QSPR Models for Predicting Retention Indices of Polygonum minus Huds. Essential Oil Composition Using GA-BWMLR and GA-BPANN Methods

References

• Padron, J., Carrasco, R. and Pellon, R. (2002). Molecular descriptor based on a molar refractivity partition using Randic-type graph-theoretical invariant. J. Pharm. Pharmaceut. Sci. 5: 258-66.
   PubMed Web of Science ®Google Scholar
• Ahmadinejad, N., Shafiei, F. and Isfahani, T.M. (2018). Quantitative Structure-Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives Comb. Chem. High Throughput Screen. 21: 533-42.
   PubMed Web of Science ®Google Scholar
• Todeschini, R., Lasagni, M. and Marengo, E. (1994). New molecular descriptors for 2D and 3D structures. Theory. J. Chemometrics. 8: 263-72.
   Web of Science ®Google Scholar
• Wolfender, J-L., Marti, G., Thomas, A. and Bertrand, S. (2015). Current approaches and challenges for the metabolite profiling of complex natural extracts. J. Chromatogr. A. 1382: 136-64.
   PubMed Web of Science ®Google Scholar
• Ma, W., Luan, F., Zhang, H., Zhang, X., Liu, M., Hu, Z. and Fan, B. (2006). Quantitative structure-property relationships for pesticides in biopartitioning micellar chromatography J. Chromatogr. A. 1113: 140-7.
   PubMed Web of Science ®Google Scholar
• Burkill, I. (1966). A Dictionary of the Economic Products of the Malay Peninsula, Vols. I and II. Art Printing Works, Malaysia. 976.
   Google Scholar
• Zhao, C-X., Liang, Y-Z., Fang, H-Z. and Li, X-N. (2005). Temperature-programmed retention indices for gas chromatography–mass spectroscopy analysis of plant essential oils. J. Chromatogr. A. 1096: 76-85.
   PubMed Web of Science ®Google Scholar
• Hérent, M-F., De Bie, V. and Tilquin, B. (2007). Determination of new retention indices for quick identification of essential oils compounds. J. Pharm. Biomed. Anal. 43: 886-92.
   PubMed Web of Science ®Google Scholar
• Pourbasheer, E., Beheshti, A., Vahdani, S., Nekoei, M., Danandeh, M., Abbasghorbani, M. and Ganjali, M.R. (2015). Simple QSPR modeling for prediction of the GC retention indices of essential oil compounds. J. Essent. Oil-Bear. Plants. 18: 1298-309.
   Web of Science ®Google Scholar
• Evans, M. and Smith, J. (1961). Prediction of Retention Data in Gas-Liquid Chromatography from Molecular Formulc. Nature. 190: 905-6.
   Web of Science ®Google Scholar
• Evans, M. and Smith, J. (1962). Gas-liquid chromatography in qualitative analysis: Part IV. A simple method of calculating Rx9 values at elevated column temperatures. J. Chromatogr. A. 9: 147-53.
   Web of Science ®Google Scholar
• Robinson, P.G. and Odell, A.L. (1971). A system of standard retention indices and its uses the characterisation of stationary phases and the prediction of retention indices. J. Chromatogr. A. 57: 1-10.
   Web of Science ®Google Scholar
• Harangi, J. (2003). Retention index calculation without n-alkanes-the virtual carbon number. J. Chromatogr. A. 993: 187-95.
   PubMed Web of Science ®Google Scholar
• Belhassan, A., Chtita, S., Lakhlifi, T. and Bouachrine, M. (2018). QSRR study of linear retention indices for volatile compounds using statistical methods. Chem. Sci. Trans 7: 558-75.
   Google Scholar
• Belhassan, A., Chtita, S., Lakhlifi, T. and Bouachrine, M. (2019). 2D-and 3D-QSRR Studies of Linear Retention Indices for Volatile Alkylated Phenols. Sino-Nasal and Olfactory System Disorders: IntechOpen.
   Google Scholar
• Balaban, A.T. (1982). Highly discriminating distance-based topological index. Chem. Phys. Lett. 89: 399-404.
   Web of Science ®Google Scholar
• Heinzen, V.E. and Yunes, R.A. (1996). Using topological indices in the prediction of gas chromatographic retention indices of linear alkylbenzene isomers. J. Chromatogr. A. 719: 462-7.
   Web of Science ®Google Scholar
• Bruchmann, A., Zinn, P. and Haffer, C.M. (1993). Prediction of gas chromatographic retention index data by neural networks. Anal. Chim. Acta. 283: 869-80.
   Web of Science ®Google Scholar
• Tropsha, A. and Golbraikh, A. (2007). Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13: 3494-504.
   PubMed Web of Science ®Google Scholar
• Zisi, C., Sampsonidis, I., Fasoula, S., Papachristos, K., Witting, M., Gika, H.G., Nikitas, P. and Pappa-Louisi, A. (2017). QSRR modeling for metabolite standards analyzed by two different chromatographic columns using multiple linear regression. Metabolites. 7: 1-7.
   Web of Science ®Google Scholar
• Acimovic, M., Zoric, M., Zheljazkov, V.D., Pezo, L., Èabarkapa, I., Stankovic Jeremic, J. and Cvetkovic, M. (2020). Chemical characterization and antibacterial activity of essential oil of medicinal plants from Eastern Serbia. Molecules. 25: 5482-5507.
   Web of Science ®Google Scholar
• Voda, K., Boh, B., Vrtaènik, M. and Pohleven, F. (2003). Effect of the antifungal activity of oxygenated aromatic essential oil compounds on the white-rot Trametes versicolor and the brown- rot Coniophora puteana Int. Biodeterior. Biodegrad. 51: 51-9.
   Web of Science ®Google Scholar
• Kovats, V.E. (1958). Gas chromatographische charakterisierung organischer verbindungen. Teil 1: retentionsindices aliphatischer halogenide, alkohole, aldehyde und ketone. Helv. Chim. Acta. 41: 1915-32.
   Web of Science ®Google Scholar
• Tarjan, G., Nyiredy, S., Györ, M., Lombosi, E., Lombosi, T., Budahegyi, M., Meszaros, S. and Takács, J. (1989). Thirtieth anniversary of the retention index according to Kováts in gas-liquid chromatography. J. Chromatogr. A. 472: 1-92.
   Web of Science ®Google Scholar
• Rahimi, M., Karimi, E., Nekoei, M. and Mohammadhosseini, M. (2016). Hydro-distilled Volatile Oil Constituents from the Aerial Parts of Satureja mutica and QSRR Simulation by Multiple Linear Regression. J. Essent. Oil-Bear. Plants.19: 307-20.
   Web of Science ®Google Scholar
• Acimovic, M., Pezo, L., Teševic, V., Cabarkapa, I. and Todosijevic, M. (2020). QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition. Ind. Crops. Prod. 154: 112752.
   Web of Science ®Google Scholar
• Baharum, S.N., Bunawan, H., Ghani, M.a.A., Mustapha, W.A.W. and Noor, N.M. (2010). Analysis of the chemical composition of the essential oil of Polygonum minus Huds. using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC-TOF MS). Molecules. 15: 7006-15.
   PubMed Web of Science ®Google Scholar
• Obrezanova, O., Csányi, G., Gola, J.M. and Segall, M.D. (2007). Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model. 47: 1847-57.
   PubMed Web of Science ®Google Scholar
• Roothaan, C C J. (1951). New developments in molecular orbital theory. Rev. Mod. Phys. 23: 69-88.
   Web of Science ®Google Scholar
• Binkley, J.S., Pople, J.A. and Hehre, W.J. (1980). Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements. J. Am. Chem. Soc. 102: 939-47.
   Web of Science ®Google Scholar
• Moriwaki, H., Tian, Y-S., Kawashita, N. and Takagi, T. (2018). Mordred: a molecular descriptor calculator. J. Cheminform. 10: 1-14.
   PubMed Web of Science ®Google Scholar
• Bahadori, B. and Atabati, M. (2017). Harmony search as a powerful tool for feature selection in QSPR study of the drugs lipophilicity. Comb. Chem. High Throughput Screen 20: 321-7.
   PubMed Web of Science ®Google Scholar
• Mauri, A., Consonni, V., Pavan, M. and Todeschini, R. (2006). Dragon software: An easy approach to molecular descriptor calculations. Match. 56: 237-48.
   Google Scholar
• Pourbasheer, E., Aalizadeh, R., Ganjali, M.R., Norouzi, P. and Shadmanesh, J. (2014). QSAR study of ACK1 inhibitors by genetic algorithm-multiple linear regression (GA–MLR). J. Saudi. Chem. Soc. 18: 681-8.
   Web of Science ®Google Scholar
• Nekoei, M., Salimi, M., Dolatabadi, M. and Mohammadhosseini, M. (2011). Prediction of antileukemia activity of berbamine derivatives by genetic algorithm-multiple linear regression. Monatsh. Chem. 142: 943-9.
   Web of Science ®Google Scholar
• Chatterjee, S. and Hadi, A.S. (2015). Regression analysis by example. John Wiley & Sons; 2015.
   Google Scholar
• Marshall, E. and Boggis, E. (2016). The statistics tutor’s quick guide to commonly used statistical tests. Statstutor Community Project: 1-57.
   Google Scholar
• Dadfar, E., Shafiei, F. and Isfahani, T.M. (2020). Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods. Curr. Comput-Aid Drug Design. 16: 207-21.
   PubMed Web of Science ®Google Scholar
• Wu, J., Mei, J., Wen, S., Liao, S., Chen, J. and Shen, Y. (2010). A self adaptive genetic algorithm artificial neural network algorithm with leave one out cross validation for descriptor selection in QSAR study J. Comput.Chem. 31: 1956-68.
   PubMed Web of Science ®Google Scholar
• Zhong, G., Kang, M. and Yang, S. (2017). Precision obtained using an artificial neural network for predicting the material removal rate in ultrasonic machining. Appl. Sci. 7: 1268.
   Google Scholar
• Bishop, C.M. (1995). Neural networks for pattern recognition. Oxford university press; 1995.
   Google Scholar
• Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1985). Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science.
   Google Scholar
• Demuth, H., Beale, M. and Hagan, M. (1992). Neural network toolbox. For Use with MATLAB The MathWorks Inc. 2000.
   Google Scholar
• Dohoo, I.R., Ducrot, C., Fourichon, C., Donald, A. and Hurnik, D. (1997). An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies. Prev. Vet. Med. 29: 221-39.
   PubMed Web of Science ®Google Scholar
• Thapliyal, A., Khar, R.K. and Chandra, A. (2018). Artificial Neural Network Modelling of Green Synthesised Silver Nanoparticles in Bentonite/Starch Bio-nanocomposite. Curr. Nanosci. 14: 239-51.
   Web of Science ®Google Scholar
• Hessler, G. and Baringhaus, K-H. (2018). Artificial intelligence in drug design. Molecules. 23: 2520-36.
   Web of Science ®Google Scholar
• Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26: 694-701.
   Google Scholar
• Chatterjee, S. and Simonoff, J.S. (2013). Handbook of regression analysis. Wiley Online Library; 2013.
   Google Scholar
• Roy, P.P. and Roy, K. (2008). On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci. 27: 302-13.
   Google Scholar
• Gramatica, P. (2013). On the development and validation of QSAR models. Computational toxicology: Springer. p. 499-526.
   Google Scholar
• Karelson, M. (2000). Molecular descriptors in QSAR/QSPR. Wiley-Interscience; 2000.
   Google Scholar
• Helguera, A.M., Combes, R.D., González, M.P. and Cordeiro, M. (2008). Applications of 2D descriptors in drug design: a DRAGON tale. Curr. Top. Med. Chem. 8: 1628-55.
   PubMed Web of Science ®Google Scholar
• Hatekar, N.R. (2010). Principles of Econometrics: An Introduction (using R). SAGE Publications India; 2010.
   Google Scholar
• Pourbasheer, E., Riahi, S., Ganjali, M.R. and Norouzi, P. (2009). Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur. J. Med. Chem. 44: 5023-8.
   PubMed Web of Science ®Google Scholar
• Golmohammadi, H. and Dashtbozorgi, Z. (2010). Quantitative structure-property relationship studies of gas-to-wet butyl acetate partition coefficient of some organic compounds using genetic algorithm and artificial neural network. Struct. Chem. 21: 1241-52.
   Web of Science ®Google Scholar
• Oluwaseye, A., Uzairu, A., Shallangwa, G. and Abechi, S. (2018). Qsar studies on derivatives of Quinazoline-4 (3h)-Ones with anticonvulsant activities. J. Eng. Exact Sci. 4: 0255-64.
   Google Scholar
• Mahmud, A.W., Shallangwa, G.A. and Uzairu, A. (2020). QSAR and molecular docking studies of 1, 3-dioxoisoindoline-4-aminoquinolines as potent antiplasmodium hybrid compounds. Heliyon. 6: e03449.
   PubMed Web of Science ®Google Scholar