• Journal of the Korean Chemical Society
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• v.42 no.6
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• pp.646-651
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• 1998

The molecular structure of sulfur compounds and the retention relationship are studied by gas chromatography. Analyzed sulfur compounds are, hydrogen sulfide, sulfur dioxide, carbon disulfide, ethyl mercaptan, dimethyl sulfide, iso-propyl mercaptan, normal propyl mercaptan, ethyl methyl sulfide, tert-butyl mercaptan, tetrahydrothiophene, thiophene, and 2-chlorothiophene. Multiple linear regression explains the retention relationship of molecular descriptors. In GC the temperature program is 30$^{\circ}C$ held for 10.5 min, and then increased to 150$^{\circ}C$ at a rate 15$^{\circ}C$/min. Predicted equation for relative retention time (RRT) using SAS program is as follows; $RRT=0.121bp+14.39dp-8.94dp^2+0.0741sqmw-35.78\; (N=8,\; R^2=0.989, \;Variance=0.175,\;F=66.21)$. RRTs are function of boiling point, the square root of molecular weight, molecular dipole moment, and boiling point effects mostly on RRT. The RRT is maximized at the molecular dipole moment of 0.805D, when using nonpolar columns. The planar and highly symmetric compounds are eluted slowly. The square, of correlation coefficient $(R^2)$ using SAS program, is 0.989, and the variance is 0.175 in training sets. For three sulfur compounds, the variance between observed RRTs and predicted RRTs is 0.432 in testing sets.

• Journal of Applied Biological Chemistry
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• v.50 no.2
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• pp.52-57
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• 2007

Quantitative structure-activity relationships (QSARs) on the inhibition activities by oleanolic acid analogues (1-19) as a potent inhibitor against protein tyrosine phosphatase-1B were studied quantitatively using 2D-QSAR and HQSAR methodologies. The inhibition activity was dependent on the variations of $R_{4-}$substituent, and as shown in 2D-QSAR model ($r^2=0.928$), it has a tendency to increase as the negative Randic Indice (RI) goes up. The size of the molecular fragments used in HQSAR varied from five to eight. The fragment distinctions had the best statistic value, whose predictability is $q^2=0.785$ and correlation coefficient is $r^2=0.970$, on condition of connections. From the atomic contribution maps, the factor that contributes to the inhibition activities is the $C_{15}{\sim}C_{17}$ bond in the D ring. From the analysis result of these two the models, the structural distinctions and descriptors that contribute to the inhibition activities were obtained.

• Bulletin of the Korean Chemical Society
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• v.32 no.12
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• pp.4352-4360
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• 2011

A four dimensional quantitative structure activity relationship analysis was applied to a series of 50 flavonoid inhibitors of $p56^{lck}$ protein tyrosine kinase by the molecular comparative electron topological method. It was found that the -log (IC50) values of the compounds were highly dependent on the topology, size and electrostatic character of the substituents at seven positions of the flavonoid scaffold in this study. Depending on the negative or positive charge of the groups correctly embedded in these substituents, three-dimensional bio-structure to increase or decrease -log (IC50) values in the training set of 39 compounds was predicted. The test set of 11 compounds was used to evaluate the predictivity of the model. To generate 4D-QSAR model, the defined function groups and pharmacophore used as topological descriptors in the calculation of activity were of sufficient statistical quality ($R^2$ = 0.72 and $Q^2$ = 0.69). Ligand docking approach by using Dock 6.0. These compounds include many flavonoid analogs, They were docked onto human families of p56lck PTKs retrieved from the Protein Data Bank, 1lkl.pdb.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2397-2404
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• 2011

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.

• 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.31 no.5
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• pp.1361-1367
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• 2010

A series of O,O-dialkyl-1-phenoxyacetoxy-1-methylphosphonate analogues (1~22) as a new class of potent inhibitors of pyruvate dehydrogenase were synthesized and 3D-QSARs (three dimensional qantitative structure-activity relationships) models on the pre-emergency herbicidal activity against the seed of cucumber (Cucumus Sativa L.) were derived and discussed quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indeces analysis (CoMSIA) methods. The statistical values of CoMSIA models were better predictability and fitness than those of CoMFA models. The inhibitory activities according to the optimized CoMSIA model I were dependent on the electrostatic field (41.4%), the H-bond acceptor field (26.0%), the hydrophobic field (20.8%) and the steric field (11.7%). And also, it was found that the optimized CoMSIA model I with the sensitivity to the perturbation ($d_q{^{2'}}/dr^2{_{yy'}}$ = 0.830) and the prediction ($q^2$ = 0.503) produced by a progressive scrambling analyses were not dependent on chance correlation. From the results of graphical analyses on the contour maps with the optimized CoMSIA model I, it is expected that the structural distinctions and descriptors that subscribe to herbicidal activities will be able to apply new an herbicide design.

• Bulletin of the Korean Chemical Society
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• v.31 no.5
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• pp.1355-1360
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• 2010

In order to search new inhibitors against farnesyl protein transferase (FPTase), a series of 2,3-bis-benzylidenesuccinaldehyde derivatives (1-29) were synthesized and their inhibition activities ($pI_{50}$) against FPTase were measured. From based on the reported results that the inhibitory activities of dimers 2,3-bis-benzylidenesuccinaldehydes were higher than those of monomers cinnamaldehydes, 3D-QSARs on FPTase inhibitory activities of the dimers (1-29) were studied quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The statistical qualities of the optimized CoMFA model II ($r^2{_{cv.}}$= 0.693 and $r^2{_{ncv.}}$= 0.974) was higher than those of the CoMSIA model II ($r^2{_{cv.}}$ = 0.484 and $r^2{_{ncv.}}$ = 0.928). The dependence of CoMFA models on chance correlations was evaluated with progressive scrambling analyses. And the inhibitory activity exhibited a strong correlation with steric factors of the substrate molecules. Therefore, from the results of graphical analyses on the contour maps and of predicted higher inhibitory active compounds, it is suggested that the structural distinctions and descriptors that contribute to inhibitory activities ($pI_{50}$) against FPTase will be able to applied new inhibitor design.

• Bulletin of the Korean Chemical Society
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• v.33 no.10
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• pp.3285-3292
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• 2012

The interaction of thiazole drug with (6,0) zigzag single-walled boron nitride nanotube of finite length in gas and solvent phases was studied by means of density functional theory (DFT) calculations. In both phases, the binding energy is negative and presenting characterizes an exothermic process. Also, the binding energy in solvent phase is more than that the gas phase. Binding energy corresponding to adsorption of thiazole on the BNNT model in the gas and solvent phases was calculated to be -0.34 and -0.56 eV, and about 0.04 and 0.06 electrons is transferred from the thiazole to the nanotube in the phases. The significantly changes in binding energies and energy gap values by the thiazole adsorption, shows the high sensitivity of the electronic properties of BNNT towards the adsorption of the thiazole molecule. Frontier molecular orbital theory (FMO) and structural analyses show that the low energy level of LUMO, electron density, and length of the surrounding bonds of adsorbing atoms help to the thiazole adsorption on the nanotube. Decrease in global hardness, energy gap and ionization potential is due to the adsorption of the thiazole, and consequently, in the both phases, stability of the thiazole-attached (6,0) BNNT model is decreased and its reactivity increased. Presence of polar solvent increases the electron donor of the thiazole and the electrophilicity of the complex. This study may provide new insight to the development of functionalized boron nitride nanotubes as drug delivery systems for virtual applications.

• Mass Spectrometry Letters
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• v.13 no.3
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• pp.49-57
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• 2022

During electrospray ionization mass spectrometry (ESI-MS) analysis of proteins, the addition of supercharging agents allows for adjusting the maximal charge state, affecting the charge state distribution, and increases the number of ions reaching the detector thus, improving signal detection. We postulate that in di-substituted arene isomers, molecules with higher polarizability values should generate greater interactions and hence elicit higher signal intensities. Polarizability is an electronic parameter which has been demonstrated to predict many chemical interactions. Many properties can be predicted based on charge polarization. Molecular polarizability is a vital descriptor for explaining intermolecular interactions. We employed DFT (density functional/Hartree-Fock hybrid model, B3LYP)-derived descriptors and computed molecular polarizability for ten disubstituted arene reagents, each set made up of three (ortho, meta, para) isomers, with reported use as supercharging reagents during ESI experiments. The atomic electronic inputs were ionization potential (IP), electron affinity (EA), electronegativity (𝛘), hardness (η), chemical potential (µ), and dipole moment (D). We determined that the para isomers showed the highest polarizability values in nine of the ten sets. There was no difference between the ortho and meta isomers. Polarizability also increased with increasing complexity of the substituents on the benzene ring. Polarizability correlated positively with IP, EA, 𝛘, η, and D but correlated negatively with chemical potential. This DFT study predicts that the para isomers of di-substituted arene isomers should elicit the strongest ESI responses. An experimental comparison of the three isomers, especially of larger supercharging molecules, could be carried out to establish this premise.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.378-384
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• 2023

In this study, we aim to derive the descriptor vector conditions that can simultaneously achieve the efficiency and accuracy of artificial Neural Network Potentials (NNP). The material system selected is silicon, a highly applicable material in various industries. Atomic structure-dependent energy data for training artificial neural networks were generated through density functional theory calculations. Behler-Parrinello type atomic-centered symmetric functions were employed as descriptors, and various length vector NNPs were generated. These NNPs were applied to reproduce the structure and mechanical properties of silicon materials in molecular dynamics simulations. In our findings, the minimum vector length for achieving both learning and computational efficiency while maintaining property reproducibility is approximately 50. It was also observed that, for the same conditions, incorporating more angle-dependent symmetric functions into the descriptor vector, could enhance the accuracy of NNP. Our results can provide guidelines for optimizing the conditions of descriptor vectors to achieve both efficiency and accuracy of NNP, simultaneously.