Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Massarelli, Ilaria; Imbriani, Marcello; Coi, Alessio; Saraceno, Marilena; Carli, Niccolò; Bianucci, Anna Maria Paola

doi:10.1016/j.ejmech.2009.02.014

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…Recently, the hepatocarcinogenic toxicity was investigated by developing a SVM regression model in combination with the feature selection procedure applied to 55 chemicals . The prediction results, corresponding to a value of correlation coefficient r 2 of 0.919, have confirmed the SVM technique as a reliable methodology to infer the potential hepatocarcinogenicity of new chemicals.…”

Section: Qsar In Predictive Toxicologymentioning

confidence: 94%

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Michielan

Moro

2010

J. Chem. Inf. Model.

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Section: Qsar In Predictive Toxicologymentioning

confidence: 94%

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Michielan

Moro

2010

J. Chem. Inf. Model.

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“…Y-randomisation test is another method, which can be used to prove that chance has no role in obtaining the results of the model [46,47]. To perform Y-randomisation test, pIC 50 values were randomly shuffled for both training and test sets, and a new QSAR model was developed using the descriptor matrix of the training set.…”

Section: Qsar Modellingmentioning

confidence: 99%

The use of ladder particle swarm optimisation for quantitative structure–activity relationship analysis of human immunodeficiency virus-1 integrase inhibitors

Jalali‐Heravi

Ebrahimi‐Najafabadi

2011

Molecular Simulation

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This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole-and triazolesubstituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure -activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x -y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R 2 , and root-mean-square error for the test set were 0.876 and 0.23, respectively. Robustness of the model was confirmed by Y-randomisation method. Comparison of the LPSO-ANN results with those of stepwise multiple linear regression (MLR), LPSO-MLR and LPSO-MLR-ANN showed the superiority of LPSO-ANN. Inspection of the selected variables indicated that atomic mass, molecular size and electronic structure of the molecules play a significant role in inhibitory behaviour of oxadiazole-and triazole-substituted naphthyridines.

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“…There are available many QSAR studies developed during past years by different research groups, which involve a limited number of class-specific compounds, most of them nitro-containing derivatives [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. On the other hand, Galvez has gathered the carcinogenic activity in the Discriminant Function (DF) scale (DFcarc) for a wide set of 1815 organic compounds extracted from the Merck index, based on the annual report of carcinogenesis [33].…”

Section: Introductionmentioning

confidence: 99%

QSAR Study for Carcinogenicity in a Large Set of Organic Compounds

Duchowicz¹,

Comelli²,

Ortiz³

et al. 2012

Current Drug Safety

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In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.

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Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Cited by 13 publications

References 26 publications

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

The use of ladder particle swarm optimisation for quantitative structure–activity relationship analysis of human immunodeficiency virus-1 integrase inhibitors

QSAR Study for Carcinogenicity in a Large Set of Organic Compounds

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