QSAR Study of a Serie of Benzimidazolylchalcone Derivatives by the Density Fonctional Theory (DFT) Method

Doumadé ZON^1, , Jean Stéphane N’DRI², Adéyolé TIMOTOU¹, Ahmont Landry Claude KABLAN¹, Mamadou Guy-Richard KONɲ, Mahama OUATTARA³ and Drissa SISSOUMA⁴

¹Département de Mathématiques-Physique-Chimie, UFR des Sciences Biologiques, Université Peleforo Gon Coulibaly de Korhogo, BP 1328 Korhogo, Côte d’Ivoire

²Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, UFR SFA, Université Nangui Abrogoua 02 BP 801 Abidjan 02, Côte-d’Ivoire

³Laboratoire de Chimie Thérapeutique et Biomolécules, UFR des Sciences Pharmaceutiques et Bilogiques, Université Félix Houphouët-Boigny 01 BP V 34 Abidjan, Côte-d’Ivoire

⁴Laboratoire de Constitution et Réaction de la matière, UFR SSMT, Université Félix Houphouët-Boigny 22 BP 582 Abidjan, Côte-d’Ivoire

Cite this paper:
Doumadé ZON, Jean Stéphane N’DRI, Adéyolé TIMOTOU, Ahmont Landry Claude KABLAN, Mamadou Guy-Richard KONÉ, Mahama OUATTARA and Drissa SISSOUMA. QSAR Study of a Serie of Benzimidazolylchalcone Derivatives by the Density Fonctional Theory (DFT) Method. Journal of Materials Physics and Chemistry. 2021; 9(1):9-15. doi: 10.12691/jmpc-9-1-2

Abstract

This QSAR study involved a series of benzimidazolylchalcone derivatives. It allowed us to obtain a model from the molecular descriptors and anthelminthical activity against Haemonchus contortus. The molecular descriptors were obtained by applying the methods of quantum chemistry at the B3LYP/6-31G (d) level. The statistical indicators of the model are: the coefficient of determination R² equals 0.990, the standard deviation S equals 0.209, the Fischer coefficient F equals 153.055 and the cross-validation coefficient equals 0.990. This model has good statistical performances. The quantum descriptors of dipole moment (μ_D) and electronic energy are responsible of the anthelminthic activity of benzimidazolylchalcone derivatives. In addition, the dipole moment is the priority descriptor for the prediction of the antibacterial activity of the studied compounds. The Eriksson et al. acceptance criteria used for the test set is verified. The values of the pCL_100theo/pCL_100exp ratio of the theoretical and experimental activities for the test set aim towards the unit.

Keywords:
benzimidazolylchalcone Quantum chemistry QSAR Quantum Descriptors Anthelminthical activity

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Aide mémoire N°139, Aout (2013).
[2]	Aide mémoire N°297, Février (2014).
[3]	D. S. Richard, C. Joanna, Bull. World Health Organ., 80, 126, (2002).
[4]	Z. Doumade, these de doctorat, (2014).
[5]	S. Chaltterjee, A. Hadi, B. Price, Regression Analysis by Examples; Wiley VCH: New York, USA, (2000).
[6]	Phuong HTN, “ Synthèse et étude des relations structure/activité quantitatives (QSAR/2D) d’analoguesBenzo [c] phénanthridiniques” Doctorat Thesis, Angers University, (France), (2007).
[7]	Gaussian 09, Revision A.02, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, A. D. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski, and D. J. Fox, Gaussian, Inc., Wallingford CT,
[8]	P. K. Chattaraj, A. Cedillo, and R. G. Parr, J. Phys. Chem., 103: 7645, (1991).
[9]	P. W. Ayers, and R. G. Parr, (2000), J. Am Chem., Soc, 122: (2010), 2000.
[10]	F. De Proft, J. M. L. Martin, and P. Geerlings, Chem. Phys Let., 250: 393, (1996).
[11]	P. Geerlings, F. De Proft, and J. M. L. Martin, In Theoretical and Computational Chemistry; Seminario, J., Ed.; Elsevier; Amsterdam. Vol-4 (Recent Developments in Density Functional Theory), p773, (1996).
[12]	F. De Proft, J. M. L. Martin, and P. Geerlings, Chem. Phys Let., 256: 400, (1996).
[13]	F. De Proft, and P. Geerlings, J Chem Phys, 106: 3270, (1997).
[14]	P. Geerlings, F. De Proft, and W. Langenaeker, Adv. Quantum Chem.33: 303, (1996).
[15]	R. G. Parr, R. A. Donnelly, M. Levy, and W. E. Palke, J. Chem. Phys., 68: 3801, (1978).
[16]	C. Hansch, P. G. Sammes, and J. B. Taylor, Computers and the medicinal chemist; in: Comprehensive Medicinal Chemistry, vol. 4, Eds. Pergamon Press, Oxford, pp. 33-58, (1990)
[17]	R. Franke, Theoretical Drug Design Methods, Elsevier, Amsterdam, (1984).
[18]	Microsoft ® Excel ® 2013 (15.0.4420.1017) MSO (15.0.4420.1017) 64 Bits (2013) Partie de Microsoft Office Professionnel Plus.
[19]	XLSTAT Version 2014.5.03 Copyright Addinsoft 1995-2014 (2014) XLSTAT and Addinsoftare Registered Trademarks of Addinsoft. https://www.xlstat.com
[20]	A. Vessereau, Méthodes statistiques en biologie et en agronomie. Lavoisier (Tec & Doc). Paris, (1988); 538.
[21]	G. W. Snedecor, W. G. Cochran, Statistical Methods; Oxford and IBH: New Delhi, India; p. 381, (1967).
[22]	M. V. Diudea, QSPR/QSAR Studies for Molecular Descriptors; Nova Science: Huntingdon, New York, USA, (2000).
[23]	E. X. Esposito, A. J. Hopfinger, J. D. Madura, Methods in Molecular Biology, 275, 131-213, (2004).
[24]	R. D. Cook, “Wiley Series in Probability and Statistics,” An introduction to Regression Graphics, (1994).
[25]	L. Eriksson, J. Jaworska, A. Worth, M.T. D. Cronin, R. M. Mc Dowell, P. Gramatica, Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs, Environmental Health Perspectives, 111(10):1361-1375, (2003).
[26]	T. Netzeva, status of methods for defining the applicability domain of (Quantitative) Structure–Activity Relationships. Altern. Lab. Anim., 33, 1-19, (2005).
[27]	F. Sahigara, Comparison of Different Approaches to Define the Applicability Domain of QSAR Models, Molecules, (2012).
[28]	J. Jaworska, QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review, ATLA 33, 445-459, (2005).
[29]	D. Gadaleta, “Applicability Domain for QSAR Models: Where Theory Meets Reality, International Journal of Quantitative Structure-Property Relationships”, vol.1, 01 January-June (2016).
[30]	M. Ghamali, “Méthodologie générale d’une étude RQSA/RQSP, Revue Interdisciplinaire.” p. vol.1, (2016).
[31]	S. Chtita, Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using QSAR Analysis, Vols. 1 sur 2vol, 1-16, Advances in Physical Chemistry, (2016).
[32]	T. Asadollahi, QSAR Models for CXCR2 Receptor Antagonists Based on the Genetic Algorithm for Data Preprocessing Prior to Application of the PLS Linear Regression Method and Design of the New Compounds Using In Silico Virtual Screening, Molecules, vol.16, (2011).
[33]	S. Chtita, “Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists of N-methyl-D-aspartate based on density functional theory with electronic and topological descriptors,” Journal of Taibah University for Science 9, 143-154, (2015).
[34]	J. Jaworska, “QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review, ATLA 33”, 445-459, Issue 1, January-June (2005).
[35]	D. Gadaleta, “Applicability Domain for QSAR Models: Where Theory Meets Reality”, International Journal of Quantitative Structure-Property Relationships, Issue 1, January-June (2016).
[36]	J. Jaworska, Review of Statistical Methods for QSAR AD Estimation by the Training Set, JRC Contract ECVA-CCR., Version of 28 January (2005).