A QSPR Study for the Prediction of the p<i>Ka</i> of N-Base Ligands and Formation Constant <i>Kc</i> of Bis(2,2′-bipyridine)Platinum(II)-N-Base Adducts Using Quantum Mechanically Derived Descriptors

Palaz, Selamı; Türkkan, Baki; Eroğlu, Erol

doi:10.5402/2012/260171

Cited by 7 publications

(4 citation statements)

References 25 publications

(26 reference statements)

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“…Several pK a prediction programs exist (Liao and Nicklaus, 2009). Commercial predictors span a range of mechanisms to predict the protonation state of particular atoms, including linear free energy relationships (LFER) that use a dictionary of chemical substructures (Lee et al, 2007), quantitative structure-property relationships (QSPR) (Jover et al, 2008;Palaz et al, 2012), and quantum chemical and ab initio methods (Bochevarov et al, 2013;Eckert and Klamt, 2006;Eckert et al, 2009;Klamt et al, 2010;Klamt et al, 2003;Vareková et al, 2011). Semi-empirical models calculate descriptors for each ionizable chemical functional group, after which pK a values are predicted using machine learning or tree-based models (Jelfs et al, 2007;Xing et al, 2003).…”

Section: Appendixmentioning

confidence: 99%

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

Strope

Mansouri

Clewell

et al. 2018

Science of The Total Environment

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Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vd). Here, estimates of ionization equilibrium constants (i.e., pK) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vd for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pK. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vd on predicted pK using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vd generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vd and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.

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Section: Appendixmentioning

confidence: 99%

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

Strope

Mansouri

Clewell

et al. 2018

Science of The Total Environment

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“…A QSPR study using quantum descriptors was reported to predict the acid dissociation constant and the formation constant of 22 compounds including pyridines, purines, pyrimidines, and quinoline …”

Section: Introductionmentioning

confidence: 99%

Quantitative modeling for prediction of thermodynamic properties of some pyridine derivatives using molecular descriptors and genetic algorithm‐multiple linear regressions

Hajibabaei

Shafiei

Abdoli‐Senejani

2019

J Chinese Chemical Soc

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Pyridines are compounds with a wide range of biological activities and they are the basis of several groups of drugs. In this study, the relationship between molecular descriptors and the thermal energy (E th kJ/mol), heat capacity (C v J/molK), entropy (S J/mol K), enthalpy of formation (ΔH f kJ/mol), and Gibbs free energy (ΔG kJ/mol) of pyridine derivatives is studied. These properties were calculated at the Hartree-Fock level of theory and 6-311G* G basis sets by Gaussian 09 software. The chemical structures of 171 pyridine derivatives were drawn by the Gauss View 05 program.The genetic algorithm-multiple linear regression (GA-MLR) and backward methods-were used to select the suitable descriptors and also for predicting the thermodynamic properties of pyridine derivatives. The correlations between the molecular descriptors in the best models were discussed by the Pearson coefficient correlation and collinearity statistics. The predictive powers of the GA-MLR models are studied using leave-one-out (LOO) cross-validation and external test set. The best quantitative structure-property relationship (QSPR) models are obtained based on the statistical parameters, such as R 2 , Q 2 LOO, and RMSE values for the data sets. The predictive ability of the GA-MLR models with two-three selected molecular descriptors was found to be satisfactory and could be used for modeling and predicting of the mentioned properties of nontested pyridine derivatives. K E Y W O R D S QSPR, pyridine derivatives, leave-one-out cross-validation, genetic algorithm-multiple linear regression

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“…0.95 sic.) [16] for N-Base ligands at the semi empirical AM1 level of theory, as well as a Principal Components Analysis (PCA) for organic and inorganic acids (RMSE = 0.0195) [17]. Moreover, genetic algorithms (GA) and neural networks (NN) have employed frontier orbital energies for a chemical space of sixty commercial drugs [18] (GA, R 2 = 0.703; NN, R 2 = 0.929).…”

Section: Introductionmentioning

confidence: 99%

Calculation of VS,max and Its Use as a Descriptor for the Theoretical Calculation of pKa Values for Carboxylic Acids

et al. 2018

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The theoretical calculation of pKa values for Brønsted acids is a challenging task that involves sophisticated and time-consuming methods. Therefore, heuristic approaches are efficient and appealing methodologies to approximate these values. Herein, we used the maximum surface electrostatic potential (VS,max) on the acidic hydrogen atoms of carboxylic acids to describe the H-bond interaction with water (the same descriptor that is used to characterize σ-bonded complexes) and correlate the results with experimental pKa values to obtain a predictive model for other carboxylic acids. We benchmarked six different methods, all including an implicit solvation model (water): Five density functionals and the Møller–Plesset second order perturbation theory in combination with six different basis sets for a total of thirty-six levels of theory. The ωB97X-D/cc-pVDZ level of theory stood out as the best one for consistently reproducing the reported pKa values, with a predictive power of 98% correlation in a test set of ten other carboxylic acids.

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A QSPR Study for the Prediction of the pKa of N-Base Ligands and Formation Constant Kc of Bis(2,2′-bipyridine)Platinum(II)-N-Base Adducts Using Quantum Mechanically Derived Descriptors

Cited by 7 publications

References 25 publications

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

Quantitative modeling for prediction of thermodynamic properties of some pyridine derivatives using molecular descriptors and genetic algorithm‐multiple linear regressions

Calculation of VS,max and Its Use as a Descriptor for the Theoretical Calculation of pKa Values for Carboxylic Acids

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