Structural Relationship Study of Octanol-Water Partition Coefficient of the Compounds in kesum Essential Oil Using GA-MLR and GA-ANN Methods

الموضوعات : Journal of Physical & Theoretical Chemistry

Atefehsadat Navabi ¹ (Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran)
Tahereh Momeni Isfahani ² (Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran)

تاريخ الإرسال : 21 السبت , شعبان, 1442 تاريخ التأكيد : 17 الأربعاء , محرم, 1443 تاريخ الإصدار : 17 الإثنين , رجب, 1442

الکلمات المفتاحية: multiple linear regressions, QSPR, artificial neural network genetic algorithm, essential oils octanol- water partition coefficient,

ملخص المقالة :

Essential Oils are highly concentrated substances the subtle, aromatic and volatile liquids. The use of essential oils is largely widespread in foods, deodorants, pharmaceuticals, drinks, cosmetics, medicine and embalming antiseptics especially with aromatherapy becoming increasingly popular. The lipophilicity of an organic compound can be described by a partition coefficient, logP, which plays a significant role in drug discovery and compound design. A data set of 40 compounds in the essential oil of kesum was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GA-ANN) were employed to design the Quantitative Structure-Property Relationship (QSPR) models. The predictive powers of the QSPR model was discussed using Coefficient of determination (R2), Absolute Average Deviation (AAD) and the Mean Squared Error (MSE). The R2 and MSE values of the MLR model were calculated to be 0.734 and 0.194 respectively. The R2 and MSE values for the training set of the ANN model were calculated to be 0.9905 and 2×10-4 respectively. Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method

المصادر:

• Andrade-Ochoa, G. Virginia Nevárez-Moorillón, L. E. Sánchez-Torres, M. Villanueva-García, B. E. Sánchez-Ramírez, L. María Rodríguez-Valdez and B. E. Rivera-Chavira, BMC Compl Alternative Med. 15 (2015) 1.


• C. Gor, I. Ismail, W. A. W Mustapha, Z. Zainal, N. M. Noor and R. Othman, Acta. Physiol. Plant. 33(2011) 283.


• A. A. Almey, C. A. J. Khan, CAJ, I. S. Zahir, K. M. Suleiman, M. R. Aisyah and K. K. Rahim, Int. Food. Res. J. 17(2010) 1077.


• Bunawan, N. Talip and N. M. Noor, Aust. J. Crop. Sci. 5(2011) 123.


• M. Uyub, I. N. Nwachukwu, A. A. Azlan and S. S. Fariza, Res. Appl. 8(2010) 95.


• M. Mackeen, A. M. Ali, S. H. El-Sharkawy, M. Y. Manap, K. M. Salleh and N. H. Lajis, Pharm. Biol. 35(1997) 174.


• Huda-Faujan, A. Noriham, A. S. Norrakiah and A. S. Babji, J. Biotechnol. 8 (2009)484.


• M. Ali, M. M. Mackeen S. H. El-Sharkawy, J. Abdul Hamid, N. H. Ismail and F. Ahmad, Pertanika. J. Trop. Agric.Sci. 19(1996) 129.


• F. H´erent, V. D. Bie and B. Tilquin, J. Pharm. Biomed. Anal. 43(2007) 886.


• Ekins, J. Pharmacol. Toxicol. Methods. 53(2006) 38.


• A. Arnott, J. A. R. Kumar and S. L. Planey, J. Appl. Biopharm. Pharmacokinet. 1 (2013) 31.


• Korinth, T. Wellner, K. Schaller and H. Drexler, ‎Toxicol. Lett. 215 (2012) 49.


• A .Wildman and G. M. Crippen, J. Chem. Inform. Comput. Sci. 39(1999) 868.


• Kokate, X. Li and B. Jasti, AAPS. Pharm.Sci.Tech. 9(2008) 501.


• Bharate, V. Kumar and R. A. Vishwakarma, Comb. Chem. High Throughput Screen. 19(2016) 461.


• A Zhong, V. Mashinson, T. A. Woolman and M. Zha, Curr. Top. Med. Chem. 13 (2013) 1290.


• Ito, H. Ando, A. Ose, Y. Kitamura, T. Ando, H. Kusuhara and Y. Sugiyama, J. Pharm. Sci.102 (2013) 3294.


• E. Czerwinski, J. P. Skvorak, D. M. Maxwell, D. E. Lenz and S.I. Baskin, J. Biochem. Mol. Toxic. 20 (2006) 241.


• Andrade-Ochoa, G. Virginia Nevárez-Moorillón, L. E. Sánchez-Torres, M. Villanueva-García, B. E. Sánchez-Ramírez, L. María Rodríguez-Valdez and B. E. Rivera-Chavira, BMC Complement.Altern. Med. 15 (2015)1.


• Voda, B. Boh Podgornik and M. Vrtačnik, J. Mol. Model.10(2004)76.


• Subramaniam and G. Rao, J. Comput. Methods. Mol. Des. 3 (2011) 69.

[22]  H. Y. Xu, J. Y. Zhang, J. W. Zou and X. S. Chen, J. Mol. Graph. Model. 26 (2008) 1076.

[23]  F. Wania and D. Mackay, Environ. Pollut. 100 (1999) 223.

• N. Baharum, H. Bunawan, M. A. A Ghani, W. A. W. Mustapha and N. M. Noor, Molecules. 15 (2010) 7006.


• Todeschini, M. Lasagni and E. Marengo, J. Chemometrics, 8 (1994) 263.


• Moriwaki, Y. S. Tian, N. Kawashita and T. Takagi, J. Cheminform. 10 (2018) 1.


• Yap, J. Comput. Chem. 32 (2010)1466.


• D. Bolboaca, L. Jantschi and M. V. Diudea, Curr. Comput. Aided. Drug. Des. 9 (2013) 195.


• Talete srl, Dragon (ver. 5.4), Milano, Italy. Web site: ww.talete.mi.it/products/software.htm


• Todeschini and V. Consonni, Handbook of molecular descriptors; Wiley-VCH: Weinheim, 2000.


• Bahadori and M. Atabati, Comb. Chem. High Throughput Screen. 20 (2017) 321.


• Ahmadi and E. Habibpour, Med. Chem. 17 (2017) 552.


• Kawczak, M. Belka, J. Slawinski and T. Baczek, Curr. Pharm. Anal. 14 (2018) 35.


• Rouhollahi, J. B. Ghasemi, E. Babaee and A. Ouammou, Curr. Anal. Chem. 6 (2010) 3.


• Qudaih, T. Kerdphol and Y. Mitani, SGCE. 4 (2015) 247.


• Emamgholizadeh, M. Parsaeian and M. Baradaran, Europ. J. Agronomy. 68 (2015) 89.


• Zhou, S. Jhamb, X. Liang, K. Sundmacher and R. Gani, Chem. Eng. Sci.183(2018) 95.


• Thapliyal, R. Krishen Khar and A. Chandra, Curr. Nanosci. 14(2018) 239.


• G. D. Steel and J. H. Torrie, Principles and Procedures of Statistics with Special Reference to the Biological Sciences. McGraw Hill. 1960.


• L. Lehmann and G. Casella, Theory of Point Estimation (2nd ed.) New York, Springer. 1998.


• J. Hyndman and A. B. Koehler, Int. J. Forecast. 22 (2006) 679.


• Devinyak, D. Havrylyuk and R. Lesyk, J. Mol. Graph. Model. 54 (2014) 194.


• Dadfar, F. Shafiei and T. Momemi Isfahani, Curr Comput-Aid Drug. 1 (2019) 19.


• K Ghose, V. N. Viswanadhan and J. Wendoloski, J. Phys. Chem. 102(1998) 23772.


• Consonni, R. Todeschini, M. Pavan and P. Gramatica, J. Chem. Inform. Comput. Sci. 42 (2002) 693.


• Dialamehpour , F. Shafiei, J. Phys. Theor. Chem. 16(2020) 117


• Li, Y. Shi and I. Gutman I Graph energy. Springer, New York, 2012.


• V. Diudea, A. Pirvan–Moldovan, R. Pop and M. Medeleanu, MATCH. Commun. Math. Comput. Chem. 3 (2018)835.


• Ma, MATCH. Commun. Math. Comput. Chem. 81 (2019) 393.


• Estrada, Chem. Phys. Lett. 319 (2000) 713.