• 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.

• Bulletin of the Korean Chemical Society
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• v.27 no.11
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• pp.1741-1746
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• 2006

3D-QSARs on the neuroblocking activities by 1-(6-chloro-3-pyridylmethyl)-2-nitroiminoimidazolidine analogues as agonist at the nicotinic acetylcholine receptor (nAChR) were studied quantitatively using CoMFA and CoMSIA methodologies. The statistical results of CoMFA (A5: $r^2\;_{cv.}\;=\;0.707\;&\;r^2\;_{ncv.}$= 0.986) and CoMSIA model (A3: $r^2\;_{cv.}$ = 0.715 & $r^2\;_{ncv.}$ = 0.961) showed the best predictability and fitness for neuroblocking activity based on the cross-validated value and non-cross validated value. The steric and H-bond acceptor nature of a compound were essential for high activity. The study on 3D-QSARs between substrate molecules and their neuroblocking activities appears to be an useful approach for designing better neuroblocking drug development.

• Bulletin of the Korean Chemical Society
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• v.17 no.7
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• pp.653-655
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• 1996

A comparative molecular field analysis (CoMFA) was carried out for the correlation of the transition state structures and the reaction rates for the SN2 reaction of 4-nitrophenyl benzoate and its sulfur analogs with anionic nucleophiles. The CoMFA analysis showed that both steric and electrostatic effects are important, and the steric contribution increased when nucleophiles are alkoxides or arylsulfides. In this study, we have demonstrated that the CoMFA analysis can be expanded beyond the scope of dealing with reactants and products. The reactant complex and transition state conformations generated along the reaction path can be more appropriately used for the correlation of structures and reaction rates.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.248.2-248.2
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• 2003

A lectin has been purified from the bark of the legume Maackia fauriei. This lectin, MFA, was found to agglutinate human ABO erythrocytes at a titer of 256. The results from electrophoretic analyses, gel-filtration chromatography, and enzyme linked lectinsorbent assay indicate that MFA is an acidic glycoprotein, and exists as a tetramer of 30 kDa subunits that are linked by noncovalent bonds. The activity of MFA is critically dependent upon CaC1$_2$. MFA demonstrated high homogeneity with the lectins from M. amurensis, which is the only legume source of lectins that bind to sialoglycoconjugate, in its N-terminal amino acid sequence and amino acid composition, (omitted)

• Proceedings of the PSK Conference
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• 2000.10a
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• pp.257.1-257.1
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• 2000

• Bulletin of the Korean Chemical Society
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• v.32 no.5
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• pp.1604-1612
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• 2011

We developed three-dimensional quantitative structure activity relationship (3D-QASR) models for 17 adamantyl derivatives as P-glycoprotein (P-gp) inhibitors. Eighteen different partial charge calculation methods were tested to check the feasibility of the 3D-QSAR models. Best predictive comparative molecular field analysis (CoMFA) model was obtained with the Austin Model 1-Bond Charge Correction (AM1-BCC) atomic charge. The 3D-QSAR models were derived with CoMFA and comparative molecular similarity indices analysis (CoMSIA). The final CoMFA model ($q^2$ = 0.764, $r^2$ = 0.988) was calculated with an AM1-BCC charge and electrostatic parameter, whereas the CoMSIA model ($q^2$ = 0.655, $r^2$ = 0.964) was derived with an AM1-BCC charge and combined steric, electrostatic, hydrophobic and HB-acceptor parameters. Leave-five-out (LFO) cross-validation was also performed, which yielded good correlation coefficient for both CoMFA (0.801) and CoMSIA (0.656) models. Robustness of the developed models was checked further with 1000 run bootstrapping analyses, which gave an acceptable correlation coefficient for CoMFA (BS-$r^2$ = 0.997, BS-SD = 0.003) and CoMSIA (BS-$r^2$ = 0.996, BS-SD = 0.018).

• Bulletin of the Korean Chemical Society
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• v.31 no.10
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• pp.2761-2770
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• 2010

In this study, we have developed QSAR models for a series of 38 piperidine-4-carboxamide CCR5 antagonists using CoMFA, CoMSIA and HQSAR methods. Developed models showed good statistics in terms of $q^2$ and $r^2$ values. Best predictions obtained with standard CoMFA model ($r^2$ = 0.888, $q^2$ = 0.651) and combined CoMSIA model ($r^2$ = 0.892, $q^2$ = 0.665) with electrostatics and H-bond acceptor parameter. The validity of developed models was assessed by test set of 9 compounds, which showed good predictive correlation coefficient for CoMFA (0.804) and CoMSIA (0.844). Bootstrapped analysis showed statistically significant and robust CoMFA (0.968) and CoMSIA (0.936) models. Best HQSAR model was obtained with a $q^2$ of 0.662 and $r^2$ of 0.936 using atom, connection, hydrogen, donor and acceptor as parameters and fragment size (7-10) with optimum number of 6 components. Predictive power of developed HQSAR model was proved by test set and it was found to be 0.728.

• Bulletin of the Korean Chemical Society
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• v.25 no.10
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• pp.1525-1530
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• 2004

Mutagen X (MX) exists in our drinking water as the bi-products of chlorine disinfection. Being one of the most potent mutagen, it attracted much attention from many researchers. MX and its analogs are synthesized and modeled by quantitative structure activity relationship (QSAR) methods. As a result, factors affecting this class of compounds have been found to be steric and electrostatic effects. We tried to collect all the data available from the literature. With both CoMFA and CoMSIA various combinations of physiochemical parameters were systematically studied to produce reasonable 3-dimensional models. The best model for CoMFA gave $q^2$ = 0.90 and $r^2$ = 0.97, while for CoMSIA $q^2$ = 0.85 and $r^2$ = 0.94. So the models seem to be reasonable. Unlike previous result of CoMFA, in our case steric parameter alone gave the best statistics. Although the steric contribution was found to be the most important in both CoMFA and CoMSIA, steric parameter along with electrostatic parameter produced slightly better model in CoMSIA. Overall, steric contribution is clearly the most important single factor. However, when we compare chlorine and bromine substitution, chlorine substitution can be more mutagenic. This indicates that other factors such as electrostatic effect also influence the mutagenicity. From the contour maps, steric contribution seems to be focused on rather small area near C6 substituent of the furanone ring, rather than C3 substituent. Therefore the locality of steric contribution can play a significant role in mutagenicity.

• 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.

• The Korean Journal of Pesticide Science
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• v.9 no.2
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• pp.140-145
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• 2005

Quantitative Structure Activity Relationship (QSAR) assumes the relatedness between physical property and biological activity. However, activity data measured at single concentration such as percent activity have not been used extensively for modeling purpose. This probably comes from the fact that these values are qualitative instead of quantitative. To utilize percent activity data for molecular modeling, we classified the whole data into two classes. One class represents the active while the other signifies the inactive. The percent activity data of ${\beta}$-Ketoacetoanilides measured for TLB (Tomato Late Blight) were investigated. CoMFA (Comparative Molecular Field Analysis) was used as a discriminant function. Using CoMFA provides 3D (three dimensional) information, which is crucial for chemical insight. It can also serve as a predictive model. The resultant model classified the given data correctly (98%). When LOO (leave-one-out) crossvalidation procedure was applied, the classification accuracy was 69%. Therefore two class approximation of percent activity data with CoMFA can be utilized to understand the relationship between chemical structure and biological activity and design subsequent chemical analogs.