• 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.35 no.7
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• pp.2065-2071
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• 2014

${\beta}$-Secretase (beta-amyloid converting enzyme 1 [BACE1]) is involved in the first and rate-limiting step of ${\beta}$-amyloid ($A{\beta}$) peptides production, which leads to the pathogenesis of Alzheimer's disease(AD). Therefore, inhibition of BACE1 activity has become an efficient approach for the treatment of AD. Ligand-based and docking-based 3D-quantitative structure-activity relationship (3D-QSAR) studies of acyl guanidine analogues were performed with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to obtain insights for designing novel potent BACE1 inhibitors. We obtained highly reliable and predictive CoMSIA models with a cross-validated $q^2$ value of 0.725 and a predictive coefficient $r{^2}_{pred}$ value of 0.956. CoMSIA contour maps showed the structural requirements for potent activity. 3D-QSAR analysis suggested that an acyl guanidine and an amide group in the $R_6$ substituent would be important moieties for potent activity. Moreover, the introduction of small hydrophobic groups in the phenyl ring and hydrogen bond donor groups in 3,5-dichlorophenyl ring could increase biological activity.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.23-37
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• 2012

As Customer Relationship Management (CRM) strategy is becoming a core competence more recently, many companies want a reliable CRM assessment system which enables measuring and diagnosing corporate customer strategies for building an optimized CRM strategy. However, there has been short of researches on developing the CRM diagnosis methodology that is directly applicable to real practices. Drawing upon the theoretical framework of CRM scorecard, we developed and suggested a corporate CRM diagnosis methodology that can systematically understand and assess the corporate CRM capability and performance, guiding their future directions. Companies can search the important but weak areas among various CRM strategy subjects through the proposed diagnostic procedures. This framework has a hierarchical structure that has four evaluative domains each of which has several evaluative subjects consisting of many evaluative themes: the score of upper factor is the weighted average of its subordinate factor scores. And the score of each evaluative theme is the weighted average of quantitative and qualitative evaluative indexes. Quantitative indexes are calculated by analyzing customer and sales data and qualitative ones are derived from survey data. Each evaluative index has more than one measure and its score can be derived from its own formula consisting of the measures. To prove the concept, we applied this framework to a real company and concluded that it might be appropriate to understand the corporate CRM strategy situation, find the pain points, and resolve them for better CRM implementation.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.221-229
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• 1999

We examined the governing factors of fatigue limit in annealed and austempered ductile iron specimens machined micro hole(dia.<0.4mm) in rotary bending fatigue test. Also, the quantitative relationship between fatigue limit and maximum defect size in specimens was investigated. Artificial defect(micro-pit type, dia.<0.4mm) on specimen surface did not bring about an obvious reduction of fatigue limit in austempered ductile iton(ADI) as compared with annealed ductile iron. According to the investigation of ${\sqrt{area}}_c$ which is the critical defect size to crack initiation at artificial defect, ${\sqrt{area}}_c$ of ADI was larger than that of annealed ductile iron. This shows that the situation of crack initiation at artificial defect in ADI is more difficult in comparison with annealed ductile iron. Maximum defect size is one of the important parameters to predict fatigue limit. And, the quantitative relationship, between the fatigue limit ${\sigma}_{\omega}$ and the maximum defect size ${\sqrt{area}}_{max}$ can be expressed to ${\sigma}_{\omega}^n{\cdot}{\sqrt{area}}_{max}=C_2$ where, $C_2$ are constant. Moreover, it is possible to explain the difference in fatigue limit between, austempered and annealed ductile iron by introducing the parameter ${\delta}(=N_{sg}/N_{total})$in a plain spectimen.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• Bulletin of the Korean Chemical Society
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• v.25 no.10
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• pp.1513-1520
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• 2004

We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) procedure. In addition, we carry out a two-dimensional Quantitative Structure-Activity Relationship (2D-QSAR) study, using the Hologram QSAR (HQSAR). We perform these studies, using 53 compounds as a training set and 10 compounds as a test set. The predictive QSAR models have conventional $r^2$ values of 0.955 at CoMFA, 0.917 at CoMSIA, and 0.910 at HQSAR respectively; similarly, we obtain cross-validated coefficient $q^2$ values of 0.822 at CoMFA, 0.763 at CoMSIA, and 0.816 at HQSAR, respectively. From these studies, the CoMFA model performs better than the CoMSIA model.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• Journal of the Korean Chemical Society
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• v.42 no.4
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• pp.378-382
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• 1998

A Quantitative Structure-Activity Relationship of 1-phenylcyclohexyl amine(PCA) and dexoxadral as a receptor has been investigated using semiempirical PM3 MO and Hyper Chem calculation. A set of 19 analogues of PCA was chosen for the study using a selection procedure aimed at minimizing the interparameter correlations, while ensuring that the frontier orbital covered the maximum possible range of LogP. The results show that the FOS and LogP is a good structural parameter to predict the maximum electroshock effective dose ($MES\;ED_{50}$) and toxicity dose ($TD_{50}$) for PCA derivatives.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.673-682
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• 2018

One of the most widely used methods to estimate core damage during a nuclear power plant accident is containment radiation measurement. The evolution of severe accidents is extremely complex, leading to uncertainty in the containment dose rate (CDR). Therefore, it is difficult to accurately determine core damage. This study proposes to conduct uncertainty analysis of CDR for core damage assessment. First, based on source term estimation, the Monte Carlo (MC) and point-kernel integration methods were used to estimate the probability density function of the CDR under different extents of core damage in accident scenarios with late containment failure. Second, the results were verified by comparing the results of both methods. The point-kernel integration method results were more dispersed than the MC results, and the MC method was used for both quantitative and qualitative analyses. Quantitative analysis indicated a linear relationship, rather than the expected proportional relationship, between the CDR and core damage fraction. The CDR distribution obeyed a logarithmic normal distribution in accidents with a small break in containment, but not in accidents with a large break in containment. A possible application of our analysis is a real-time core damage estimation program based on the CDR.