QSPR Modeling of the Reactivity Parameters of Monomers in Radical Copolymerizations

Toropov, Andrey A.; Kudyshkin, V. O.; Воропаева, Н. Л.; Ruban, I. N.; Рашидова, С. Ш.

doi:10.1007/s10947-005-0084-8

Cited by 18 publications

(14 citation statements)

References 2 publications

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“…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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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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“…Classical QSPR modeling of chemical reactions was performed on homogeneous series, either varying the reagents under fixed reaction conditions (solvent, temperature), or inversely varying conditions to study a reaction between given reactants . In these studies topological indices, quantum‐chemical, or mixed descriptors for reagents were used. For an non‐exhaustive overview of the studies carried out in the field of chemical reactivity one can refer on papers by Warr, Baskin et al., or on earlier works ,…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for Kinetic Parameters of Cycloaddition Reactions

Glavatskikh

Madzhidov

Horvath

et al. 2018

Molecular Informatics

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This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (logk), activation energy (Ea) and pre-exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and "dynamical" bonds characterizing chemical transformations. Different scenarios of logk assessment were exploited: direct modeling, application of the Arrhenius equation and temperature-scaled GTM landscapes. The logk models with optimal cross-validated statistics (Q =0.78-0.94 RMSE=0.45-0.86) have been challenged to predict rates for the external test set of 200 reactions, comprising both reactions that were not present in the training set, and training set transformations performed under different reaction conditions. The models are freely available on our web-server: http://cimm.kpfu.ru/models.

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“…4 Many researchers have predicted the reactivity parameters Q and e with QSPR approaches by using the multiple linear regression (MLR) technique and/or artificial neural network (ANN) approaches. 2,[5][6][7][8] In fact, Q and e values are correlated with the reference monomer. 2,[9][10][11] While these descriptors used in these QSPR models did not include the information of the reference monomer.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Regression for Reactivity Parameters of Vinyl Monomers

Yu¹,

Wang

Chen³

2011

J. Chil. Chem. Soc.

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Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, ε-support vector regression (ε-SVR) and ν-support vector regression (ν-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q-e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to developed the SVR models were calculated from styrene and radicals with structures CH 3 CH 2 C 1The optimum ε-SVR model of lnQ (C= 9, ε =0.05 and γ =0.2) and the optimum ν-SVR model of e (C=100, ν = 0.5 and γ =0.4) produced low root mean square (rms) errors for prediction sets: 0.318 and 0.266, respectively. Thus, applying SVR to predict parameters Q and e is successful.

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QSPR Modeling of the Reactivity Parameters of Monomers in Radical Copolymerizations

Cited by 18 publications

References 2 publications

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

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

Predictive Models for Kinetic Parameters of Cycloaddition Reactions

Support Vector Machine Regression for Reactivity Parameters of Vinyl Monomers

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