• Archives of Pharmacal Research
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• v.27 no.5
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• pp.467-470
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• 2004

Comparative molecular field analysis and comparative molecular similarity indices analysis were performed on twenty five analogues of pimarane COX-2 inhibitor to optimize their cyclooxygenase-2 (COX-2) selective anti-inflammatory activities.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.186.3-186.3
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• 2003

The three-dimensional quantitative structure-activity relationship (3D-QSAR) study using the comparative molecular field analysis (CoMFA) was performed on indolinones derivatives as an inhibitor of the protein tyrosine kinase of fibroblast growth factor receptor (FGFR). In the training set, twenty-four indolinone derivatives were aligned based on the indole fragment and the steric and electrostatic fields were included in the analysis. The best predicted model showed the cross-validated coefficient (r$^2$$\sub$cv/) of 0.804 and bib-cross validated coefficient (r$^2$) of 0.942. The CoMFA study can be used to predict several new inhibitors of the FGFR.

• Bulletin of the Korean Chemical Society
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• v.34 no.8
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• pp.2387-2393
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• 2013

Benzotriazole is an important synthetic auxiliary for potential clinical applications. A series of benzotriazoles as potential antiproliferative agents by inhibiting histone deacetylase (HDAC) were recently reported. Three-dimensional quantitative structure-activity relationship (3D-QSAR), including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were performed to elucidate the 3D structural features required for the antiproliferative activity. The results of both ligand-based CoMFA model ($q^2=0.647$, $r^2=0.968$, ${r^2}_{pred}=0.687$) and CoMSIA model ($q^2=0.685$, $r^2=0.928$, ${r^2}_{pred}=0.555$) demonstrated the highly statistical significance and good predictive ability. The results generated from CoMFA and CoMSIA provided important information about the structural characteristics influence inhibitory potency. In addition, docking analysis was applied to clarify the binding modes between the ligands and the receptor HDAC. The information obtained from this study could provide some instructions for the further development of potent antiproliferative agents and HDAC inhibitors.

• Applied Biological Chemistry
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• v.48 no.3
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• pp.252-257
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• 2005

Three-dimensional quantitative structure-activity relationships (3D-QSARs) for the herbicidal activities against pre-emergence barnyard grass (Echinochloa crus-galli) by new 2-(4-(6-chloro-2-benzoxazolyloxy)phenoxy)-N-phenylpropion amide derivatives were studied quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. The best CoMFA model (AI-2) and CoMSIA model (AII-4) were derived from an atom based fit alignment and a combination of CoMFA fields. The herbicidal activities from CoMFA and CoMSIA contour maps showed that the activity will be able to be increased according to the substituents variation on the N-phenyl ring.

• Applied Biological Chemistry
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• v.51 no.3
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• pp.171-176
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• 2008

The comparative molecular similarity indices analysis (CoMSIA) models between 3${\beta}$-Hydroxy-12-oleanen-28-oic acid (1-30) analogues as substrate molecule and their inhibitory activities ($pI_{50}$) against protein tyrosine phosphatase (PTP)-1B were derived and discussed quantitatively. Listing in order, the CoMFA>CoMSIA${\geq}$HQSAR>2D-QSAR model, these QSAR models had the better statistical values. The optimized CoMSIA F1 model at grid 3.0${\AA}$ had the best predictability and fitness ($q^2$=0.754 and $r^2$=0.976) by field fit alignment. The order of contribution ratio (%) of CoMSIA fields concerning the inhibitory activities was a H-bond acceptor (48.9%), steric field (25.8%) and hydrophobic field (25.4%), respectively. Therefore, the inhibitory activities of substrate molecules against PTP-1B were dependent upon H-bond acceptor field (A) of $R_4$-group. From the analytical results of CoMSIA contour maps, oleanolic acid derivatives will have better inhibition activities if $R_1$ group has H-bond acceptor disfavor, $R_3$group has steric disfavor and $R_4$ group has steric, hydrophobic, H-bond favor.

• Analytical Science and Technology
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• v.24 no.6
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• pp.544-555
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• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• Applied Biological Chemistry
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• v.34 no.3
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• pp.287-294
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• 1991

The relationships between the quantitative structure of benzotriazoles and their post-emergence growth inhibiting activity$(pI_{50})$ against Oryzae sativa L. and Echinochloa crus-galli were investigated using a generalized quantitative structure activity relationships (QSAR). According to the QSAR analysis, the free radical parameter $(E_R)$ is a very important factor and the growth inhibiting activity values showed parabolic relation to $E_R$ parameter of para-substituents(X). The activity of (3) was superior to those of (4) and (3b) is selected as the most highly effective compound. The optimal values of $E_R$ parameter of the growth inhibiting activity aganist E.crus-galli are $E_R(3)=0.52\;and\;E_R(4)=0.15$, respectively. From the result of molecular design, the substituents(X) of electron withdrawing properties and $E_R$ parameter of optimal value(0.52) were most desirable for high activity of the benzotriazoles. And in view of this, benzotriazoles may also be effective in blocking the photosynthetic electron transfer.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• 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.

• 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.