Literature DB >> 19190994

QSAR modelling of carcinogenicity by balance of correlations.

A A Toropov1, A P Toropova, E Benfenati, A Manganaro.   

Abstract

Optimal descriptors based on the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity. Carcinogenicity of 401 compounds has been modeled by means of balance of correlations for the training (n = 170) and calibration (n = 170) sets. The obtained models were evaluated with an external test set (n = 61). Comparison of models based on the balance of correlations and models which were obtained on the basis of the total training set (i.e., both training and calibration sets as the united training set) has shown that the balance of correlations improves the statistical quality for the external test set.

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Year:  2009        PMID: 19190994     DOI: 10.1007/s11030-009-9113-4

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  19 in total

1.  Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network.

Authors:  G Gini; M Lorenzini; E Benfenati; P Grasso; M Bruschi
Journal:  J Chem Inf Comput Sci       Date:  1999 Nov-Dec

2.  Derivation and validation of toxicophores for mutagenicity prediction.

Authors:  Jeroen Kazius; Ross McGuire; Roberta Bursi
Journal:  J Med Chem       Date:  2005-01-13       Impact factor: 7.446

3.  LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities.

Authors:  David Vidal; Michael Thormann; Miquel Pons
Journal:  J Chem Inf Model       Date:  2005 Mar-Apr       Impact factor: 4.956

4.  Optimisation of correlation weights of SMILES invariants for modelling oral quail toxicity.

Authors:  Andrey A Toropov; Emilio Benfenati
Journal:  Eur J Med Chem       Date:  2006-12-15       Impact factor: 6.514

Review 5.  The expanding role of predictive toxicology: an update on the (Q)SAR models for mutagens and carcinogens.

Authors:  Romualdo Benigni; Tatiana I Netzeva; Emilio Benfenati; Cecilia Bossa; Rainer Franke; Christoph Helma; Etje Hulzebos; Carol Marchant; Ann Richard; Yin-Tak Woo; Chihae Yang
Journal:  J Environ Sci Health C Environ Carcinog Ecotoxicol Rev       Date:  2007 Jan-Mar       Impact factor: 3.781

6.  Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models.

Authors:  Joseph F Contrera; Naomi L Kruhlak; Edwin J Matthews; R Daniel Benz
Journal:  Regul Toxicol Pharmacol       Date:  2007-07-17       Impact factor: 3.271

7.  The trouble with QSAR (or how I learned to stop worrying and embrace fallacy).

Authors:  Stephen R Johnson
Journal:  J Chem Inf Model       Date:  2007-12-28       Impact factor: 4.956

Review 8.  Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: a review and roadmap for research.

Authors:  Susan D Richardson; Michael J Plewa; Elizabeth D Wagner; Rita Schoeny; David M Demarini
Journal:  Mutat Res       Date:  2007-09-12       Impact factor: 2.433

9.  Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds.

Authors:  Aliuska Morales Helguera; Maykel Pérez González; Maria Natália D S Cordeiro; Miguel Angel Cabrera Pérez
Journal:  Toxicol Appl Pharmacol       Date:  2007-03-15       Impact factor: 4.219

10.  Correlation weighting of valence shells in QSAR analysis of toxicity.

Authors:  Andrey A Toropov; Emilio Benfenati
Journal:  Bioorg Med Chem       Date:  2006-02-03       Impact factor: 3.641

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  3 in total

1.  Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

Authors:  Kazutoshi Tanabe; Bono Lučić; Dragan Amić; Takio Kurita; Mikio Kaihara; Natsuo Onodera; Takahiro Suzuki
Journal:  Mol Divers       Date:  2010-02-26       Impact factor: 2.943

2.  New public QSAR model for carcinogenicity.

Authors:  Natalja Fjodorova; Marjan Vracko; Marjana Novic; Alessandra Roncaglioni; Emilio Benfenati
Journal:  Chem Cent J       Date:  2010-07-29       Impact factor: 4.215

3.  Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions.

Authors:  Andrey A Toropov; Alla P Toropova; Emilio Benfenati
Journal:  Int J Mol Sci       Date:  2009-07-08       Impact factor: 6.208

  3 in total

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