• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2433-2442
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• 2011

The three dimensional quantitative structure activity relationship (3D QSAR) models were developed using Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and docking studies. The fit of Quinazolinone antifolates inside the active site of modeled bovine dihydrofolate reductase (DHFR) was assessed. Both ligand based (LB) and receptor based (RB) QSAR models were generated, these models showed good internal and external statistical reliability that is evident from the $q^2_{loo}$, $r^2_{ncv}$ and $r^2_{pred}$. The identified key features enabled us to design new Quinazolinone analogues as DHFR inhibitors. This study is a building bridge between docking studies of homology modeled bovine DHFR protein as well as ligand and target based 3D QSAR techniques of CoMFA and CoMSIA approaches.

• Bulletin of the Korean Chemical Society
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• v.25 no.12
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• pp.1801-1806
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• 2004

Ligand-based quantitative structure-activity relationship (QSAR) studies were performed on indolinones derivatives as a potential inhibitor of the protein tyrosine kinase of fibroblast growth factor receptor (FGFR) by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) implemented in the SYBYL packages. The initial X-ray structure of docked ligand (Su5402) to FGFR was used to minimize the 27 training set molecules using TRIPOS force field. Seven models were generated using CoMFA and CoMSIA with grid spacing 2 ${\AA}$. After the PLS analysis the best predicted CoMSIA model with hydrophobicity, hydrogen bond donor and acceptor property showed that a leave-one out(LOO) cross validated value $({r^2}_{cv})^$ and non-cross validated conventional value $({r^2}_{ncv})^$ are 0.543 and 0.938, respectively.

• Reproductive and Developmental Biology
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• v.32 no.3
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• pp.141-146
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• 2008

To search a new porcine pheromonal odorant, the models of four type (2D-QSAR, HQSAR, CoMFA & CoMSlA) were derived from quantitative structure-activity relationship (QSAR) between tetrahydrofuran-2-yl family compounds and their observed binding affinity constants (Obs.p$[Od]_{50}$). The optimized CoMFA model (predictability; $r^{2}_{cv.}(q^2)$=0.886 & correlation coefficient: $r^{2}_{ncv.}$=0.984) from ligand based approaches was confirmed as the best model among them. The $N^{1}$-allyl-$N^{2}$-(tetrahydrofuran-2-yl)methyl)oxalamide (P1), 2-(4-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P5) and 2-(3-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P6) molecules predicted as porcine pheromonal odorant by the CoMFA model were showed relatively high binding affinity constant values (Pred.p$[Od]_{50}=8{\sim}10$) and very lower toxicity values against some sorts of toxicity.

• 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.

• Journal of Integrative Natural Science
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• v.3 no.3
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• pp.162-167
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• 2010

A pseudoreceptor combines structure-based and ligand-based techniques to represent a unifying concept for both receptor mapping and ligand matching. In this molecular modeling approach, there are opportunities to construct the pseudoreceptor models using a set of small molecules. To build a reliable pseudoreceptor model, we need a set of ligand molecules with known affinity (biological activity) to generate 3D bioactive conformation for each of these ligand molecules. Several software packages are available to generate a pseudoreceptor model and this can provide an entry point for structure based drug discovery in cases where receptor structure information is not available. In this review, we presented the concept of pseudoreceptor, as well as discussed about various software packages available to generate a pseudoreceptor model.

• Journal of Integrative Natural Science
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• v.4 no.4
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• pp.266-272
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• 2011

Focal adhesion kinase (FAK) is a potential target for the treatment of primary cancers as well as prevention of tumor metastasis. To understand the structural and chemical features of FAK inhibitors, we report comparative molecular field analysis (CoMFA) for the series of 7H-pyrrolo(2,3-d)pyrimidines. The CoMFA models showed good correlation between the actual and predicted values for training set molecules. Our results indicated the ligand-based alignment has produced better statistical results for CoMFA ($q^2$ = 0.505, $r^2$ = 0.950). Both models were validated using test set compounds, and gave good predictive values of 0.537. The statistical parameters from the generated 3D-QSAR models were indicated that the data are well fitted and have high predictive ability. The contour map from 3D-QSAR models explains nicely the structure-activity relationships of FAK inhibitors and our results would give proper guidelines to further enhance the activity of novel inhibitors.

• Journal of Integrative Natural Science
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• v.8 no.1
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• pp.40-47
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• 2015

Glycogen synthase kinase 3 (GSK-3) is a serine/threonine protein kinase that has recently emerged as a promising target in drug discovery. It is involved in multiple cellular processes and associated with the pathogenesis of several diseases. A three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed on a series of GSK-3 inhibitors to understand the structural basis for inhibitory activity. Comparative molecular field analysis (CoMFA) method was used to derive 3D-QSAR models. A reliable CoMFA model was developed using ligand-based alignment scheme. The model produced statistically acceptable results with a cross-validated correlation coefficient ($q^2$) of 0.594 and a non-cross-validated correlation coefficient ($r^2$) of 0.943. Robustness of the model was checked by bootstrapping and progressive scrambling analysis. This study could assist in the design of novel compounds with enhanced GSK-3 inhibitory activity.

• Bulletin of the Korean Chemical Society
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• v.28 no.2
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• pp.211-219
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• 2007

The subtle but significant differences and thereby the lack of consensus in active site structures among the crystal structures of cyclin-dependent kinase 2 (CDK2) has hampered structure-based drug design. In this study, we devised a simple but effective ‘mutation, pharmacophore-guided docking, followed by mutation' strategy to generate an “average” CDK2 structure, which was used for ligand docking study to successfully reproduce 30 out of 32 X-ray ligand positions within 2.0 A of heavy atom RMSD. This novel docking method was applied for structure-based 3D QSAR with CoMSIA study of a series of structurally related ligands, which showed a good discrimination between CDK2 binders and nonbinders.

• 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.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.