• Journal of the Society of Cosmetic Scientists of Korea
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• v.40 no.4
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• pp.383-390
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• 2014

The following conclusion was made from the 2D-QSAR model for the tyrosinase inhibitory activity according to the variation of the substituents R1 and R2 in analogues of compound 2-[(2,6-dioxocyclohexyl)methyl]cyclohexane- 1,3-dione (1-23). The best optimized 2D-QSAR model was $Obs.pI_{50}=-0.295({\pm}0.031)TDM$ $-0.120({\pm}0.014)DMZ+0.135({\pm}0.050)DMX.R_2+6.382({\pm}0.17)$, and the correlation $r^2=0.905$) of which was greater than its predictability ($q^2=0.843$). The magnitude of the effect of tyrosinase inhibitory activities was in order of TDM > $DMX.R_2{\geq}DMZ$, and it tended to increase as the hydrophobicity of substrate molecule (ClogP > 0) as well as the steric favor of substituent $R_1$ increased. The analysis of the model implies that inhibitory activity of substrate molecule will increase as $DMX.R_2$ (Dipole moment X component of $R_2$-substituent) increases, while TDM (Total Dipole Moment) and DMZ(Dipole Moment of Z-Component) decrease. As such, it is deemed feasible to conclude, that in order to increase the inhibitory effect, it would be rather desirable to replace the polar groups within the molecules with non-polar functional groups.

• Journal of Integrative Natural Science
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• v.9 no.1
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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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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.91-98
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• 2011

Quantitative structure-activity relationships (QSAR) have been applied for two decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the QSAR is that the structural difference is responsible for the variations in biological activities of the compounds. In this work, we developed 3D-QSAR model for a series of 5-Lipoxygenase inhibitors, utilizing comparative molecular field analysis (CoMFA) and Topomer CoMFA methodologies. Our developed models addressed superiority of Topomer CoMFA over CoMFA. The CoMFA model was obtained with $q^2$=0.593, $r^2$=0.939, $Q^2$=0.334 with 6 optimum number of components (ONC). Higher statistical results were obtained with the Topomer CoMFA model ($q^2$=0.819, $r^2$=0.947, ONC=5). Further robustness of developed models was checked with the ANOVA test and it shows F=113 for CoMFA and F=162.4 for Topomer CoMFA model. Contour map analysis indicated that the more requirement of electrostatic parameter for improved potency.

• Journal of Integrative Natural Science
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• v.8 no.3
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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.

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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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.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.647-655
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• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• Bulletin of the Korean Chemical Society
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• v.33 no.1
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• pp.149-152
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• 2012

The (S)-(+)-decursin and its analogues are reported as potent inhibitors of melanin production in B16 murine melanoma cells. In order to understand the factors responsible for potency as well as inhibition of potency of (S)-(+)-decursin and its analogues, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Since receptor structures are not available, a pharmacophore model was constructed. Using PHASE, we generated 3 different models and selected the seven-site model, which returned excellent statistical values ($r^2$ = 0.9127, $Q^2$ = 0.6878, Pearson-R = 0.9014). Using the generated pharmacophore model, we screened a natural products library and obtained 4'-epi-decursin as the most related compound. 4'-epidecursin is similar to (S)-(+)-decursin, but shows additional interaction possibilities with tyrosinase. The study thus sheds some light on possibility of developing more potent tyrosinase inhibitors.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.216-221
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• 2011

c-Jun N-terminal kinase-3 (JNK-3) has been shown to mediate neuronal apoptosis and make the promising therapeutic target for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and other CNS disorders. In order to better understand the structural and chemical features of JNK-3, comparative molecular field analysis (CoMFA) was performed on a series of 3,5-disubstituted quinolines derivatives. The best predictions were obtained CoMFA model ($q^2$=0.707, $r^2$=0.972) and the statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The resulting contour map from 3D-QSAR models might be helpful to design novel and more potent JNK3 derivatives.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.