QSAR Modelling of Thymidylate Synthase Inhibitors in a Series of Quinazoline Derivatives

Khairullina, V. R.; Gerchikov, A. Ya.; Lagunin, Alexey; Zarudii, F. S.

doi:10.1007/s11094-018-1710-z

Cited by 7 publications

(23 citation statements)

References 17 publications

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“…The structures of the phenol, aminophenol and uracil derivatives selected for the QSAR modeling in GUSAR 2013 [ 35 , 36 , 37 , 42 , 43 , 44 , 45 , 46 , 47 ] are shown in Figure 1 . A complete list of compounds with their experimental k 7 values is presented in Supplementary Materials (Table S2) .…”

Section: Computational Detailsmentioning

confidence: 99%

“…The QSAR models M1–M9 for quantitative prediction of the antioxidant activity of uracil, phenol, and aminophenol derivatives were based on two types of substructural descriptors of atomic neighborhoods: QNA (Quantitative Neighborhood of Atoms) and MNA (Multilevel Neighborhoods of Atoms) [ 35 , 36 , 37 , 42 , 43 , 44 , 45 , 46 , 47 ]. These descriptors are automatically computed in the GUSAR2013 program based on the structural formulas of chemical compounds, taking into account the valence and partial atomic charges.…”

Section: Computational Detailsmentioning

confidence: 99%

“…Let’s briefly explain the ideology of calculating descriptors in the program GUSAR2013. The ideology of calculating QNA and MNA descriptors is described in detail Supplementary Materials and papers [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. Additionally, three descriptors of the whole molecule (topological length, topological volume, and lipophilicity) were used to enhance the descriptive and predictive ability of the models.…”

Section: Computational Detailsmentioning

confidence: 99%

“…To modelling the large-sized training sets of the diverse compounds, one has to use the methods for calculating descriptors that allow rationalizing the QSAR model development process. GUSAR 2013 (General Unrestricted Structure Activity Relationships) is one of the programs for calculating physicochemical and structural descriptors, selection of the most significant of them and developing a QSAR consensus model based on them [ 35 , 36 , 37 , 38 ]. This program for the formation of independent descriptors and their selection at the stage of the formation of regression models uses unique approaches, which we describe in detail in Supplementary Materials .…”

Section: Introductionmentioning

confidence: 99%

“…This program for the formation of independent descriptors and their selection at the stage of the formation of regression models uses unique approaches, which we describe in detail in Supplementary Materials . The program has demonstrated its efficiency for modeling various types of biological activity [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. It is important to note that GUSAR 2013 allows prompt processing (in a matter of seconds) various structural and physicochemical descriptors, automatic selecting the most significant of them, and develop statistically significant QSAR consensus models.…”

Section: Introductionmentioning

confidence: 99%

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QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

et al. 2021

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Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01–6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk7 values for the compounds of training sets and appropriately predict lgk7 for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R2 (R2TSi, R20, Q2(F1), Q2(F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk7 errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk7 for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons.

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Section: Computational Detailsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

et al. 2021

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Determination of the chain termination rate constants of the radical chain oxidation of organic compounds on antioxidant molecules by the QSPR method

et al. 2020

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A quantitative analysis of the structure-antioxidant activity relationship was performed for 128 derivatives of phenols, amines, uracil, benzopyrane, and benzofuran using the GUSAR 2013 program. Nine statistically signifi cant QSAR consensus models characterized by a high accuracy of prediction of the chain termination rate constant of oxidation on the antioxidant molecules were constructed. The results of the structural analysis performed in the GUSAR 2013 program are in good agreement with the literature data.

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Quantitative structure–activity relationship of the thymidylate synthase inhibitors of Mus musculus in the series of quinazolin-4-one and quinazolin-4-imine derivatives

Khairullina

Gimadieva

Gerchikov

et al. 2018

Journal of Molecular Graphics and Modelling

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QSAR Modelling of Thymidylate Synthase Inhibitors in a Series of Quinazoline Derivatives

Cited by 7 publications

References 17 publications

QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

Determination of the chain termination rate constants of the radical chain oxidation of organic compounds on antioxidant molecules by the QSPR method

Quantitative structure–activity relationship of the thymidylate synthase inhibitors of Mus musculus in the series of quinazolin-4-one and quinazolin-4-imine derivatives

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