• Journal of Applied Biological Chemistry
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• v.53 no.4
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• pp.225-231
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• 2010

To understand the inhibitory activity with changing hydroxyl substituents ($R_l-R_9$) of polyhydroxy substituted 2-phenyl-l,4-benzopyrone analogues (1-25) against tyrosinase (PDB ID: oxy-form; 1WX2), molecular docking and the three dimensional quantitative structure-activity relationships (3D-QSARs: Comparative molecular field analysis (CoMFA) & Comparative molecular similarity indices analysis (CoMSIA)) were studied quantitatively. The statistically best models were CoMFA 1 and CoMSIA 1 model from the results. The optimized CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced ($dq^2'/dr_{yy'}^2$=1.009 & $q^2$=0.51l) by a progressive scrambling analysis were not dependent on chance correlation. The inhibitory activities with optimized CoMSIA 1 model were dependent upon electrostatic factor (51.4%) of substrate molecules. Contour mapping the 3D-QSAR models to the active site of tyrosinase provides new insight into the interaction between tyrosinase as receptor and 2-phenyl-l,4-benzopyrone analogues as inhibitor. Therefore, the results will he able to apply to the optimization of a new potent tyrosinase inhibitors.

• The Korean Journal of Pesticide Science
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• v.10 no.1
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• pp.7-14
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• 2006

Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the herbicidal activities against in-vitro pre-emergence rice plant (Oryza sativa L.) and barnyard grass (Echinochloa crus-galli) by new 5-benzofuryl-2-[1-(alkoxyimino)alkyl]-3-hydroxycyclohex-2-en-1-one derivatives were studied quantitatively using comparative molecular similarity indices analysis (CoMSIA) methodology. The optimized CoMSIA model(A5: $r^2_{cv.}=0.569$ & $r^2_{ncv.}=0.941$) for rice plant exhibited a good correlation with steric (31.6%) and hydrophobic (39.7%) factors of the substrate molecules, and the model (B4: $r^2_{cv.}=0.569$ & $r^2_{ncv.}=0.941$) for barnyardgrass exhibited a good correlation with electrostatic (46.7%) and H-bond acceptor field (30.8%), respectively. The predicted $R_1=SF_5,\;R_2=R_3=R_4=H(P1)$ substituent (Rice plant: $pI_{50}=4.84$ & Barnyardgrass: $pI_{50}=7.21$, ${\Delta}pI_{50}=2.37$) by the model (B4) not only exhibited to the highest herbicidal activity against barnyardgrass, but also exhibited to the highest selecticity between two plants.

• The Korean Journal of Pesticide Science
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• v.10 no.3
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• pp.157-164
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• 2006

Three dimensional quantitative structure-activity relationships (3D-QSARs) for the fungicidal activities against Rhizoctonia solani (RS) and Phytophthora capsici (PC) by N-phenyl substituents(X) of N-phenylthionocarbamate derivatives were studied quantitatively using comparative molecular field analysis (CoMFA) methodology based on different alignment approaches. Statistical quality of CoMFA models with field fit alignment were slightly higher than that of atom based fit alignment. The optimized CoMFA models (RS: RF2 & PC: PF2) were derived from field fit alignment and combination of CoMFA fields. And the statistical results of the two models showed the best predictability of the fungicidal activities based on the cross-validated value $q^2$ ($r^2_{cv.}$ =RS: 0.557 & PC: 0.676) and non-cross-validated value ($r^2_{ncv.}$ =RS: 0.954 & PC: 0.968), respectively. The selective fungicidal activities between two fungi were dependence upon the electrostatic field of substrate molecule. Therefore, the fungicidal activities from CoMFA contour maps showed that the fungicidal activity will be able to increased according to the modification of X-substituents on the substrate molecules.

• Environmental Analysis Health and Toxicology
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• v.19 no.1
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• pp.33-40
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• 2004

Benzoyl peroxide is a High Production Volume Chemical, which is produced about 1,371 tons/year in Korea as of 2001 survey. The substance is mainly used as initiators in polymerization, catalysts in the plastics industry, bleaching agents for flour and medication for acne vulgaris. In this study, Quantitative Structure-Activity Relationships (QSAR) are used for getting adequate information on the physical -chemical properties of this chemical. And hydrolysis in water, acute toxicity to aquatic and terrestrial organisms for benzoyl peroxide were studied. The physical -chemical properties of benzoyl peroxide were estimated as followed; vapor pressure=0.00929 Pa, Log $K_{ow}$ = 3.43, Henry's Law constant=3.54${\times}$10$^{-6}$ atm-㎥/mole at $25^{\circ}C$, the half-life of photodegradation=3 days and bioconcentration factor (BCF)=92. Hydrolysis half-life of benzoyl peroxide in water was 5.2 hr at pH 7 at $25^{\circ}C$ and according to the structure of this substance hydrolysis product was expected to benzoic acid. Benzoyl peroxide has toxic effects on the aquatic organisms. 72 hr-Er $C_{50}$ (growth rate) for algae was 0.44 mg/1.,48 hr-E $C_{50}$ for daphnia was 0.07mg/L and the 96hr-L $C_{50}$ of acute toxicity to fish was 0.24mg/L. Acute toxicity to terrestrial organisms (earth worm) of benzoyl peroxide was low (14 day-L $C_{50}$ = > 1,000 mg/kg). Although benzoyl peroxide is high toxic to aquatic organisms, the substance if not bioaccumulated because of the rapid removal by hydrolysis (half-life=5.2 hr at pH 7 at $25^{\circ}C$) and biodegradation (83% by BOD after 21 days). The toxicity observed is assumed to be due to benzoyl peroxide rather than benzoic acid, which shows much lower toxicity to aquatic organisms. One can assume that effects occur before hydrolysis takes place. From the acute toxicity value of algae, daphnia and fish, an assessment factor of 100 was used to determine the predicted no effect concentration (PNEC). The PNEC was calculated to be 0.7$\mu\textrm{g}$/L based on the 48 hr-E $C_{50}$ daphnia (0.07 mg/L). The substance shows high acute toxicity to aquatic organisms and some information indicates wide-dispersive ore of this substance. So this substance is, a candidate for further work, even if it hydrolysis rapidly and has a low bioaccumulation potential. This could lead to local concern for the aquatic environment and therefore environmental exposure assessment is recommended.

• The Korean Journal of Pesticide Science
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• v.14 no.4
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• pp.319-325
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• 2010

To understand the influences of the substituents ($R_1{\sim}R_4$) on insecticidal activity of N'-phenyl-N-methylformamidine analogues (1~22) against two spotted spider mite (Tetranychus urticae), comparative molecular field analysis (CoMFA) model and comparative molecular similarity indices analysis (CoMSIA) model as three dimensional quantitative structure-activity relationships (3D-QSARs) model were derived and discussed quantitatively. From the results, the correlativity and predictability ($r^2{_{cv.}}=0.575$ and $r^2{_{ncv.}}=0.945$) of the CoMFA 1 model were higher than those of the rest models. The the CoMFA 1 and CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced ($d_q{^{2'}}/dr^2{_{yy}}=1.071{\sim}1.146$ & $q^2=0.545{\sim}0.626$) by a progressive scrambling analysis were not dependent on chance correlation. The insecticidal activities from the optimized CoMFA 1 model were depend upon the steric field (62.5%), electrostatic field (28.9%), and hydrophobic field (8.6%) of N'-phenyl-N-methylformamidine analogues. Therefore, the inhibitory activities with optimized CoMFA 1 model were dependent upon steric factor. From the contour maps of the optimized models, it is predicted that the structural distinctions that contribute to the insecticidal activity will be able to applied new potent insecticides design.

• Analytical Science and Technology
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• v.24 no.6
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• pp.544-555
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• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• The Korean Journal of Pesticide Science
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• v.8 no.3
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• pp.151-161
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• 2004

Three dimensional quantitative structure-activity relationships (3D-QSAR) studies for the protox inhibition activities against root and shoot of rice plant (Orysa sativa L.) and barnyardgrass (Echinochloa crus-galli) by a series of new A=3,4,5,6-tetrahydrophthalimino, B=3-chloro-4,5,6,7-tetrahydro-2H-indazolyl and C=3,4-dimethylmaleimino group, and R-group substituted on the phenyl ring in 1-(5-methyl-3-phenylisoxazolin-5-yl)methoxy-2chloro-4-fluorobenzene derivatives were performed using comparative molecular field analyses (CoMFA) methodology with Gasteiger-Huckel charge. Four CoMFA models for the protox inhibition activities against root and shoot of the two plants were generated using 46 molecules as training set and the predictive ability of the each models was evaluated against a test set of 8 molecules. And the statistical results of these models with combination (SIH) of standard field, indicator field and H-bond field showed the best predictability of the protox inhibition activities based on the cross-validated value $r^2_{cv.}$ $(q^2=0.635\sim0.924)$, conventional coefficient $(r^2_{ncv.}=0.928\sim0.977)$ and PRESS value $(0.091\sim0.156)$, respectively. The activities exhibited a strong correlation with steric $(74.3\sim87.4%)$, electrostatic $(10.10\sim18.5%)$ and hydrophobic $(1.10\sim8.30%)$ factors of the molecules. The steric feature of molecule may be an important factor for the activities. We founded that an novel selective and higher protox inhibitors between the two plants may be designed by modification of X-subsitutents for barnyardgrass based upon the results obtained from CoMFA analyses.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.