QSPR Correlation of the Melting Point for Pyridinium Bromides, Potential Ionic Liquids

Katritzky, Alan R.; Lomaka, Andre; Petrukhin, Ruslan; Jain, Ritu; Karelson, Mati; Visser, Ann E.; Rogers, Robin D.

doi:10.1021/ci0100503

Cited by 177 publications

(163 citation statements)

References 27 publications

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“…Equations 1 and 2 are also similar to melting-point QSARs derived for other ionic liquids in that they involve hydrogen-bond descriptors and NRI. 9,17 Ten excellent, single-descriptor QSPRs were derived for the densities of the bromide salts and are displayed in Table 5. Most descriptors include either various measures of hydrogen-bonding capability or have to do with the partial atomic charge distribution and are interpreted as characterizing the interactions between ions.…”

Section: Resultsmentioning

confidence: 99%

“…19 The resulting melting-point QSPRs have r 2 values ranging from 0.690-0.788, except for the QSPR derived for a subset of 18 substituted imidazolium bromides, which has an r 2 value of 0.943. 9,17 Liquid-density QSPRs have been previously derived for alkanes in two studies. 20,21 In the first study, structural descriptors computed from molecular graphs were used to derive physical properties, including liquid density, for 134 alkanes.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Structure−Property Relationships for Melting Points and Densities of Ionic Liquids

et al. 2004

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Although some of the descriptors that appear in our QSPRs were designed to describe chemical reactions, we infer that they serve in this study to quantify interactions between the cation and anion. Melting-point QSPRs were then derived from molecular orbital, thermodynamic, and electrostatic descriptors. Good correlations with the experimental data were found. The correlation coefficients for three-parameter melting-point QSPRs and for one-parameter density QSPRs exceed 0.9. Although some of the descriptors that appear in our QSPRs were designed to describe chemical reactions, we infer that they serve in this study to quantify interactions between the cation and anion.3

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Structure−Property Relationships for Melting Points and Densities of Ionic Liquids

et al. 2004

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“…However, when the individual points are taken into consideration, some very interesting results emerge. For instance, it can be noticed that methanol (45) and methylamine (77), ethanol (46) and ethylamine (78), and 1-propanol (47) and 1-propylamine (79) lay very close to each other, especially in the plot of trial III. This should mean that, in the representational space, an alkanol is much more similar to a primary amine with the same number of carbon atoms than to the corresponding superior homologous one.…”

Section: Resultsmentioning

confidence: 92%

“…CODESSA PRO has been successfully applied to a large variety of problems. 22,23,[44][45][46] In recent years, various approaches have been taken into consideration to realize the function g by more complex machine-learning models such as the neural networks. 24,41,47 NNs, in fact, are powerful data modeling tools able to approximate nonlinear relationships among chemical structural parameters and physical-chemical properties.…”

Section: Prediction Of the Solvation Free Energymentioning

confidence: 99%

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Bernazzani

Duce

Micheli

et al. 2006

J. Chem. Inf. Model.

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In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

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“…Two separate studies were reported for pyridinium, [22] and benzylimidazolium bromides. [23] Melting point data were withdrawn from the Beilstein database and ranged from 30 to 370 ºC.…”

Section: Reports On Modelling Melting Point For Ionic Liquidsmentioning

confidence: 99%

Physico-Chemical Properties of Task-Specific Ionic Liquids

Branco¹,

Carrera²,

Aires-de-Sousa³

et al. 2011

Ionic Liquids: Theory, Properties, New Approaches

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QSPR Correlation of the Melting Point for Pyridinium Bromides, Potential Ionic Liquids

Cited by 177 publications

References 27 publications

Quantitative Structure−Property Relationships for Melting Points and Densities of Ionic Liquids

Quantitative Structure−Property Relationships for Melting Points and Densities of Ionic Liquids

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Physico-Chemical Properties of Task-Specific Ionic Liquids

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