and capillary electrochromatography, J. Chromatogr. A 856 (1999) 443-463.[1] D.A. Kevin, Overview of capillary electrophoresis and capillary electrochromatography, J. Chromatogr. A 856 (1999) 443-463.

    2. [2] R.L. Kay, The current state of our understanding of ionic mobilities, Pure Appl. Chem. 63 (1991) 1393-1399.[2] R.L. Kay, The current state of our understanding of ionic mobilities, Pure Appl. Chem. 63 (1991) 1393-1399.

    3. [3] A. Yasri, D. Hartsough, Toward an optimal procedure for variable selection and QSAR model building, J. Chem. Inf. Comput. Sci. 41 (2001) 1218-1227.[3] A. Yasri, D. Hartsough, Toward an optimal procedure for variable selection and QSAR model building, J. Chem. Inf. Comput. Sci. 41 (2001) 1218-1227.

    4. [4] D. Barŕon Jimenez-Lozano, J. Barbosa, Prediction of electrophoretic behaviour of a series of quinolones in aqueous methanol, J. Chromatogr. A 919 (2001) 395-406.[4] D. Barŕon Jimenez-Lozano, J. Barbosa, Prediction of electrophoretic behaviour of a series of quinolones in aqueous methanol, J. Chromatogr. A 919 (2001) 395-406.

    5. [5] M. Jalali-Heravi, Z. Garkani-Nejad, Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks, J. Chromatogr. A 927 (2001) 211-218.[5] M. Jalali-Heravi, Z. Garkani-Nejad, Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks, J. Chromatogr. A 927 (2001) 211-218.

    6. [6] Q.F. Li, L.J. Dong, R.P. Jia, et al., Development of a quantitative structure-property relationship model for predicting the electrophoretic mobilities, Comput. Chem. 26 (2002) 245-251.[6] Q.F. Li, L.J. Dong, R.P. Jia, et al., Development of a quantitative structure-property relationship model for predicting the electrophoretic mobilities, Comput. Chem. 26 (2002) 245-251.

    7. [7] A. Jouyban, B.H. Yousefi, Quantitative structure property relationship study of electrophoretic mobility of analytes in capillary zone electrophoresis, Comput. Biol. Chem. 27 (2003) 297-303.[7] A. Jouyban, B.H. Yousefi, Quantitative structure property relationship study of electrophoretic mobility of analytes in capillary zone electrophoresis, Comput. Biol. Chem. 27 (2003) 297-303.

    8. [8] N. Goudarzi, M. Goodarzi, Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LSSVM), Mol. Phys. 106 (2008) 2525-2535.[8] N. Goudarzi, M. Goodarzi, Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LSSVM), Mol. Phys. 106 (2008) 2525-2535.

    9. [9] N. Goudarzi, M. Goodarzi, Prediction of the acidic dissociation constant (pKa) of some organic compounds using linear and nonlinear QSPR methods, Mol. Phys. 107 (2009) 1495-1503.[9] N. Goudarzi, M. Goodarzi, Prediction of the acidic dissociation constant (pKa) of some organic compounds using linear and nonlinear QSPR methods, Mol. Phys. 107 (2009) 1495-1503.

    10. [10] M.H. Fatemi, N. Goudarzi, Quantitative structure property relationship study of the electrophoretic mobilities of some benzoic acids derivatives in different carrier electrolyte compositions, Electrophoresis 26 (2005) 2968-2973.[10] M.H. Fatemi, N. Goudarzi, Quantitative structure property relationship study of the electrophoretic mobilities of some benzoic acids derivatives in different carrier electrolyte compositions, Electrophoresis 26 (2005) 2968-2973.

    11. [11] N. Goudarzi, M. Goodarzi, QSPR models for prediction of half wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods, Mol. Phys. 107 (2009) 1739-1744.[11] N. Goudarzi, M. Goodarzi, QSPR models for prediction of half wave potentials of some chlorinated organic compounds using SR-PLS and GA-PLS methods, Mol. Phys. 107 (2009) 1739-1744.

    12. [12] N. Goudarzi, M. Goodarzi, Prediction of the vapor pressure of some halogenated methyl-phenyl ether (anisole) compounds using linear and nonlinear QSPR methods, Mol. Phys. 107 (2009) 1615-1620.[12] N. Goudarzi, M. Goodarzi, Prediction of the vapor pressure of some halogenated methyl-phenyl ether (anisole) compounds using linear and nonlinear QSPR methods, Mol. Phys. 107 (2009) 1615-1620.

    13. [13] Z. Elmi, K. Faez, M. Goodarzi, N. Goudarzi, Feature selection method based on fuzzy entropy for regression in QSAR studies, Mol. Phys. 107 (2009) 1787-1798.[13] Z. Elmi, K. Faez, M. Goodarzi, N. Goudarzi, Feature selection method based on fuzzy entropy for regression in QSAR studies, Mol. Phys. 107 (2009) 1787-1798.

    14. [14] N. Goudarzi, M. Goodarzi, M.C.U. Araujo, R.K.H. Galvao, QSPR modeling of soil sorption coefficients (KOC) of pesticides using SPA-ANN and SPA-MLR, J. Agric. Food Chem. 57 (2009) 7153-7158.[14] N. Goudarzi, M. Goodarzi, M.C.U. Araujo, R.K.H. Galvao, QSPR modeling of soil sorption coefficients (KOC) of pesticides using SPA-ANN and SPA-MLR, J. Agric. Food Chem. 57 (2009) 7153-7158.

    15. [15] N. Goudarzi, M.H. Fatemi, A. Samadi, Quantitative structure-properties relationship study of the 29Si-NMR chemical shifts of some silicate species, Spectrosc. Lett. 42 (2009) 186-193.[15] N. Goudarzi, M.H. Fatemi, A. Samadi, Quantitative structure-properties relationship study of the 29Si-NMR chemical shifts of some silicate species, Spectrosc. Lett. 42 (2009) 186-193.

    16. [16] M. Goodarzi, M.P. Freitas, Feature selection and linear/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3B inhibitory activities, QSAR Comb. Sci. 27 (2008) 1092-1098.[16] M. Goodarzi, M.P. Freitas, Feature selection and linear/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3B inhibitory activities, QSAR Comb. Sci. 27 (2008) 1092-1098.

    17. [17] M. Goodarzi, M.P. Freitas, Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure-property relationships, J. Phys. Chem. A 112 (2008) 11263-11265.[17] M. Goodarzi, M.P. Freitas, Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure-property relationships, J. Phys. Chem. A 112 (2008) 11263-11265.

    18. [18] M. Goodarzi, M.P. Freitas, On the use of PLS and N-PLS inMIA-QSAR: azole antifungals, Chemom. Intell. Lab. Syst. 96 (2008) 59-62.[18] M. Goodarzi, M.P. Freitas, On the use of PLS and N-PLS inMIA-QSAR: azole antifungals, Chemom. Intell. Lab. Syst. 96 (2008) 59-62.

    19. [19] H. Golmohammadi, M.H. Fatemi, Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellarelectrokinetic chromatography, Electrophoresis 26 (2005) 3438-3444.[19] H. Golmohammadi, M.H. Fatemi, Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellarelectrokinetic chromatography, Electrophoresis 26 (2005) 3438-3444.

    20. [20] M. Goodarzi, M.P. Freitas, R. Jensen, Feature selection and linear/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3beta inhibitory activities, J. Chem. Inf. Model 49 (2009) 824-832.[20] M. Goodarzi, M.P. Freitas, R. Jensen, Feature selection and linear/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3beta inhibitory activities, J. Chem. Inf. Model 49 (2009) 824-832.

    21. [21] M. Goodarzi, P.R. Duchowicz, C.H. Wu, F.M. Fernández, E.A. Castro, J. Chem. Inf. Model 49 (2009) 1475-1485.[21] M. Goodarzi, P.R. Duchowicz, C.H. Wu, F.M. Fernández, E.A. Castro, J. Chem. Inf. Model 49 (2009) 1475-1485.

    22. [22] N. Goudarzi, M. Goodarzi, M. Arab Chamjangali, Prediction of inhibition effect of some aliphatic and aromatic organic compounds using QSAR method, J. Environ. Chem. Ecotoxicol. 2 (2010) 47-51.[22] N. Goudarzi, M. Goodarzi, M. Arab Chamjangali, Prediction of inhibition effect of some aliphatic and aromatic organic compounds using QSAR method, J. Environ. Chem. Ecotoxicol. 2 (2010) 47-51.

    23. [23] N. Goudarzi, P. Kalhor, Quantitative structure-retention index relationship (QSRIR) study of monomethylalkanes on the methylsilicone OV-1 stationary phase, Anal. Chem. Lett. 2 (2012) 13-26.[23] N. Goudarzi, P. Kalhor, Quantitative structure-retention index relationship (QSRIR) study of monomethylalkanes on the methylsilicone OV-1 stationary phase, Anal. Chem. Lett. 2 (2012) 13-26.

    24. [24] N. Goudarzi, M. Goodarzi, Application of successive projections algorithm(SPA) as a variable selection in a QSPR study to predict the octanol/water partition coefficients (Kow) of some halogenated organic compounds, Anal. Methods 2 (2010) 758-764.[24] N. Goudarzi, M. Goodarzi, Application of successive projections algorithm(SPA) as a variable selection in a QSPR study to predict the octanol/water partition coefficients (Kow) of some halogenated organic compounds, Anal. Methods 2 (2010) 758-764.

    25. [25] N. Goudarzi, M. Goodarzi, T. Chen, QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions, Med. Chem. Res. 21 (2012) 437-443.[25] N. Goudarzi, M. Goodarzi, T. Chen, QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions, Med. Chem. Res. 21 (2012) 437-443.

    26. [26] J. Hosseini, M. Nekoei, M. Mohammadhosseini, N. Goudarzi, Quantitative structure-activity relationship study of arylsulfonylpiperazine inhibitors of 11b-HSD1 by genetic algorithm-multiple linear regression, J. Appl. Res. Chem. 5 (2011) 5-17.[26] J. Hosseini, M. Nekoei, M. Mohammadhosseini, N. Goudarzi, Quantitative structure-activity relationship study of arylsulfonylpiperazine inhibitors of 11b-HSD1 by genetic algorithm-multiple linear regression, J. Appl. Res. Chem. 5 (2011) 5-17.

    27. [27] M.C.U. Araujo, T.C.B. Saldanha, R.K.H. Galvão, et al., The successive projections algorithm for variable selection in spectroscopicmulticomponent analysis, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 57 (2001) 65-73.[27] M.C.U. Araujo, T.C.B. Saldanha, R.K.H. Galvão, et al., The successive projections algorithm for variable selection in spectroscopicmulticomponent analysis, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 57 (2001) 65-73.

    28. [28] A.D.F. Heronides, E.S.O.N. Souza, V. Visani, et al., Simultaneous spectrometric determination of Cu2+, Mn2+ and Zn2+ in polivitaminic/polimineral drug using SPA and GA algorithms for variable selection, J. Braz. Chem. Soc. 16 (2005) 58-61.[28] A.D.F. Heronides, E.S.O.N. Souza, V. Visani, et al., Simultaneous spectrometric determination of Cu2+, Mn2+ and Zn2+ in polivitaminic/polimineral drug using SPA and GA algorithms for variable selection, J. Braz. Chem. Soc. 16 (2005) 58-61.

    29. [29] M.S. Di Nezio, M.F. Pistonesi, W.D. Fragoso, et al., Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water, Microchem. J. 85 (2007) 194-200.[29] M.S. Di Nezio, M.F. Pistonesi, W.D. Fragoso, et al., Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water, Microchem. J. 85 (2007) 194-200.

    30. [30] M. Grunhut, M.E. Centurión, W.D. Fragoso, et al., Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm, Talanta 75 (2008) 950-958.[30] M. Grunhut, M.E. Centurión, W.D. Fragoso, et al., Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm, Talanta 75 (2008) 950-958.

    31. [31] R.K.H. Galvão, M.F. Pimentel, M.C.U. Araujo, T. Yoneyama, V. Visani, Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry, Anal. Chim. Acta 443 (2001) 107-115.[31] R.K.H. Galvão, M.F. Pimentel, M.C.U. Araujo, T. Yoneyama, V. Visani, Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry, Anal. Chim. Acta 443 (2001) 107-115.

    32. [32] F.A. Honorato, R.K.H. Galvão, M.F. Pimentel, et al., Robust modeling for multivariate calibration transfer by the successive projections algorithm, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 76 (2005) 65-72.[32] F.A. Honorato, R.K.H. Galvão, M.F. Pimentel, et al., Robust modeling for multivariate calibration transfer by the successive projections algorithm, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 76 (2005) 65-72.

    33. [33] M.C. Breitkreitz, I.M. Raimundo Jr., J.J.R. Rohwedder, et al., Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration, Analyst 128 (2003) 1204-1208.[33] M.C. Breitkreitz, I.M. Raimundo Jr., J.J.R. Rohwedder, et al., Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration, Analyst 128 (2003) 1204-1208.

    34. [34] H.A.D. Dantas Filho, R.K.H. Galvão, M.C.U. Araújo, et al., A strategy for selecting calibration samples for multivariate modelling, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 72 (2004) 83-91.[34] H.A.D. Dantas Filho, R.K.H. Galvão, M.C.U. Araújo, et al., A strategy for selecting calibration samples for multivariate modelling, Chemom. Intell. Lab. Syst., Lab. Inf. Manage. 72 (2004) 83-91.

    35. [35] M.J.C. Pontes, R.K.H. Galvão, M.C.U. Araújo, et al., The successive projections algorithm for spectral variable selection in classification problems, Chemom. Intell. Lab. Syst. 78 (2005) 11-18.[35] M.J.C. Pontes, R.K.H. Galvão, M.C.U. Araújo, et al., The successive projections algorithm for spectral variable selection in classification problems, Chemom. Intell. Lab. Syst. 78 (2005) 11-18.

    36. [36] F.F. Gambarra-Neto, G. Marino, M.C.U. Araújo, et al., Classification of edible vegetable oils using square wave voltammetry with multivariate data, Talanta 77 (2009) 1660-1666.[36] F.F. Gambarra-Neto, G. Marino, M.C.U. Araújo, et al., Classification of edible vegetable oils using square wave voltammetry with multivariate data, Talanta 77 (2009) 1660-1666.

    37. [37] R.K.H. Galvão, M.C.U. Araújo, W.D. Fragoso, et al., A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm, Chemom. Intell. Lab. Syst. 92 (2008) 83-91.[37] R.K.H. Galvão, M.C.U. Araújo, W.D. Fragoso, et al., A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm, Chemom. Intell. Lab. Syst. 92 (2008) 83-91.

    38. [38] R.K.H. Galvão, M.C.U. Araújo, E.C. Silva, et al., Cross-validation for the selection of spectral variables using the successive projections algorithm, J. Braz. Chem. Soc. 18 (2007) 1580-1584.[38] R.K.H. Galvão, M.C.U. Araújo, E.C. Silva, et al., Cross-validation for the selection of spectral variables using the successive projections algorithm, J. Braz. Chem. Soc. 18 (2007) 1580-1584.

    39. [39] R.K.H. Galvão, M.C.U. Araújo, Linear regression modeling: variable selection, in: S. Brown, R. Tauler, B. Walczak (Eds.), Comprehensive chemometrics, Elsevier, 2009.[39] R.K.H. Galvão, M.C.U. Araújo, Linear regression modeling: variable selection, in: S. Brown, R. Tauler, B. Walczak (Eds.), Comprehensive chemometrics, Elsevier, 2009.

    40. [40] S.D. Noblitt, R.L. Mazzoleni, S.V. Hering, et al., Separation of common organic and inorganic anions in atmospheric aerosols using a piperazine buffer and capillary electrophoresis, J. Chromatogr. A 1154 (2007) 400-406.[40] S.D. Noblitt, R.L. Mazzoleni, S.V. Hering, et al., Separation of common organic and inorganic anions in atmospheric aerosols using a piperazine buffer and capillary electrophoresis, J. Chromatogr. A 1154 (2007) 400-406.

    41. [41] HyperChem Release 7, HyperCube, Inc., http://www.hyper.com.[41] HyperChem Release 7, HyperCube, Inc., http://www.hyper.com.

    42. [42] R. Todeschini, Milano Chemometrics and QSPR Group, http://www.disat.unimib.it/vhml.[42] R. Todeschini, Milano Chemometrics and QSPR Group, http://www.disat.unimib.it/vhml.

  • 加载中
WeChat 扫一扫看文章
计量
  • PDF下载量:  0
  • 文章访问数:  0
  • HTML全文浏览量:  0
文章相关
  • 收稿日期:  2013-03-11
  • 网络出版日期:  2013-05-28
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章

All Rights Reserved by the Chinese Chemical Society   中国化学会 版权所有 京ICP备05002797号

本系统由北京仁和汇智信息技术有限公司开发    技术支持: info@rhhz.net