• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.150-155
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• 2003

We discussed a quantitative structure-activity relationships (QSAR) technique on incomplete data set. We proposed a new solver that used 2 kinds of multi-layer neural networks. One is to compensate the defect data, and another is to evaluate the QSAR. The solver can predict the defects in model QSAR data. By using them, we get very high precision QSAR. It is 5-10 times higher than that of a traditional method. However, in case of anti-cancer Carboquone, the prediction is not so complete. It was about O(3) wrong than the model calculation. The predicted values would have rather large error. It is caused by noisy observations of Carboquone. However, if we used the uncertain predictions, new data are included in QSAR. If not, they were omitted. The effect would not be little. Therefore, we evaluated the QSAR. The results are contrary to the expectation, are not so wrong. We believe that the wrong effect is suppressed by including information of new data.

• Bulletin of the Korean Chemical Society
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• v.25 no.10
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• pp.1513-1520
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• 2004

We investigate a series of synthesized ${\beta}$-methoxyacrylate analogues for their 3D QSAR & HQSAR against Magnaporthe grisea (Rice Blast Disease). We perform the three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies, using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) procedure. In addition, we carry out a two-dimensional Quantitative Structure-Activity Relationship (2D-QSAR) study, using the Hologram QSAR (HQSAR). We perform these studies, using 53 compounds as a training set and 10 compounds as a test set. The predictive QSAR models have conventional $r^2$ values of 0.955 at CoMFA, 0.917 at CoMSIA, and 0.910 at HQSAR respectively; similarly, we obtain cross-validated coefficient $q^2$ values of 0.822 at CoMFA, 0.763 at CoMSIA, and 0.816 at HQSAR, respectively. From these studies, the CoMFA model performs better than the CoMSIA model.

• The Korean Journal of Pesticide Science
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• v.11 no.2
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• pp.72-81
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• 2007

Quantitative structure-activity relationships (QSARs) on the pre-emergency herbicidal activity and reactivity of a series of new O,O-dialkyl-1-phenoxyacetoxy-1-methylphosphonates (S) analogues against seed of cucumber (Cucumus Sativa) were discussed quantitatively using 2D-QSAR and HQSAR methods. The statistical values of HQSAR model were better than that of 2D-QSAR model. From the frontier molecular orbital (FMO) interaction between substrate molecule (S) and $BH^+$ ion (I) in PDH enzyme, the electrophilic reaction was superior in reactivity. From the effect of substituents, $R_2$-groups in substrate molecule (S) contributed to electrophilic reaction with carbonyl oxygen atom while X, Y-groups contributed to nucleophilic reaction with carbonyl carbon atom. And the influence of X,Y-groups was more effective than that of $R_2$-groups. As a results of 2D-QSAR model (I & II) and atomic contribution maps with HQSAR model, the more length of X, Y-groups is longer, the more herbicidal activity tends to increased. And also, the optimal ${\epsilon}LUMO$ energy, $({\epsilon}LUMO)_{opt.}$=-0.479 (e.v.) of substrate molecule is important factor in determining the herbicidal activity. It is predicted that the herbicidal activity proceeds through a nucleophilic reaction. From the analytical results of 2D-QSAR and HQSAR model, it is suggested that the structural distinctions and descriptors that contribute to herbicidal activities will be able to applied new herbicide design.

• Journal of Environmental Health Sciences
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• v.39 no.2
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• pp.130-137
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• 2013

Objectives: The OECD QSAR Application Toolbox was developed by the Organisation for Economic Cooperation and Development (OECD) to facilitate the practical use of QSAR approaches in regulatory contexts as well as to reduce the need for additional animal testing. In this study, human health and the ecotoxicity of chemicals were predicted by applying the OECD QSAR Application Toolbox and the results were compared with experimental data in order to evaluate the applicability of this program. Methods: Read-across, trend analysis, and QSAR of OECD QSAR Application Toolbox were used for the prediction of toxicity. Results: The toxicity prediction was conducted on 6,354 chemicals for which toxicity data have been produced on the six endpoints of skin sensitization, skin irritation, eye irritation, mutagenicity, and acute toxicities of fish and Daphnia. From the total of 6,354, we obtained prediction results for 1,621 chemicals (25.5%). Conclusions: The predicted properties of mutagenicity, skin sensitization, and acute aquatic toxicities were reasonably good when compared with experimental data, but other endpoints were not due to the limitation of applicable chemical groups.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.647-655
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• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• Archives of Pharmacal Research
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• v.23 no.4
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• pp.324-328
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• 2000

Three dimensional QSAR studies for antihistamine and antibradykinin effects of new piperazine derivatives were conducted using the comparative molecular field analysis. Electrostatic and steric factors, but not hydrophobic factor, of the synthesized compounds were correlated with the antagonistic effect.

• 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.

• Environmental Analysis Health and Toxicology
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• v.18 no.4
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• pp.271-276
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• 2003

The purpose of the present study was to perform experiments of cytotoxicity using HeLa cell and to evaluate the possibility that QSAR is applicable to the cytotoxicity of alkylphenols. Higher toxicities were found in four alkylphenols in the following order: 4-n-Nonylphenol) 4-tert-Octylphenol) 4-n-Octylphenol > 4-n Heptylpheonl. Whereas other alkylphenols were apparently less toxic. By using Percent Hydrophilic Surface Area (PHSA) quantitative structure-activity relationships (QSARs) models were developed: Cytotoxicity (%) = 90.14089-4.72224 PHSA ($R^2$=0.2046, $\alpha$=0.0265). It is concluded that some of the obtained data are useful to determine whether QSAR methods can be of general use in predicting that until further work is undertaken to develop QSARs for a much wider range of homologous series of alkylphenol compounds.

• Environmental Mutagens and Carcinogens
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• v.20 no.1
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• pp.21-25
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• 2000

Recently there is increasing interest in the use of structure activity relationships for predicting the biological activity of chemicals. The reasons for the interest include the decrease cost and time per chemical as compared with animal or cell system for identifying toxicological effects of chemicals and the reduction in the use of animals for toxicological testing. This study is to test the validity of the mutagenicity data generated from QSAR (Quantitative Structure Activity Relationship) program. Thirty chemicals, which had been evaluated by Ames test during 1997-1999, were assessed with TOPKAT QSAR mutagenicity prediction module. Among 30chemicals experimented, 28 were negative and 2 were positive for Ames test. On the contrary, 23 chemicals showed the high confidence level indicating high prediction rate in mutagenicity evaluation, and 7 chemicals showed the lsow to moderate confidence level indicating low prediction in mutagenicity evaluation. Overall mutagenicity prediction rate was 77% (23/30). The prediction rates for non-mutagenic chemicals were 79% (22/28) and mutagenic chemicals were 50% (1/2). QSAR could be a useful tool in providing toxicological data for newly introduced chemicals or in furnishing data for MSDS or in determining the dose in toxicity testing for chemicals with no known toxicological data.

• Reproductive and Developmental Biology
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• v.31 no.1
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• pp.15-20
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• 2007

To search of a new porcine pheromonal odorant for biostimulation control system technologies to offer a potentially useful and practical way to improve reproductive efficiency in livestock species, the two dimensional quantitative structure-activity relationship (QSAR) models between physicochemical parameters as descriptors of 2-cyclohexyloxytetrahydrofurane (A), 2-phenoxytetrahydrofurane (B) analogues and binding affinity constant ($p[Od.]_{50}$) for porcine odorant-binding protein (pOBP) as receptor of pig pheromones were derived and disscused. The statistical quality of the optimized 2D-QSAR model is good ($r^{2}=0.964$) and accounts for 96.4% of the variance in the binding affinity constants. It was found that the binding affinity constants were dependent upon the optimal value, $(SL)_{opt.}=1.418$ of substituent lipole (SL) in molecules. Therefore, the SL constant was very important factor for binding affinity.