• Title, Summary, Keyword: QSAR

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Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodologies. I. The basic concepts and types of QSAR methodologies (정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발 I. 기본 개념과 QSAR 기법의 유형)

  • Sung, Nack-Do
    • The Korean Journal of Pesticide Science
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    • v.6 no.3
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    • pp.166-174
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    • 2002
  • The fundamental concepts on the basis of linear free energy relationship (LFER), history of development, prediction of pharmacological effects, advantages and disadvantages, etc. according to the 2D and 3D QSAR methodologies were summarized in utilizing the quantitative structure-activity relation ship (QSAR) techniques for searching and development of new agrochemicals. Objectives, role of QSAR techniques in development process of pesticides and limitations in QSARs were discussed and introduced.

Development of New Agrochemicals by Quantitative Structure-Activity Relationship (QSAR) Methodology -IV. A Tendency of Research and Prospect in Korea- (정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발 -IV. 국내의 연구 동향과 전망-)

  • Sung, Nack-Do
    • Applied Biological Chemistry
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    • v.46 no.3
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    • pp.155-164
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    • 2003
  • It was reviewed for the status of domestic research before and after 1990's for search of a new pesticides using 2D QSAR of quantitative structure-activity relationship (QSAR) methodologies (Sung, Nack-Do (2002) Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodology. Kor J. Pestic. Sci. 6, 166-174, 231-243 & 7, 1-11) which was proposed according to Hansch-Fujita equation based on the concept of biological Hammett equation.

Development of QSAR Model Based on the Key Molecular Descriptors Selection and Computational Toxicology for Prediction of Toxicity of PCBs (PCBs 독성 예측을 위한 주요 분자표현자 선택 기법 및 계산독성학 기반 QSAR 모델 개발)

  • Kim, Dongwoo;Lee, Seungchel;Kim, Minjeong;Lee, Eunji;Yoo, ChangKyoo
    • Korean Chemical Engineering Research
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    • v.54 no.5
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    • pp.621-629
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    • 2016
  • Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

Development of new agrochemicals by quantitative structure-activity relationship (QSAR) methodology. III. 3D QSAR methodologies and computer-assisted molecular design (CAMD) (정량적인 구조-활성상관 (QSAR) 기법에 의한 새로운 농약의 개발. III. 3D QSAR 기법들과 컴퓨터를 이용한 분자설계(CAMD))

  • Sung, Nack-Do
    • The Korean Journal of Pesticide Science
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    • v.7 no.1
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    • pp.1-11
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    • 2003
  • Acoording to improvement of HTOS (high throughput organic synthesis) and HTS (high throughput screening) technique, the CoMFA (comparative molecular field analysis), CoMSIA (comparative molecular similarity indeces analysis) and molecular HQSAR (hologram quantitative structure-activity relationship) analysis techniques as methodology of computer assisted molecular design (CAMD) were introduced generally and summarized for some application cases.

A new learning algorithm for incomplete data sets and multi-layer neural networks

  • Bitou, Keiichi;Yuan, Yan;Aoyama, Tomoo;Nagashima, Umpei
    • 제어로봇시스템학회:학술대회논문집
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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.

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Toward Proper 3D-QSAR Datasets for Parameter Evaluation

  • Cho, Seung Joo
    • Journal of the Chosun Natural Science
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    • v.4 no.3
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    • pp.197-201
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    • 2011
  • 3D-QSAR techniques including CoMFA have been used a lot for more than two decades now. For now, the perspective of 3D-QSAR has been changed. The realization of gorge activity cliffs and higher chance correlation with many independent variables (IVs) has changed the requirements. Some suggested the benchmarking datasets for 3D-QSAR. However, were they still useful for right reasons? Here, we propose the requirement of any general purpose 3D-QSAR benchmarking datasets for lead optimization, especially for feasibility test of any IVs. Specifically, we summarize the conceptual requirements for an ideal settings for 3D-QSAR especially CoMFA.

Prediction of Human Health and Ecotoxicity of Chemical Substances Using the OECD QSAR Application Toolbox (OECD QSAR Application Toolbox를 이용한 화학물질의 건강유해성 및 생태독성 예측)

  • Kim, Jungkon;Seo, Jung-Kwan;Kim, Taksoo;Kim, Hyun-Kyung;Park, Sanghee;Kim, Pil-Je
    • 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.

2D-QSAR and HQSAR Analysis on the Herbicidal Activity of New Cyclohexanedione Derivatives (새로운 Cyclohexanedione계 유도체의 제초활성에 관한 2D-QSAR 및 HQSAR 분석)

  • Kim, Yong-Chul; Hwang, Tae-Yeon;Sung, Nack-Do
    • The Korean Journal of Pesticide Science
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    • v.12 no.1
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    • pp.9-17
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    • 2008
  • QSARs (Quantitative structure-activity relationships) between a series of new cyclohexanedione derivatives (5-benzofuryl-2-[1-(alkoxyimino)-alkyl]-3-hydroxycyclohex-2-en-1-ones) and their herbicidal activity against Rice plant (Oryza sativa L.) and Barnyard grass (Echinochloa crus-galli.) were discussed quantitatively using 2D-QSAR and holographic (H) QSAR methods. Generally, the HQSAR models have better predictability and fitness than the 2D-QSAR models. The herbicidal activities against Barnyard grass with 2D-QSAR II model were dependent upon Balaban indice (BI) of molecule and hydrophobicity of $R_1$ and $R_3$ group. And also, the $R_3=ethyl$ group, according to the information of the optimized HQSAR IV model, was more contribute to the herbicidal activities against Rice plant, while the 5-(cyclohex-3-enyl)-2,3-dihydrobenzofuran ring part was not contribute to the herbicidal activities against two plants.

Prediction of Sorption/Desorption Parameters of Halogenated Aliphatic Compounds Using QSAR (QSAR을 이용한 지방족 할로겐화합물 흡착 및 탈착 계수의 예측)

  • 김종오;박증석;최연돈
    • Journal of Environmental Science International
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    • v.11 no.7
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    • pp.737-742
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    • 2002
  • Sorption and desorption is an important phenomenon to determine the fate of halogenated aliphatic hydrocarbons in the aqueous phase. This study was conducted to develope a predictive equation capable of estimating the sorption and desorption potentials of halogenated aliphatic hydrocarbons onto the sludge from activated process, sediment, and clay. It has shown that the sorption and desorption parameters can be accurately estimated using Quantitative Structural Activity Relationship(QSAR) models based on molecular connectivity indexes of test compounds. The QSAR model could be applied to predict the sorption and desorption capacity of the other halogenated aliphatic hydrocarbons. The QSAR modeling would provide a useful tool to predict the sorption and desorption capacity without time-consuming experiments.

Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?

  • Kim, Jae Hyoun
    • Journal of Environmental Health Sciences
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    • v.38 no.6
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    • pp.550-560
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    • 2012
  • Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.