• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• 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.

• Archives of Pharmacal Research
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• v.27 no.10
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• pp.1001-1008
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• 2004

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on 59 cinnamaldehyde analogues as Farnesyl Protein Transferase (FPTase) inhibitors were investigated using comparative molecular field analysis (CoMFA) with the PLS region-focusing method. Forty-nine training set inhibitors were used for CoMFA with two different grid spacings, $2{\AA}\;and\;1{\AA}$ Ten compounds, which were not used in model generation, were used to validate the CoMFA models. After the PLS analysis, the best predictive CoMFA model showed that the cross-validated value $(r^2_{cv})$ and the non-cross validated conventional value$(r^2_{ncv})$ are 0.557 and 0.950, respectively. From the CoMFA contour maps, the steric and electrostatic properties of cinnamaldehyde analogues can be identified and verified.

• 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.

• The Korean Journal of Pesticide Science
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• v.7 no.2
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• pp.100-107
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• 2003

To improve the growth inhibitory activity against the shoot and root of rice plant (Oryza sativa L) and barnyard grass (Echinochloa crus-galli), a series of N-substituted phenyl-3,4-dimethylmaleimdes derivatives as substrates were synthesized and then their the inhibitory activities of protoporphyrinogen oxidase (1.3.3.4), protox were measured. The quantitative structure-activity relationships (QSAR) between structures and the inhibitory activities were studied quantitatively using the 2D-QSAR method. And also, molecular sharp similarity between the substrate derivatives and protogen, substrare of protox enzyme were studied. The activities of the two plants indicated that barnyard grass had a higher activity than the rice plant and their correlation relationships have shown in proportion for each. Accordingly, the results of SARs suggest that the electron donating groups as $R_2=Sub.X$ group will bind to phenyl ring because the bigger surface area of negative charged atoms in the substrate molecule derivatives may increase to the higher the activity against barnyard grass. Based on the molecular shape similarity, when the derivatives and protogen, subsbrate of protox enzyme were superimposed by atom fitting, the similarity indices (S) were above 0.8 level but the correlation coefficients (r) between S values and the activities showed not good.

• 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.

• 월간산업보건
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• s.328
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• pp.61-63
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• 2015

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 1993.06a
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• pp.6-8
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• 1993

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 1996.12a
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• pp.77-77
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• 1996