IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH

SAIDU TUKUR, GIDEON ADAMU SHALLANGWA, ADAMU UZAIRU, ABDULKADIR IBRAHIM

Resumo


The study of the quantitative structure-activity relationship (QSAR) was used in a set of data from 43 heterocyclic and phenylic inhibitor compounds in order to establish a correlation between the inhibitory concentrations of the compounds in question and their structures. The optimization method of the density  functional theory (DFT) was used to minimize the energy of the 3D structures using the Becke functional  hybrid Exchange (B3)  parameter with the Lee, Yang, and Parr Functional Correlation (LYP), commonly called the B3LYP functional Hybrid and 6-31G* Basis Set (B3LYP/6-31G*) method, to discover their molecular Quantum descriptors. Five models of QSAR were generated with the technique of genetic function algorithm (GFA). Among the five models generated, model 1 was selected as the best model because of its statistical significance (Friedman's LOF = 0.3008, R2 = 0.9784, R2adj = 0.9739, Qcv2 = 0.9675 and R2pred = 0.7348). The meticulous model was evaluated by means of the Leave One out cross-validation (LOO-CV) approach, external validation of the compounds of the test set, Y -randomization test and applicability domain (Williams Plot). The proposed QSAR model was highly predictive and vigorous with good validation parameters. The molecular descriptors used in the model should be considered of great importance in improving the inhibitory concentrations of the herbicides and also in the conception of new herbicides with a higher concentration of inhibitor.


Palavras-chave


Herbicide; QSAR; Multiple Linear Regression (MLR); Genetic Function Algoeithm (GFA); Applicability Domain; Y- Randomization.

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Referências


ABDULFATAI, U.; UZAIRU, A.; UBA, S. Investigation of some acetamido-N-benzylacetamide derivatives as potent anti-convulsant inhibitors. Journal of Computational Methods in Molecular Design, v.5, n. 4, p. 77-83, 2015.

ABDULFATAI, U.; UZAIRU, A.; UBA, S. Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogs as inhibitors of gamma-aminobutyric acid aminotransferase. Journal of Advanced Research, v.8, n. 1, p. 33-43, 2017.

ADENIJI, S. E.; UBA, S.; UZAIRU, A. A Novel QSAR Model for the Evaluation and Prediction of (E)-N’-Benzylideneisonicotinohydrazide Derivatives as the Potent Anti-mycobacterium Tuberculosis Antibodies Using Genetic Function Approach. Physical Chemistry Research, v.6, n. 3, p. 479-492, 2018.

ALHO, J. S.; VÄLIMÄKI, K.; MERILÄ, J. Rhh: an R extension for estimating multilocus heterozygosity and heterozygosity–heterozygosity correlation. Molecular Ecology Resources, v.10, n. 4, p. 720-722, 2010.

ARTHUR, D. E.; UZAIRU, A.; MAMZA, P.; ABECHI, E.; SHALLANGWA, G. In Silico Modelling of Cytotoxic Behaviour of Anti-Leukemic Compounds on HL-60 Cell Line. Journal of the Turkish Chemical Society, Section A: Chemistry, v.3, n. 2, p. 147-158, 2016.

ARTHUR, D. E.; UZAIRU, A.; MAMZA, P.; ABECHI, S. Quantitative structure-activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines. Journal of Advanced Research, v.7, n. 5, p. 823-837, 2016.

ARTHUR, D. E.; UZAIRU, A.; MAMZA, P.; ABECHI, E.; SHALLANGWA, G. Insilco study on the toxicity of anti-cancer compounds tested against MOLT-4 and p388 cell lines using GA-MLR technique. Beni-Suef University Journal of Basic and Applied Sciences, v.5, n. 4, p. 320-333, 2016.

BECKE, A. D. Becke's three-parameter hybrid method using the LYP correlation functional. Journal of Chemical Physics, v. 98, p. 5648-5652, 1993.

CHO, D. H.; LEE, S. K.; KIM, B. T.; NO, K. T. Quantitative structure-activity relationship (QSAR) study of new fluorovinyloxyacetamides. Bulletin of the Korean Chemical Society, v.22, n. 4, p. 388-394, 2001.

CRUZ, V. L.; MARTINEZ, S.; RAMOS, J.; MARTINEZ-SALAZAR, J. 3D-QSAR as a tool for understanding and improving single-site polymerization catalysts. a review. Organometallics, v.33, n. 12, p. 2944-2959, 2014.

DAYAN, F. E.; ZACCARO, M.; LETICIA DE, M. Chlorophyll fluorescence as a marker for herbicide mechanisms of action. Pesticide Biochemistry and Physiology, v.102, n. 3, p. 189-197, 2012.

FUNAR-TIMOFEI, S.; BOROTA, A.; CRISAN, L. Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants. Molecular diversity, v.21, n. 2, p. 437-454, 2017.

GANDY, M. N.; CORRAL, M. G.; MYLNE, J. S.; STUBBS, K. A. An interactive database to explore herbicide physicochemical properties. Organic & biomolecular chemistry, v,13, n. 20, p. 5586-5590, 2015.

HANSCH, C.; MUIR, R. M.; FUJITA, T.; MALONEY, P. P.; GEIGER, F.; STREICH, M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. Journal of the American Chemical Society, v.85, n. 18, p. 2817-2824, 1963.

IBRAHIM, M. T.; UZAIRU, A.; SHALLANGWA, G. A.; IBRAHIM, A. In-silico studies of some oxadiazoles derivatives as anti-diabetic compounds. Journal of King Saud University-Science, p. 2018.

JALALI-HERAVI, M.; KYANI, A. "Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. Journal of chemical information and computer sciences, v.44 n. 4, p. 1328-1335, 2004.

KENNARD, R. W.; STONE, LARRY, A. Computer-aided design of experiments. Technometrics, v.11, n. 1, p. 137-148, 1969.

LEE, C.; Yang, W.; PARR, R.G. Becke's three-parameter hybrid method using the LYP. Phys. Rev. B, v.37, p. 785, 1988.

LIU, Y.; Zhao, H.; WANG, Z.; Li, Y.; Song, H.; RICHES, H.; BEATTIE, D.; Gu, Y.; WANG, Q. The discovery of 3-(1-aminoethylidene) quinoline-2, 4 (1H, 3H)-dione derivatives as novel PSII electron transport inhibitors. Molecular diversity, v.17, n. 4, p. 701-710, 2013.

MINOVSKI, N.; ŽUPERL, Š.; DRGAN, V.; NOVIČ, M. Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study. Analytica chimica acta, v.759, p. 28-42, 2013.

OLASUPO, S. B.; UZAIRU, A.; SAGA GIS, B. S. Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans., 2017.

PANCHAL, J. H.; KALIDINDI, S. R.; MCDOWELL, D. L. Key computational modeling issues in integrated computational materials engineering. Computer-Aided Design, v.45, n. 1, 4-25, 2013.

PFISTER, K.; ARNTZEN, C. J. The mode of action of photosystem II-specific inhibitors in herbicide-resistant weed biotypes. Zeitschrift für Naturforschung C, v.34, n. 11, p. 996-1009, 1979.

PRASAD, R. K.; SHARMA, R. 2D QSAR Analysis of pyrazine carboxamide derivatives as an herbicidal agent. Journal of Computational Method & Molecular Design, v.1, p. 7-13, 2011.

RASULEV, B. F.; ABDULLAEV, N. D.; SYROV, V. N.; LESZCZYNSKI, J. A Quantitative Structure‐Activity Relationship (QSAR) Study of the Antioxidant Activity of Flavonoids. QSAR & Combinatorial Science, v.24, n. 9, p. 1056-1065, 2005.

ROY, K.; KAR, S.; Das, R. N. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, 2015.

SAIDI, A.; MIRZAEI, M. Prediction of AHAS inhibition by sulfonylurea herbicides using a genetic algorithm and artificial neural network. 2016.

SHEN, M.; LET IRAN, A.; XIAO, Y.; GALBRAITH, A.; KOHN, H.; TROPSHA, A. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. Journal of medicinal chemistry, v.45, n. 13, p. 2811-2823, 2002.

TAKAČ, M. J.; MEDIĆ-ŠARIČ, M. QSPR, and QSAR in Pharmacy. I. Classic QSAR models. Hansch and Fred Wilson's model. Farmaceutski glasnik: glacial Hrvatskog farmaceutskog društva, v.47, n. 6, p. 161-178, 1991.

TROPSHA, A.; GRAMATICA, P.; GOMBAR, V. K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science, v.22, n. 1, p. 69-77, 2003.

TROYER, J. R. In the beginning: the multiple discoveries of the first hormone herbicides. Weed Science, v.49, n. 2, p. 290-297, 2001.

VEERASAMY, R.; RAJAK, H.; JAIN, A.; SIVADASAN, S.; VARGHESE, C. P.; AGRAWAL, R. K. Validation of QSAR models-strategies and importance. International Journal of Drug Design & Discovery, v.3, p. 511-519, 2011.

VERMA, J.; KHEDKAR, V. M.; COUTINHO, E. C. "3D-QSAR in drug design-a review. Current topics in medicinal chemistry, v.10, n. 1, p. 95-115, 2010.

ZHANG, C.; CHANG, S.; TIAN, X.; Tian, Y. 3D-QSAR and docking modeling study of 1, 3, 5-triazine derivatives as PSII electron transport inhibitor. Asian Journal of Chemistry, v.26, n. 1, p. 264, 2014.

ZIMMERMAN, P. W.; HITCHCOCK, A. E. Plant hormones. Annual review of biochemistry, v.17, n. 1, p. 601-626, 1948.




DOI: https://doi.org/10.18540/jcecvl5iss1pp0049-0062

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