• 월간산업보건
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• s.311
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• pp.61-63
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• 2014

• Bulletin of the Korean Chemical Society
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• v.22 no.7
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• pp.775-778
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• 2001

• Environmental Analysis Health and Toxicology
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• v.14 no.1_2
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• pp.65-73
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• 1999

Dioxins refer to a family of chemicals comprising 75 polychlorinated dibenzo-p-dioxin (PCDD) and 135 polychlorinated dibenzo-p-furan (PCDF) congeners, which may cause skin disorder, human immune system disruption, birth defects, severe hormonal imbalance, and cancer. The effects of exposure of dioxin-like compounds such as PCBs are mediated by binding to the aryl hydrocarbon receptor (AHR), which is a ligand-activated transcription factor. To grasp physicochemical factors affecting human toxicity of dioxins, six geometrical and topological indices, eleven thermodynamic variables, and quantum mechanical descriptors including ESP (electrostatic potential) were analyzed using QSAR and semi-empirical AM1 method. Planar dioxins with high lipophilicity and large surface tension show the probability that negative electrostatic potential in the lateral oxygen may make hydrogen bonding with DNA bases to be a carcinogen.

• Molecular & Cellular Toxicology
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• v.4 no.2
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• pp.170-176
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• 2008

The quantitative structure-activity relationship (QSAR) of a set of 35 flavonoid compounds presenting antioxidant activity was established by means of Genetic Algorithm-Multiple Linear Regression (GA-MLR) technique. Four-parametric models for two sets of data, the 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity $(R^2=0.788,\;Q^2_{cv}=0.699\;and\;Q^2_{ext}=0.577)$ and scavenging activity of reactive oxgen species (ROS) induced by $H_2O_2 (R^=0.829,\;Q^2_{cv}=0.754\;and\;Q^2_{ext}=0.573)$ were obtained with low external predictive ability on a mass basis, respectively. Each model gave some different mechanistic aspects of the flavonoid compounds tested in terms of the radical scavenging activity. Topological charge, H-bonding complex and deprotonation processes were likely to be involved in the radical scavenging activity.

• Bulletin of the Korean Chemical Society
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• v.32 no.4
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• pp.1241-1248
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• 2011

Three dimensional quantitative structure-activity relationships (3D-QSARs) between new thiosemicarbazone analogues (1-31) as a substrate molecule and their inhibitory activity against tyrosinase as a receptor were performed and discussed quantitatively using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods. According to the optimized CoMSIA 2 model obtained from the above procedure, inhibitory activities were mainly dependent upon H-bond acceptor favored field (36.5%) of substrate molecules. The optimized CoMSIA 2 model, with the sensitivity of the perturbation and the prediction, produced by a progressive scrambling analysis was not dependent on chance correlation. From molecular docking studies, it is supposed that the inhibitory activation of the substrate molecules against tyrosinase (PDB code: 1WX2) would not take place via uncompetitive inhibition forming a chelate between copper atoms in the active site of tyrosinase and thiosemicarbazone moieties of the substrate molecules, but via competitive inhibition based on H-bonding.

• Interdisciplinary Bio Central
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• v.2 no.2
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• pp.6.1-6.5
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• 2010

Fragment molecular orbital (FMO) method provides a novel tool for ab initio calculations of large biomolecules. This method overcomes the size limitation difficulties in conventional molecular orbital methods and has several advantages compared to classical force field approaches. While there are many features in this method, we here focus on explaining the issues related to protein-ligand binding: FMO method provides useful interaction-analysis tools such as IFIE, CAFI and FILM. FMO calculations can provide not only binding energies, which are well correlated with experimental binding affinity, but also QSAR descriptors. In addition, FMO-derived charges improve the descriptions of electrostatic properties and the correlations between docking scores and experimental binding affinities. These calculations can be performed by the ABINIT-MPX program and the calculation results can be visualized by its proper BioStation Viewer. The acceleration of FMO calculations on various computer facilities is ongoing, and we are also developing methods to deal with cytochrome P450, which belongs to the family of drug metabolic enzymes.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.247.2-248
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• 2003

The three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) was applied to 62 derivatives known as COX-2 selective inhibitors. Partial least square (PLS) analyses produced good predicted models with q2 value of 0.803 (s=0.285, F=215.401, r2=0.951) and 0.769 (s=0.192, F=245.364, r2=0.980) for CoMFA and CoMSIA, respectively. (omitted)

• Journal of Photoscience
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• v.8 no.3_4
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• pp.119-121
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• 2001

Three-Dimensional Quantitative Structure-Activity Relationship(QSAR) has been investigated over 67 flavonoids to correlate and predict GI$\sub$50/ values. The partial least-squares(PLS) model was performed to calculate the activity of each derivatives, and this was compared with the actual value. The results of the cross-validated(${\gamma}$$^2$=0.997) values show that cytotoxic activities play an important role which is in good agreement with the observed GI$\sub$50/ values.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Genomics & Informatics
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• v.5 no.1
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• pp.24-29
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• 2007

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.