• 통합자연과학논문집
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• 제4권3호
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• pp.210-215
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• 2011

Holographic quantitative structure-activity relationships (HQSAR) is a useful tool to correlates structures with their biological activities. HQSAR is a two dimensional (2D) QSAR methodology, which generates QSAR equations through 2D fingerprint and correlates it with biological activity. Here, we report a 2D-QSAR model for a series of fifty-one 3,4-dihydroxychalcones derivatives utilizing HQSAR methodology. We developed HQSAR model with 6 optimum numbers of components (ONC), which resulted in cross-validated correlation coefficient ($q^2$) of 0.855 with 0.283 standard error of estimate (SEE). The non-cross-validated correlation coefficient (r2) with 0.966 indicates the model is predictive enough for analysis. Developed HQSAR model was binned in to a hologram length of 257. Atomic contribution map revealed the importance of dihydroxy substitution on phenyl ring.

• 품질경영학회지
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• 제46권3호
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• pp.551-568
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• 2018

Purpose: This study addressed the relationship between technological innovation activity and management efficiency of Korean automobile firms. We tested the hypothesis of non-liner relationship of innovation activity in relation to management efficiency. Methods: We discussed prior literature in the firm innovation strategy and management efficiency studies to provide better understanding of relationships between technological innovation activity and management efficiency. As a result, we developed develop and tested a model (Inverted-U shaped) capturing the non-linear impact of technological innovation activity. While we used R&D expenditure and patent registration data for measuring firms' innovation activity, management efficiency was evaluated by using DEA(Data Envelopment Analysis). Results: Main findings of our empirical analysis indicated that the relationships between technological innovation activity and management efficiency was inverted U shaped. This implied that the relationship between technological innovation and management efficiency is inverted U-shaped non-linear, with management efficiency increasing up to a point, beyond which higher levels of R&D and patent registration activities led to a decrease in management efficiency. Conclusion: This study empirically assessed the inconclusive findings of previous research in the area of effects of innovation activities in relation to firm performance. The paper also provided theoretical and practical implications for firms who explore efficient strategy to promote the management performance through technological innovation activities. Future research directions with the limitation of the study was discussed.

• 통합자연과학논문집
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• 제5권1호
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• pp.1-5
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• 2012

Quantitative structure-activity relationship (QSAR) methodologies have been applied for many years, to correlate the relationship between physicochemical properties of chemical substances and their biological activities to generate a statistical model for prediction of the activities of new chemical entities. The basic principle behind the QSAR models is that, how structural variation is responsible for the difference in biological activities of the compounds. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which develops the 3D properties of the ligands to predict their biological activities using various chemometric techniques (PLS, G/PLS, ANN etc). It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. This review seeks to provide different 3D-QSAR approaches involved in drug designing process to develop structure-activity relationships and also discussed the fundamental limitations, as well as those that might be overcome with the improved methodologies.

• 통합자연과학논문집
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• 제8권3호
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• pp.214-220
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• 2015

Cancer has emerged as one of the leading cause of deaths worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of quinazoline-based anticancer agents. Purpose of the study is to understand the structural basis for their inhibitory activity. Comparative molecular field analysis (CoMFA) technique was employed to develop 3D-QSAR model. Ligand-based alignment scheme was used to generate a reliable CoMFA model. The model produced statistically significant results with a cross-validated correlation coefficient ($q^2$) of 0.589 and a non-cross-validated correlation coefficient ($r^2$) of 0.928. Model was further validated by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel and more potent anticancer agents.

• Nutrition Research and Practice
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• 제3권4호
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• pp.286-294
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• 2009

This study aimed to identify risk factors for type 2 diabetes (T2D) in Korea, a rapidly changing country. Data of 5,132 adults aged 20-85 were used from the 2001 Korean Health and Nutrition Examination Survey. Multiple logistic regression was carried out to identify risk factors for T2D. Three models were specified: (i) socioeconomic and demographic factors (model 1: age, gender, education, poverty income ratio, employment), (ii) behavioral risk factors and covariates (model 2: obesity, physical activity, smoking, alcohol drinking, dietary quality, family history of T2D, co-morbidity) and (iii) socioeconomic, demographic, and behavioral factors (model 3). The prevalence of T2D was 7.4%. Less education (OR 1.41, 95% CI 1.08-1.84), age (OR 2.19, 95% CI 1.56-3.08 in 40-59 yrs, OR 4.05, 95% CI 2.76-5.95 in 60 yrs + comparing to 20-39 yrs) and abdominal obesity (OR 2.24, 95% CI 1.79-2.82) were risk factors for T2D even after controlling for other factors simultaneously. There was a significant association of T2D with ever smoking (OR 1.34, 95% CI 1.06-1.67). The relationship of age with T2D was modified by gender in model 1 and the relationship of smoking with T2D was modified by obesity in model 2. Less educated, older, obese or ever smokers were more likely to have T2D. Gender mediated the relationship of age, and obesity mediated the relationship of smoking, with T2D. Intervention programs for T2D in Korea should take the interactions among risk factors into account.

• Bulletin of the Korean Chemical Society
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• 제30권11호
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

• 통합자연과학논문집
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• 제9권1호
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• pp.1-9
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• 2016

Leucine rich repeat kinase 2 (LRRK2) is a highly promising target for Parkinson's disease (PD) that affects millions of people worldwide. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of pyrrolopyrimidine-based selective LRRK2 kinase inhibitors. This study was performed to rationalize the structural requirements responsible for the inhibitory activity of these compounds. A reliable 3D-QSAR model was developed using comparative molecular field analysis (CoMFA) technique. The model produced statistically acceptable results with a cross-validated correlation coefficient ($q^2$) of 0.539 and a non-cross-validated correlation coefficient ($r^2$) of 0.871. Robustness of the model was further evaluated by bootstrapping and progressive scrambling analysis. This work could assist in designing more potent LRRK2 inhibitors.

• Bulletin of the Korean Chemical Society
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• 제25권10호
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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.

• 통합자연과학논문집
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• 제8권1호
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• pp.40-47
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• 2015

Glycogen synthase kinase 3 (GSK-3) is a serine/threonine protein kinase that has recently emerged as a promising target in drug discovery. It is involved in multiple cellular processes and associated with the pathogenesis of several diseases. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of GSK-3 inhibitors to understand the structural basis for inhibitory activity. Comparative molecular field analysis (CoMFA) method was used to derive 3D-QSAR models. A reliable CoMFA model was developed using ligand-based alignment scheme. The model produced statistically acceptable results with a cross-validated correlation coefficient ($q^2$) of 0.594 and a non-cross-validated correlation coefficient ($r^2$) of 0.943. Robustness of the model was checked by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel compounds with enhanced GSK-3 inhibitory activity.

• Bulletin of the Korean Chemical Society
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• 제34권6호
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• pp.1823-1828
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• 2013

Tetraoxanes (1,2,4,5-tetraoxanes) have been reported to exhibit potent antimalarial activity. In the present study, the three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of tetraoxanes derivatives using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The best predictive CoMFA model with atom fit alignment resulted in cross-validated coefficient ($q^2$) value of 0.719, non-cross-validated coefficient ($r^2$) value of 0.855 with standard error of estimate (SEE) 0.335. Similarly, the best predictive CoMSIA model was derived with $q^2$ of 0.739, $r^2$ of 0.847 and SEE of 0.344. The generated models were externally validated using test sets. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel tetraoxanes having improved antimalarial activity.