• The Korean Journal of Physiology and Pharmacology
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• v.15 no.6
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• pp.423-429
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• 2011

B13 analogues are being considered as therapeutic agents for cancer cells, since B13 is a ceramide analogue and inhibits ceramidase to promote apoptosis in cancer cells. B13 sulfonamides are assumed to have biological activity similar to B13, since they are made by bioisosterically substituting the carboxyl moiety of B13 with sulfone group. Twenty B13 sulfonamides were evaluated for their in vitro cytotoxicities against human colon cancer HT-29 and lung cancer A549 cell lines using MTT assays. Replacement of the amide group with a sulfonamide group increased cytotoxicity in both cancer cell lines. The sulfonamides with long alkyl chains exhibited activities two to three times more potent than that of B13 and compound (15) had the most potent activity with $IC_{50}$ values of 27 and $28.7{\mu}M$ for HT-29 and A549, respectively. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to carry out QSAR molecular modeling of these compounds. The predictive CoMSIA models for HT-29 and A549 gave cross-validated q2 values of 0.703 and 0.830, respectively. From graphical analysis of these models, we suppose that the stereochemistry of 1,3-propandiol is not important for activity and that introduction of a sulfonamide group and long alkyl chains into B13 can increase cytotoxicity.

• Journal of Integrative Natural Science
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• v.8 no.4
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• pp.258-266
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• 2015

Xanthine Oxidase is an enzyme, which oxidizes hypoxanthine to xanthine, and xanthine to uric acid. It is widely distributed throughout various organs including the liver, gut, lung, kidney, heart, brain and plasma. It is involved in gout pathogenesis. In this study, we have performed Comparative Molecular Field Analysis (CoMFA) on a series of 2-(indol-5-yl) thiazole derivatives as xanthine oxidase (XO) inhibitors to identify the structural variations with their inhibitory activities. Ligand based CoMFA models were generated based on atom-by-atom matching alignment. In atom-by-atom matching, the bioactive conformation of highly active molecule 11 was generated using systematic search. Compounds were aligned using the bioactive conformation and it is used for model generation. Different CoMFA models were generated using different alignments and the best model yielded a cross-validated $q^2$ of 0.698 with five components and non-cross-validated correlation coefficient ($r^2$) of 0.992 with Fisher value as 236.431, and an estimated standard error of 0.068. The predictive ability of the best CoMFA models was found to be $r^2_{pred}$0.653. The CoMFA study revealed that the $R_3$ position of the structure is important in influencing the biological activity of the inhibitors. Electro positive groups and bulkier substituents in this position enhance the biological activity.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.899-906
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• 2013

Using generated conformations from docking analysis by Gold algorithm, some 3D-QSAR models; CoMFA and CoMSIA have been created on 39 N-benzoylated phenoxazines and phenothiazines, including their S-oxidized analogues. These molecules inhibit the polymerization of tubulin into microtubules and thus they have been studied for the development of antitumor drugs. Training set for the CoMFA and CoMSIA models using 30 docked conformations gives $q^2$ Leave one out (LOO) values of 0.756 and 0.617, and $r^2$ ncv values of 0.988 and 0.956, respectively. The ability of prediction and robustness of the models were evaluated by test set, cross validation (leave-one-out and leave-ten-out), bootstrapping, and progressive scrambling approaches. The all-orientation search (AOS) was used to achieve the best orientation to minimize the effect of initial orientation of the structures. The docking results confirmed CoMFA and CoMSIA contour maps. The docking and 3D-QSAR studies were thoroughly interpreted and discussed and confirmed the experimental $pIC_{50}$ values.

• Bulletin of the Korean Chemical Society
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• v.32 no.2
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• pp.645-650
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• 2011

The 3D-QSAR study of 2-arylbenzoxazoles as novel cholesteryl ester transfer protein inhibitors was performed by comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RF) for optimizing the region for the final PLS analysis, and comparative molecular similarity indices analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The best orientation was searched by all-orientation search strategy using AOS, to minimize the effect of the initial orientation of the structures. The predictive ability of CoMFARF and CoMSIA were determined using a test set of twelve compounds giving predictive correlation coefficients of 0.886, and 0.754 respectively indicating good predictive power. Further, the robustness and sensitivity to chance correlation of the models were verified by bootstrapping and progressive scrambling analyses respectively. Based upon the information derived from CoMFA(RF) and CoMSIA, identified some key features that may be used to design new inhibitors for cholesteryl ester transfer protein.

• Bulletin of the Korean Chemical Society
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• v.35 no.7
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• pp.2065-2071
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• 2014

${\beta}$-Secretase (beta-amyloid converting enzyme 1 [BACE1]) is involved in the first and rate-limiting step of ${\beta}$-amyloid ($A{\beta}$) peptides production, which leads to the pathogenesis of Alzheimer's disease(AD). Therefore, inhibition of BACE1 activity has become an efficient approach for the treatment of AD. Ligand-based and docking-based 3D-quantitative structure-activity relationship (3D-QSAR) studies of acyl guanidine analogues were performed with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to obtain insights for designing novel potent BACE1 inhibitors. We obtained highly reliable and predictive CoMSIA models with a cross-validated $q^2$ value of 0.725 and a predictive coefficient $r{^2}_{pred}$ value of 0.956. CoMSIA contour maps showed the structural requirements for potent activity. 3D-QSAR analysis suggested that an acyl guanidine and an amide group in the $R_6$ substituent would be important moieties for potent activity. Moreover, the introduction of small hydrophobic groups in the phenyl ring and hydrogen bond donor groups in 3,5-dichlorophenyl ring could increase biological activity.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.656-662
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• 2008

Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.

• The Korean Journal of Physiology and Pharmacology
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• v.17 no.3
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• pp.237-243
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• 2013

B13 is a ceramide analogue and apoptosis inducer with potent cytotoxic activity. A series of arylpropyl sulfonamide analogues of B13 were evaluated for their cytotoxicity using MTT assays in prostate cancer PC-3 and leukemia HL-60 cell lines. Some compounds (4, 9, 13, 14, 15, and 20) showed stronger activities than B13 in both tumor cell lines, and compound (15) gave the most potent activity with $IC_{50}$ values of 29.2 and 20.7 ${\mu}M$, for PC-3and HL-60 cells, respectively. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed to build highly reliable and predictive CoMSIA models with cross-validated $q^2$ values of 0.816 and 0.702, respectively. Our results suggest that long alkyl chains and a 1R, 2R configuration of the propyl group are important for the cytotoxic activities of arylpropyl sulfonamides. Moreover, the introduction of small hydrophobic groups in the phenyl ring and sulfonamide group could increase biological activity.

• Journal of the Korean Chemical Society
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• v.60 no.4
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• pp.225-234
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• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2523-2531
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• 2009

Effects of some diacid, diamine and dinitro aromatic compounds on the structure and activity of adenosine deaminase (ADA) were investigated by UV-Vis spectrophotometry in 50 mM phosphate buffer at pH = 7.5 and 27 ${^{\circ}C}$ and molecular docking studies. The results showed that all tested ligands are showing inhibition; five ligands are uncompetitive and other two ligands are mixed of competitive and noncompetetive inhibitors with majority of competitive behavior. For the later case analysis was done based on competitive inhibition. Diacids have larger size and higher inhibition constant ($K_I$) relative to others. A logical correlation between calculated free energy of binding and experimental values was obtained for un-competitive. Experimental and calculated data showed that competitive inhibitors are distributed near the active site of enzyme and form several cluster of ranks, whereas uncompetitive inhibitors bind to the enzyme-substrate complex and distributed far from the active site. Results of structure-activity relationship showed that, larger, more hydrophobe, less spherical and more aromatic ligands have higher inhibition constants.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.45-49
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• 2014

The (c-Jun N-terminal kinase 3) JNK3 is a potential therapeutic target for various neurological disorders. Here, a three dimensional quantitative structure-activity relationship (3D-QSAR) study on phenoxypyridine as JNK3 inhibitors was performed to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The comparative molecular field analysis (CoMFA) using different partial atomic charges, was employed to understand the structural factors affecting JNK3 inhibitory potency. The Gasteiger-Marsili yielded a CoMFA model with cross-validated correlation coefficient ($q^2$) of 0.54 and non-cross-validated correlation coefficient ($r^2$) of 0.93 with five components. Furthermore, contour maps suggested that bulky substitution with oxygen atom in $R^3$ position could enhance the activity considerably. The work suggests that further chemical modifications of the compounds could lead to enhanced activity and could assist in the design of novel JNK3 inhibitors.