پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 41 |
| تعداد شمارهها | 1,105 |
| تعداد مقالات | 9,457 |
| تعداد مشاهده مقاله | 17,063,272 |
| تعداد دریافت فایل اصل مقاله | 11,959,556 |
Quantitative Study of 13C Chemical Shifts of β-Naphthalenes Using 2D Image Approach and Density Functional Theory Computation | ||
| Iranian Journal of Analytical Chemistry | ||
| مقاله 5، دوره 3، شماره 2، آذر 2016، صفحه 116-126 اصل مقاله (1.16 M) | ||
| نوع مقاله: Full research article | ||
| نویسندگان | ||
| Zahra Garkani-Nejad* 1؛ Marziyeh Poshteh-Shirani2 | ||
| 1Department of Chemistry, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2Department of Chemistry, Isfahan University of Technology, Isfahan, Iran | ||
| چکیده | ||
| A 2D image approach has been used to predict 13C NMR chemical shifts of β-naphthalene derivatives. In multivariate image analysis-Quantitative structure property relationship (MIA-QSPR) study, descriptors correlating with dependent variable are pixels (binaries) of 2D chemical structures; Variant pixels in the structures (substitutes) account to explained variance in the property (chemical shifts). A case study is carried out in order to predict 13C NMR chemical shifts of 10 carbon positions of 24 mono substituted β-naphthalenes. The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Then, MIA-QSPR modeling was done by means of principal component regression (PCR) and principal component –artificial neural network (PC-ANN) methods. A correlation ranking procedure is proposed here to select the most relevant set of PCs as inputs for PCR and PC-ANN modeling methods. Here, the 13C chemical shifts of studied compounds were predicted using density functional theory (DFT) calculations, too. The widely applied method of gauge included atomic orbital (GIAO) B3LYP/6-311++ G have been used. The performance of the GIAO was also compared with PCR and PC-ANN models. Results showed the superiority of the PC-ANN over GIAO and PCR models. Finally, 13C NMR chemical shifts of studied compounds were calculated using ChemDraw program. | ||
| کلیدواژهها | ||
| Multivariate Image Analysis؛ density functional theory؛ 13C Chemical Shift؛ β-Naphthalenes | ||
| عنوان مقاله [English] | ||
| مطالعه کمی جابجایی شیمیایی 13C بتا نفتالن ها با استفاده ازروش تصویر دو بعدی و محاسبه تئوری تابع چگالی | ||
| نویسندگان [English] | ||
| زهرا گرکانی نژاد1؛ مرضیه پشته شیرانی2 | ||
| 1بخش شیمی، دانشکده علوم، دانشگاه شهید باهنر کرمان، کرمان، ایران | ||
| 2بخش شیمی، دانشگاه صنعتی اصفهان، اصفهان، ایران | ||
| چکیده [English] | ||
| روش تصویر دو بعدی برای پیش بینی جابجایی شیمیایی 13C-NMR مشتقات بتا نفتالن استفاده شده است.درمطالعه آنالیز چند متغیره تصویر-ارتباط کمی ساختار ویژگی(MIA-QSPR) ̨ توصیف کننده ها نقاط دو بعدی ساختارهای شیمیایی می باشند. تغییر درساختار(استخلاف) موجب تغییر در ویژگی (جابجایی شیمیایی) می شود. در این مطالعه، جابجایی شیمیایی 13C-NMR 10 موقعیت کربن مربوط به 24 بتا نفتالن تک استخلافی پیش بینی شده است. بر روی توصیف کننده های حاصل آنالیز اجزاء اصلی صورت گرفته و مهمترین اجزاء اصلی استخراج شده اند. سپس مدلسازی MIA-QSPR با روش های رگرسیون اجزاء اصلی (PCR) و اجزاء اصلی- شبکه عصبی مصنوعی (PC-ANN) انجام شده است. برای انتخاب مناسب ترین اجزائ اصلی به عنوان ورودی برای دو روش رگرسیون اجزاء اصلی و اجزاء اصلی- شبکه عصبی مصنوعی ̨ ازروش ترتیب همبستگی استفاده شده است. همچنین جابجایی شیمیایی 13C ترکیبات مورد مطالعه با استفاده از تئوری تابع چگالی و روش (GIAO) B3LYP/6-311++ G پیش بینی شده است. عملکرد GIAO با دو روش PCR و PC-ANN مقایسه شده است. نتایج ارجحیت مدل PC-ANN را نسبت به دو روش GIAO و PCR نشان می دهد. نهایتا" جابجایی شیمیایی ترکیبات با استفاده از برنامه ChemDraw محاسبه شده است. | ||
| کلیدواژهها [English] | ||
| آنالیز چند متغیره تصویر, تئوری تابع چگالی, جابجایی شیمیایی 13C, بتا نفتالن | ||
| مراجع | ||
|
[1] S.Witkowski, D. Maciejewska and I. Wawer, 13C NMR studies of conformational dynamics in 2,2,5,7,8-pentamethylchroman-6-ol derivatives in solution and the solid state, J. Chem. Soc. Perkin Trans. 2 (2000) 1471– 1476.
[2] H. Neuvonen and K. Neuvonen, Correlation analysis of carbonyl carbon 13C NMR chemical shifts, IR absorption frequencies and rate coefficients of nucleophilic acyl substitutions. A novel explanation for the substituent dependence of reactivity, J. Chem. Soc. Perkin Trans. 2 (1999) 1497–1502.
[3] K. Wu¨thrich, The way of NMR structures of proteins, Nat. Struct. Biol. 8 (2001) 923–925.
[4] A.E. Aliev, D. Courtier-Murias and S. Zhou, Scaling factors for carbon NMR chemical shifts obtained from DFT B3LYP calculations, J. Mol. Struct. THEOCHEM 893 (2009) 1–5.
[5] E.L. Canto, L. Tasic, R.E. Bruns and R. Rittner, Principal component analysis in studies of substituent-induced chemical shifts of 1,4-disubstituted benzenes, Magn. Reson. Chem. 39 (2001) 316–322.
[6] M. Holík, Principal component regression with chemical-shift incerements .1. Pdisubstituted benzenes and 2- naphtyl derivatives, Chem. Commun. 61 (1996) 713– 725.
[7] M. Jalali-Heravi, S. Masoum and P. Shahbazikhah, Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks, J. Magn. Reson. 171 (2004) 176–185.
[8] R. Kiralj and M.M.C. Ferreira, Simple Quantitative Structure−Property Relationship (QSPR) Modeling of 17O Carbonyl Chemical Shifts in Substituted Benzaldehydes Compared to DFT and Empirical Approaches, J. Phys. Chem. A 112 (2008) 6134–6149.
[9] R.A.A. Santos, J.B. Ghasemi, C.A. Braz, R. Safavi-Sohi and E.G. Barbosa, Mixed 2D–3DLQTA-QSAR study of a series of Plasmodium falciparum dUTPase inhibitors, Med. Chem. Res. 24 (2015) 1098-1111.
[10] M. Goodarzi, T. Chen and M.P. Freitas, QSPR predictions of heat of fusion of organic compounds using Bayesian regularized artificial neural networks, Chemom. Intell. Lab. Sys. 104 (2010) 260-264.
[11] P.R. Duchowicz, M.V. Mirifico, M.F. Rozas, J.A. Caram, F.M. Fernandez and E.A. Castro, Quantitative Structure-Spectral Property relationships for functional groups of novel 1,2,5-thiadiazole compounds, Chemom. Intell. Lab. Sys. 105 (2011) 27-37.
[12] C. Duchesne, J.J. Liu and J.F. MacGregor, Multivariate image analysis in the process industries: A review, Chemom. Intell. Lab. Sys. 117 (2012) 116-128.
[13] J.M. Prats-Montalban, A. de Juan and A. Ferrer, Multivariate image analysis: a review with applications, Chemom. Intell. Lab. Sys. 107 (2011) 1-23.
[14] Z. Garakani-Nejad and M. Poshteh-Shirani, Application of multivariate image analysis in QSPR study of 13C chemical shifts of naphthalene derivatives: A comparative study, Talanta 83 (2010) 225-232.
[15] M. Goodarzi, M.P. Freitas and T.C. Ramalho, Prediction of 13C chemichal shifts in methoxyflavonol derivatives using MIAQSPR, Spectrochim. Acta Part A 74 (2009) 563–568.
[16] P. Geladi and K. Esbensen, Can image analysis provide information useful in. chemistry?, J. Chemometr. 3 (1989) 419-429.
[17] Z. Garakani-Nejad and M. Poshteh-Shirani, Modeling of 13C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors, Can. J. Chem. 89 (2011) 598-607.
[18] Z. Garakani-Nejad and M. Ahmadvand, Comparative QSRR modeling of nitrobenzene derivatives based on original molecular descriptors and multivariate image analysis descriptors, Chromatographia 73 (2011) 733742.
[19] E. Pretsch, P. Buhlmann and C. Affolter, Structure determination of organic com- pounds, Tables of spectral data, Ch. 4, p.p. 101.
[20] ACD/ChemSketch version 11.02, Advanced Chemistry Development, Inc., Toronto, Ont., Canada (2008).
[21] MATLAB Version 7.1 Mathworks Inc. (2005) www.Mathworks.com.
[22] D.C. Montgomery and E.A. Peck, Introduction to Linear Regression Analysis, Wiley, New York (1982).
[23] L.I. Smith, A tutorial on Principal Component Analysis (2002).
[24] C.B.Y. Cordella, R. Leardi and D.N. Rutledge, Three-way principal component analysis applied to noodles sensory data analysis, Chemom. Intell. Lab. Sys. 106 (2011) 125-130.
[25] I. Abdel-Qader, S. Pashaie-Rad and S. Yehia, PCA-Based algorithm for unsupervised bridge crack detection, Adv. Eng. Soft. 37 (2006) 771778.
[26] J. Sun, A correlation principal component regression analysis of NIR data, J. Chemometr. 9 (1995) 21-29.
[27] Minitab version 15.1.0.0, (2006) www.Minitab.com.
[28] N. Fjodorova, M. Vracko, A. Jezierska and M. Novic, Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals, SAR and QSAR in Environ. Res. 21 (2010) 5775.
[29] P.J. Gemperline, J.R. Long and V.G. Gregoriou, Nonlinear multivariate calibration using principal components regression and artificial neural networks, Anal. Chem. 63 (1991) 2313-2323.
[30] W. Koch and M.C. Holthausen, A Chemist’s Guide to Density Functional Theory, second ed., Wiley-VCH Verlag GmbH, Weinheim, (2001).
[31] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, J.A. Montgomery Jr., T. Vreven, K.N. Kudin, J.C. Burant, J.M.Millam, S.S. Iyengar, J. Tomasi, V. Barone, B. Mennucci, M. Cossi, G. Scalmani, N. Rega, G.A. Petersson, H. Nakatsuji, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M. Klene, X. Li, J.E. Knox, H.P. Hratchian, J.B. Cross, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, P.Y. Ayala, K. Morokuma, G.A. Voth, P. Salvador, J.J. Dannenberg, V.G. Zakrzewski, S. Dapprich, A.D. Daniels, M.C. Strain, O. Farkas, D.K. Malick, A.D. Rabuck, K. Raghavachari, J.B. Foresman, J.V. Ortiz, Q. Cui, A.G. Baboul, S. Clifford, J. Cioslowski, B.B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R.L. Martin, D.J. Fox, T. Keith, M.A. Al-Laham, C.Y. Peng, A. Nanayakkara, M. Challacombe, P.M.W. Gill, B. Johnson, W. Chen, M.W. Wong, C. Gonzalez, J.A. Pople, Gaussian03, revision B.03, Gaussian, Inc., Pittsburgh, PA (2003).
[32] R. Ditchfield, Molecular Orbital Theory of Magnetic Shielding and Magnetic Susceptibility, J. Chem. Phys. 56 (1972) 56885691.
[33] ChemDraw program, www.cambridgesoft .com. | ||
|
آمار تعداد مشاهده مقاله: 1,703 تعداد دریافت فایل اصل مقاله: 1,008 |
||