Prediction of the acidic dissociation constant (pK<sub>a</sub>) of some organic compounds using linear and nonlinear QSPR methods

Goudarzi, Nasser; Goodarzi, Mohammad

doi:10.1080/00268970902950394

Cited by 21 publications

(20 citation statements)

References 37 publications

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“…[30][31][32] The rate of racemization was determined experimentally, both via D-exchange experiments employing 1 H NMR or following the time dependence of the loss of the specific rotations in enantiomerically enriched molecules.…”

Section: Resultsmentioning

confidence: 99%

Base catalyzed racemization of amino acid derivatives

D’Arrigo

Arosio

Cerioli

et al. 2011

Tetrahedron: Asymmetry

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Section: Resultsmentioning

confidence: 99%

Base catalyzed racemization of amino acid derivatives

D’Arrigo

Arosio

Cerioli

et al. 2011

Tetrahedron: Asymmetry

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“…Insofar as artificial neural networks (ANN) utilize intrinsic mechanisms of input data classification according to specified criteria and without them, such mathematical models are frequently used in QSPR methods to distinguish most statistically significant descriptors [157,159,160,[163][164][165]. Support vector machine (SVM) [157], recursive ANNs (RecNN) [192,193], radial basis ANNs [161], and back propagation networks (BPG) [175] are involved here.…”

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

“…This procedure has become very popular for prediction of many other physicochemical parameters of compounds and their biological activity [150,[176][177][178][179][180][181][182][183][184][185][186][187][188]. Extensive use of QSPR in solving various problems of physical organic chemistry is favored by development of computational methods and instruments and statistical processing procedures such as principal component analysis (PCA) [154,[163][164][165]185], independent component analysis (ICA) [189], partial least squares (PLS) [153,154,156,157,168,174] in combination with iterative variable elimination (IVE-PLS) [156], multilinear regression (MLR) [155,157,159,160,175,187], and variable importance in projection (VIP) [190,191].…”

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

Modern methods for estimation of ionization constants of organic compounds in solution

Zevatskii

Samoilov²

2011

Russ J Org Chem

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The review considers main up-to-date approaches to the calculation of ionization constants of organic compounds in solutions. Methods based on quantum-chemical calculations, continuum solvent models, cluster models, and quantitative structure-property relationships are discussed. The results of calculations of pK a values by different methods, obtained in the past decade, are compared. REVIEW ACID IONIZATION CONSTANTS AND THEIR SIGNIFICANCEA quantitative parameter of acid-base properties of an HA molecule is the equilibrium constant for proton transfer (K a ) from acid HA to solvent molecule M with formation of lyonium ion and acid anion [1-3] (if an acid is an onium ion, neutral base is formed).

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“…In recent yeares, several QSAR and QSPR models have been used based on both linear and non-linear methods that aimed to predict different activities and properties [60][61][62][63][64][65][66][67][68][69][70][71] .…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Retention Index Relationship (QSRIR) Study of Monomethylalkanes on the Methylsilicone OV-1 Stationary Phase

Goudarzi¹,

Kalhor

2012

Analytical Chemistry Letters

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A back-propagation artificial neural network (ANN) is used to create a model of gas chromatography retention indices for a data set of 196 monomethylalkanes on the methylsilicone OV-1 stationary phase. Multlinear regression (MLR) model of the same data is developed for comparison. The quantitative structure-property relationship (QSPR) models are validated by cross-validation as well as application of the models for prediction of retention indices of external set of compounds which do not have contribution to model development steps. The models are also validated by statistical parameters and Y-randomization. Both linear and non-linear methods provide accurate predictions. The mean-squared errors (MSE) for validation and test sets for MLR are 18.44 and 28.96, and those for ANN are 2.24 and 4.21, respectively.

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Prediction of the acidic dissociation constant (pK_a) of some organic compounds using linear and nonlinear QSPR methods

Cited by 21 publications

References 37 publications

Base catalyzed racemization of amino acid derivatives

Base catalyzed racemization of amino acid derivatives

Modern methods for estimation of ionization constants of organic compounds in solution

Quantitative Structure-Retention Index Relationship (QSRIR) Study of Monomethylalkanes on the Methylsilicone OV-1 Stationary Phase

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Prediction of the acidic dissociation constant (pKa) of some organic compounds using linear and nonlinear QSPR methods

Cited by 21 publications

References 37 publications

Base catalyzed racemization of amino acid derivatives

Base catalyzed racemization of amino acid derivatives

Modern methods for estimation of ionization constants of organic compounds in solution

Quantitative Structure-Retention Index Relationship (QSRIR) Study of Monomethylalkanes on the Methylsilicone OV-1 Stationary Phase

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Prediction of the acidic dissociation constant (pK_a) of some organic compounds using linear and nonlinear QSPR methods