QSPR Modeling of critical parameters of organic compounds belonging to different classes in terms of the simplex representation of molecular structure

Mokshina, Elena; Кузьмин, В. Е.; Nedostup, V. I.

doi:10.1134/s1070428014030026

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…An insignificant contribution of descriptors weighted by hydrogen bond may be accounted for by the small number of compounds capable of forming hydrogen bonds, 11 to 22% for different properties. Comparison with the variable importance in models for pure substances [2,4] showed similar trends: the contributions of descriptors related to electrostatic and van der Waals interactions predominate for both pure substances and their binary mixtures.…”

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confidence: 78%

“…Furthermore, previously obtained QSPR models of organic compounds based on 2D representation of molecular structure can be employed under thermodynamic conditions analogous to critical point of a pure substance. As we showed in [2][3][4], the set of descriptors for pure substances includes both parameters characterizing intermolecular interactions and steric factors ensuing, in particular, from the van der Waals model.…”

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confidence: 99%

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QSPR modeling of critical properties of organic binary mixtures

et al. 2016

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QSPR models for the critical temperatures, critical volumes, and critical pressures of binary organic mixtures are given. The binary organic mixtures have been described in terms of the mixture modification of simplex representation of molecular structure. The accuracy of the obtained models is comparable to the recommended one, the mean error ranging from 6.8 to 14.6%. The models imply that electronic polarizability is the most important factor for the critical volume and that the critical temperature and critical pressure are determined primarily by van der Waals and electrostatic interactions.Critical properties of substances are important not only for understanding fundamental principles of their thermodynamic behavior but also from the application viewpoint since most methods for predicting unknown properties of compounds utilize the principle of corresponding states, which is based on critical parameters. Critical points exist not only for pure substances but also for their mixtures, and they determine stability parameters of mixtures, i.e., the transition from a heterogeneous mixture (phase boundary) to a homogeneous solution (single phase) [1].

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confidence: 78%

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confidence: 99%

QSPR modeling of critical properties of organic binary mixtures

et al. 2016

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“…SiRMS-based 2D-QSPR models attempting to predict the critical temperatures (Tc), volumes (Vc), and pressures (Pc) and Pitzer’s acentric factors (ω) of organic compounds used 407, 382, 309, and 331 compounds, respectively, all from NIST WebBook [ 75 , 76 , 101 ]. Structurally diverse organic compounds were used and this resulted in high statistics for the QSPR model after 5-fold external cross validation ( R 2 = 0.97–0.99, R 5f 2 = 0.86–0.95, predicted error T c and V c <3%, predicted error Pc and ω 3–10%).…”

Section: Qspr Models Based On Simplex Descriptorsmentioning

confidence: 99%

“…Lipophilicity and water solubility [44], [66][67][68][69][70][71][72] Luminescent properties [73] Thermodynamic properties [74][75][76][77][78][79][80] Properties of ionic compounds and materials [81], [82] Properties of nanosystems [63], [83], [84] The concept of chiral simplexes helped us to understand these problems. As a mathematical object, a simplex is a ndimensional polyhedron, which is a convex shell (n+1) of points (vertexes of simplex) that do not lie in the (n-1)-dimensional plane [85].…”

Section: Qspr Tasksmentioning

confidence: 99%

Simplex representation of molecular structure as universal QSAR/QSPR tool

et al. 2021

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We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment “topography” identification.

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“…Despite that recent works that make use of other QSPR approaches to predict the critical properties of organic compounds, such as support vector regression [ 23 ] or nonlinear random forest learning algorithms [ 24 ], have also included the estimation of the acentric factor, the number of papers that can be found on MLR and ANN models for the prediction of this last property is quite limited. Many examples of ANN models involve the estimation of the acentric factor for petroleum fractions.…”

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confidence: 99%

Comparison between Multi-Linear- and Radial-Basis-Function-Neural-Network-Based QSPR Models for The Prediction of The Critical Temperature, Critical Pressure and Acentric Factor of Organic Compounds

Banchero

Manna

2018

Molecules

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Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance.

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QSPR Modeling of critical parameters of organic compounds belonging to different classes in terms of the simplex representation of molecular structure

Cited by 8 publications

References 11 publications

QSPR modeling of critical properties of organic binary mixtures

QSPR modeling of critical properties of organic binary mixtures

Simplex representation of molecular structure as universal QSAR/QSPR tool

Comparison between Multi-Linear- and Radial-Basis-Function-Neural-Network-Based QSPR Models for The Prediction of The Critical Temperature, Critical Pressure and Acentric Factor of Organic Compounds

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