• Applied Biological Chemistry
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• v.46 no.3
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• pp.155-164
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• 2003

It was reviewed for the status of domestic research before and after 1990's for search of a new pesticides using 2D QSAR of quantitative structure-activity relationship (QSAR) methodologies (Sung, Nack-Do (2002) Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodology. Kor J. Pestic. Sci. 6, 166-174, 231-243 & 7, 1-11) which was proposed according to Hansch-Fujita equation based on the concept of biological Hammett equation.

• The Korean Journal of Pesticide Science
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• v.6 no.3
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• pp.166-174
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• 2002

The fundamental concepts on the basis of linear free energy relationship (LFER), history of development, prediction of pharmacological effects, advantages and disadvantages, etc. according to the 2D and 3D QSAR methodologies were summarized in utilizing the quantitative structure-activity relation ship (QSAR) techniques for searching and development of new agrochemicals. Objectives, role of QSAR techniques in development process of pesticides and limitations in QSARs were discussed and introduced.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• The Korean Journal of Pesticide Science
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• v.7 no.1
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• pp.1-11
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• 2003

Acoording to improvement of HTOS (high throughput organic synthesis) and HTS (high throughput screening) technique, the CoMFA (comparative molecular field analysis), CoMSIA (comparative molecular similarity indeces analysis) and molecular HQSAR (hologram quantitative structure-activity relationship) analysis techniques as methodology of computer assisted molecular design (CAMD) were introduced generally and summarized for some application cases.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.197-201
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• 2011

3D-QSAR techniques including CoMFA have been used a lot for more than two decades now. For now, the perspective of 3D-QSAR has been changed. The realization of gorge activity cliffs and higher chance correlation with many independent variables (IVs) has changed the requirements. Some suggested the benchmarking datasets for 3D-QSAR. However, were they still useful for right reasons? Here, we propose the requirement of any general purpose 3D-QSAR benchmarking datasets for lead optimization, especially for feasibility test of any IVs. Specifically, we summarize the conceptual requirements for an ideal settings for 3D-QSAR especially CoMFA.

• The Korean Journal of Pesticide Science
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• v.12 no.1
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• pp.9-17
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• 2008

QSARs (Quantitative structure-activity relationships) between a series of new cyclohexanedione derivatives (5-benzofuryl-2-[1-(alkoxyimino)-alkyl]-3-hydroxycyclohex-2-en-1-ones) and their herbicidal activity against Rice plant (Oryza sativa L.) and Barnyard grass (Echinochloa crus-galli.) were discussed quantitatively using 2D-QSAR and holographic (H) QSAR methods. Generally, the HQSAR models have better predictability and fitness than the 2D-QSAR models. The herbicidal activities against Barnyard grass with 2D-QSAR II model were dependent upon Balaban indice (BI) of molecule and hydrophobicity of $R_1$ and $R_3$ group. And also, the $R_3=ethyl$ group, according to the information of the optimized HQSAR IV model, was more contribute to the herbicidal activities against Rice plant, while the 5-(cyclohex-3-enyl)-2,3-dihydrobenzofuran ring part was not contribute to the herbicidal activities against two plants.

• Journal of Environmental Health Sciences
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• v.38 no.6
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• pp.550-560
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• 2012

Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

• YAKHAK HOEJI
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• v.58 no.1
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• pp.53-57
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• 2014

In recent, the abuse of newly-emerging psychoactive drugs, ('designer drugs') is a rapidly increasing problem in Korean society. Quantitative-structure activity relationship (QSAR) is an alternative method to predict bioactivities of new abused compounds. In this study, cathinone-related new designer drugs, 4-methylbuphedrone and 4-methoxy-N,N-dimethylcathinone were tested for prediction of the bioactivity with QSAR model. The bioactivity of 4-methylbuphedrone and 4-methoxy-N,N-dimethylcathinone was similar to those of methylone. These results suggest that the prediction with QSAR model may provide scientific evidences for regulatory decision.

• Journal of Environmental Science International
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• v.11 no.7
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• pp.737-742
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• 2002

Sorption and desorption is an important phenomenon to determine the fate of halogenated aliphatic hydrocarbons in the aqueous phase. This study was conducted to develope a predictive equation capable of estimating the sorption and desorption potentials of halogenated aliphatic hydrocarbons onto the sludge from activated process, sediment, and clay. It has shown that the sorption and desorption parameters can be accurately estimated using Quantitative Structural Activity Relationship(QSAR) models based on molecular connectivity indexes of test compounds. The QSAR model could be applied to predict the sorption and desorption capacity of the other halogenated aliphatic hydrocarbons. The QSAR modeling would provide a useful tool to predict the sorption and desorption capacity without time-consuming experiments.

• Journal of Integrative Natural Science
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• v.5 no.1
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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.