Determination and prediction of xenoestrogens by recombinant yeast-based assay and QSAR

Li, Fēi; Chen, Jingwen; Wang, Zijian; Li, Jian; Qiao, Xianliang

doi:10.1016/j.chemosphere.2008.11.081

Cited by 14 publications

(7 citation statements)

References 42 publications

(34 reference statements)

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“…There are thousands of descriptors to represent the structure of a compound, thus it is hard to select the best variance to develop a robust and predictive C-QSAR model. Current variable selection methods include PLS [14], k-NN [9], ANN [6,8], GA [5], and HM (heuristic method) [13]. It is impossible to interpret the built model from the aspect of mechanistic interpretation because too many descriptors are included in the kNN and ANN model.…”

Section: Introductionmentioning

confidence: 99%

“…The QSAR model is roughly classified into classical QSAR (C-QSAR) model and three-dimensional QSAR (3D-QSAR) model, which are used to build the quantitative structure-activity relationship of estrogenic compounds [4][5][6][7][8][9][10][11][12][13]. Compared with 3D-QSAR model, C-QSAR model has an advantage of no requirement of alignment and rapid computation [5,8,13]. There are thousands of descriptors to represent the structure of a compound, thus it is hard to select the best variance to develop a robust and predictive C-QSAR model.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

et al. 2010

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Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor (ER) exists as two subtypes, ER and ER. Compounds might have different relative affinities and binding modes for ER and ER. In this study, the heuristic method was performed on 31 compounds binding to ER to select 5 variances most related to the activity (LogRBA) from 1524 variances, which were then employed to develop the best model with the significant correlation and the best predictive power (r 2 = 0.829, q 2 LOO = 0.742, r 2 pred = 0.772, q 2 ext = 0.724, RMSEE = 0.395) using multiple linear regression (MLR). The model derived identified critical structural features related to the activity of binding to ER. The applicability domain (AD) of the model was assessed by Williams plot. estrogen receptor  (ER), quantitative structure-activity relationship (QSAR), heuristic method, applicability domain

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

et al. 2010

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“…Recently computational methods have been used to solve complex problems in many aspects of science. One particularly useful method of the development of quantitative structure-activity relationships (QSARs) has found application in environmental chemistry and ecotoxicology (Deweese and Schultz, 2001;Leblond et al, 2000;Cotescu and Diudea, 2006;Li et al, 2009;Lu et al, 2008). QSAR approach systematization which has to be associated to the work of Hansch and Fujita (1964) is based on the assumption that the structure of a molecule must contain the features responsible for its physical, chemical and biological properties and on the possibility of representing a molecule by numerical descriptors.…”

Section: Introductionmentioning

confidence: 99%

Chemometric modeling to predict retention times for a large set of pesticides or toxicants using hybrid genetic algorithm/multiple linear regression approach

Amirat

Ziani

Messadi

2016

Management of Environmental Quality: An International Journal

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Purpose – The purpose of this paper is to predict the retention times of 84 pesticides or toxicants. Design/methodology/approach – Quantitative structure – retention relationship analysis was performed on a set of 84 pesticides or toxicants using a hybrid approach genetic algorithm/multiple linear regression (GA/MLR). Findings – A model with six descriptors was developed using as independent variables. Theoretical descriptors derived from Spartan and Dragon softwares when applying GA/MLR approach. Originality/value – A six parameter linear model developed by GA/MLR, with R² of 90.54, Q² of 88.15 and S of 0.0381 in Log value. Several validation techniques, including leave-many-out cross-validation, randomization test, and validation through the test set, illustrated the reliability of the proposed model. All of the descriptors involved can be directly calculated from the molecular structure of the compounds, thus the proposed model is predictive and could be used to estimate the retention times of pesticides or toxicants.

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“…So, in the last decade, computational-based rational design of drugs has increased rapidly. Most of these approaches are focused on using different kinds of molecular descriptors to encode chemical information (Doležal et al, 2009;Li et al, 2009;Mercader et al, 2008). Topological index, geometrical descriptors, and other descriptors are included in QSAR studies.…”

Section: Introductionmentioning

confidence: 99%

QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach

Cheng

Zhang

2009

Med Chem Res

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In this work, the multiple linear regression (MLR) and support vector machine (SVM) methods were applied for modeling and predicting the inhibitory activity of imidazopyridine derivatives with two-dimensional (2D) autocorrelation descriptors calculated from the molecular structure alone for the first time. We define the new objective function as fitness, and it can guide the particle swarm optimization (PSO) method to select important descriptors which are responsible for the inhibitory activity of these compounds. The square of the correlation coefficient (R 2 = 0.897), the square of correlation coefficients of the test set R 2 ext ¼ 0:660 À Á , and the obtained statistical parameter of the calibrating set in the PSO-SVM model was 0.743, which demonstrated the reliability of the model. The PSO-SVM model is superior over the PSO-MLR method in the dataset with imidazopyridine derivatives as Et-PKG inhibitors. Our best quantitative structure-activity relationship model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals atomic masses, van der Waals volumes, Sanderson electronegativities, and polarizabilities to be the most influential atomic properties in the structures of the imidazopyridine derivatives.

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Determination and prediction of xenoestrogens by recombinant yeast-based assay and QSAR

Cited by 14 publications

References 42 publications

Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

Chemometric modeling to predict retention times for a large set of pesticides or toxicants using hybrid genetic algorithm/multiple linear regression approach

QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach

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