• Journal of the Korean Chemical Society
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• v.44 no.6
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• pp.501-506
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• 2000

Some previous linear equations to predict hydrocarbon heats of formation are generalized. The basic molecular descriptors used for the QSPR analysis are atoms and chemcal bonds. This particular choice makes the method extremely simple and quite inexpensive. The predictions for a set of 19 alkenes yield deviations which are similar to experimental uncertainties. Some possible extensions of the method are pointed out.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4341-4346
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• 2011

Impact sensitivity, one of the most important screening factors for novel high energy density materials (HEDMs), was predicted by use of quantitative structure-property relationship (QSPR) based on the electrostatic potential (ESP) values calculated on the van der Waals molecular surface (MSEP). Among various 3D descriptors derived from MSEP, we utilized total and positive variance of MSEP, and devised a new QSPR equation by combining three other parameters. We employed 37 HEDMs bearing a benzene scaffold and nitro substituents, which were also utilized by Rice and Hare. All the molecular structures were optimized at the B3LYP/6-31G(d) level of theory and confirmed as minima by the frequency calculations. Our new QSPR equation provided a good result to predict the impact sensitivities of the molecules in the training set including zwitterionic molecules.

• Bulletin of the Korean Chemical Society
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• v.27 no.2
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• pp.211-218
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• 2006

This work deals with 24 substances composed of nitrogen-containing heterocycles. The relationships between the chromatographic retention factor (k) and those physicochemical properties which are relevant in quantitative structure-properties relationship (QSPR) studies, such as the polarizability $(\alpha)$, molar refractivity (MR), lipophilicity (logP), dipole moment $(\mu)$, total energy $(E_{tot})$, heat of formation $(\Delta H_f)$, molecular surface area $(S_M)$, and binding energy $(E_b)$, were investigated. The accuracy of the simple linear regressions between the chromatographic retention and the descriptors for all of the compounds was satisfactory (correlation coefficient, $0.8 \leq r \leq 1.0$). The QSPR models of these nitrogen-containing heterocyclic compounds could be predicted with a multiple linear regression equation having the statistical index, r = 1.000. This work demonstrated the successful application of the multiple linear approaches through the development of accurate predictive equations for retention factors in liquid chromatography.

• Bulletin of the Korean Chemical Society
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• v.32 no.11
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• pp.3865-3872
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• 2011

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima of azobenzene dyes. The entire set of 191 azobenzenes was divided into a training set of 150 azobenzenes and a test set of 41 azobenzenes according to Kennard and Stones algorithm. A seven-descriptor model, with squared correlation coefficient ($R^2$) of 0.8755 and standard error of estimation (s) of 14.476, was developed by applying stepwise multiple linear regression (MLR) analysis on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-many-out crossvalidation procedure, randomization tests, and validation through the test set.

• Analytical Science and Technology
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• v.28 no.3
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• pp.187-195
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• 2015

The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that the heat of sublimation was related to dispersion, H-bond, electrostatic forces, and the dipole-dipole interaction between inter-molecules.

• Bulletin of the Korean Chemical Society
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• v.27 no.12
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• pp.2011-2018
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• 2006

We have investigated the solvent effects on $\Delta log\;K_s $(the difference of stability constant of binding) and the different free energies of binding of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6, i.e., the selectivity of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6 using a Monte Carlo simulation of statistical perturbation theory (SPT) in diverse solvents. The stability constant ($\Delta log\;K_s $) of binding of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6, in $CH_3OH$ was calculated in this study as -1.06 agrees well with the different experimental results of -0.44~-0.6, respectively. We have reported here the quantitative solvent-polarity relationships (QSPR) studied on the solvent effects the relative free energies of binding of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6. From the calculated coefficients of QSPR, we have noted that solvent polarity (ET) and Kamlet -Tafts solvatochromic parameters (b ) dominate the differences in relative solvation Gibbs free energies of $Nd^{3+}$ and $Eu^{3+}$ ions but basicity (Bj) dominates the negative values in differences in the stability constant ($\Delta log\;K_s $) as well as the relative free energies of binding of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6 and acidity (Aj) dominates the positive values in differences in the stability constant ($\Delta log\;K_s $) as well as the relative free energies of binding of $Nd^{3+}$ and $Eu^{3+}$ ions to 18-crown-6.

• Analytical Science and Technology
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• v.28 no.4
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• pp.270-277
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• 2015

Boiling point (BP) is one of the most fundamental physicochemical properties of organic compounds to characterize and identify the thermal characteristics of target compounds. Previously developed QSPR equations, however, still had some limitation for the specific compounds, like high-energy molecules, mainly because of the lack of experimental data and less coverage. A large BP dataset of 5,923 solid organic compounds was finally secured in this study, after dedicated pre-filtration of experimental data from different sources, mostly consisting of compounds not only from common organic molecules but also from some specially used molecules, and those dataset was used to build the new BP prediction model. Various machine learning methods were performed for newly collected data based on meaningful 2D descriptor set. Results of combined check showed acceptable validity and robustness of our models, and consensus approaches of each model were also performed. Applicability domain of BP prediction model was shown based on descriptor of training set.

• Analytical Science and Technology
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• v.20 no.4
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• pp.339-346
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• 2007

To search the ninhydrin derivatives that have high chromogenic and fluorogenic properties, molecular holographic quantitative structure property relationship (HQSPR) models on the reactivity between glycine and ninhydrin analogues as latent fingerprint detector were derived and investigated quantitatively. The ${\varepsilon}LUMO$ (e.v.) energy of ninhydrin molecule was an important factor to reactivity of ninhydrin. And, it is suggested that the nucleophilic reaction by orbital-controlled reaction from the frontier molecular orbital (FMO) interaction between glycine and ninhydrin derivatives was more superior than that of electrophilic reaction by charged controlled reaction. The analytical results in atomic contribution maps also shows that the reactivity of ninhydrin was increased by meta-substituents as strong electron withdrawing groups on the benzo ring. Therefore, it is sugested by HQSPR and QSPR model that the 5,6-dinitroninhydrin molecule would increase the reactivity as much as three times as compared to none substituted ninhydrin molecule.

• Bulletin of the Korean Chemical Society
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• v.29 no.2
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• pp.431-437
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• 2008

The solvent effects on the relative free energies of binding of K+ and Na+ ions to 12-crown-4 and Dlog Ks (the difference of stability constant of binding) have been investigated by a Monte Carlo simulation of statistical perturbation theory (SPT) in several solvents. Comparing the relative free energies of binding of K+ and Na+ ions to 12-crown-4, in CH3OH of this study with experimental works, there is a good agreement among the studies. We have reported here the quantitative solvent-polarity relationships (QSPR) studied on the solvent effects the relative free energies of binding of K+ and Na+ ions to 12-crown-4. We noted that DN(donor number) dominates the differences in relative solvation Gibbs free energies of K+ and Na+ ions and DN dominates the negative values in differences in the stability constant (Dlog Ks) as well as the relative free energies of binding of K+ and Na+ ions to 12-crown-4 and p* (Kamlet-Tafts solvatochromic parameters) dominates the positive values in differences in the stability constant (Dlog Ks) as well as the relative free energies of binding of K+ and Na+ ions to 12-crown-4.

• Bulletin of the Korean Chemical Society
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• v.30 no.1
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• pp.67-76
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• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.