Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study

Shahlaei, Mohsen

doi:10.1021/cr3004339

Cited by 181 publications

(117 citation statements)

References 65 publications

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“…the lowest ratio of the number of NMs to the number of descriptors should be 5 to 1, and the dependence of its performance on redundant variables, i.e. the presence of correlated input variables, as well as input variables that are irrelevant to the output, may lead to poor model performance (Shahlaei, 2013). Dimension reduction methods such as PCA can be useful for eliminating correlations between input variables (i.e.…”

Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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“…In this procedure, the independent variables (descriptors) which have high correlation with the experimental values (such as IC 50 ) added step by step to a regression equation and significance of each added variable tested in order to remain in the final multi-variate equation. If the joined descriptors have not any significant value in the regression equation, it will be removed (Shahlaei, 2013). The SPSS 16.0 was used for the computaion probability of F as stepping criteria (F = 0.05 and 0.1 for entry and removal, respectively).…”

Section: Stepwise Regression Methodsmentioning

confidence: 99%

Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity

et al. 2014

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HIV-1 integrase is an extremely important nominee in developing new and effective drugs especially naphthyridine compounds against acquired immune deficiency syndrome. The quantitative structure-activity relationship (QSAR) modeling is the most powerful method in computer-aided drug design and will be used to help the design of new naphthyridine derivatives. Different computational 2D-QSAR procedures applied to predict the relationship between the computational descriptors of naphthyridine derivatives with their HIV-1 integrase inhibition activities. Four different models including stepwise-MLR, consensus stepwise-MLR, GAPLS-MLR, and consensus GAPLS-MLR with appropriate correlation between the calculated and experimental biological activities (pIC 50 ) against HIV-1 integrase were generated. Predictive QSAR models were obtained with R training 2 values of 0.848, 0.862, 0.709, and 0.751 as well as R test 2 values of 0.521, 0.651, 0.502, and 0.775 for stepwise-MLR, consensus stepwise-MLR, GAPLS-MLR, and consensus GAPLS-MLR models, respectively. QSAR models are high efficiency in prediction of the pIC 50 in comparison with other models because of concerning the combination of ''quantum and molecular mechanical'' descriptors. Combination of ''quantum'' and ''molecular mechanical'' descriptors improved our models with high efficient test set activity prediction potency. The obtained results provided useful information for understanding the effects of polarizability, electronegativity, and especially functional groups such as aromatic nitrogens that are important for the activities of naphthyridine compounds. The developed QSAR models will be efficient for the rational design of potent naphthyridine derivatives against HIV-1 integrase activity.

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“…The leave-one-out (LOO) crossvalidation method [29][30][31][32] was used to find out the optimal set of spectral lines and to estimate the regression model predictivity. We used spectra of 36 pellets with nine different heating values for calibration, and for testing, we left out a subset of spectra of four pellets of the same sample having the known heating value from calorimetric measurements.…”

Section: Cross-validationmentioning

confidence: 99%

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

Aints¹,

Paris²,

Laan³

et al. 2018

Journal of Spectroscopy

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The laser-induced breakdown spectroscopy (LIBS) combined with multivariate regression analysis of measured data were utilised for determination of the heating value and the chemical composition of pellets made from Estonian oil shale samples with different heating values. The study is the first where the oil shale heating value is determined on the basis of LIBS spectra. The method for selecting the optimal number of spectral lines for ordinary multivariate least squares regression model is presented. The correlation coefficient between the heating value predicted by the regression model, and that measured by calorimetric bomb, was R 2 = 0 98. The standard deviation of prediction was 0.24 MJ/kg. Concentrations of oil shale components predicted by the regression model were compared with those measured by ordinary methods.

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Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study

Cited by 181 publications

References 65 publications

(Q)SAR modelling of nanomaterial toxicity: A critical review

(Q)SAR modelling of nanomaterial toxicity: A critical review

Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

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