Literature DB >> 18426198

Predictivity of QSAR.

Romualdo Benigni1, Cecilia Bossa.   

Abstract

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.

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Year:  2008        PMID: 18426198     DOI: 10.1021/ci8000088

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  14 in total

1.  Robust optimization of scoring functions for a target class.

Authors:  Markus H J Seifert
Journal:  J Comput Aided Mol Des       Date:  2009-05-27       Impact factor: 3.686

2.  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

3.  QSAR model based on weighted MCS trees approach for the representation of molecule data sets.

Authors:  Bernardo Palacios-Bejarano; Gonzalo Cerruela García; Irene Luque Ruiz; Miguel Ángel Gómez-Nieto
Journal:  J Comput Aided Mol Des       Date:  2013-02-06       Impact factor: 3.686

4.  An integrated approach to model the biomagnification of organic pollutants in aquatic food webs of the Yangtze Three Gorges Reservoir ecosystem using adapted pollution scenarios.

Authors:  Björn Scholz-Starke; Richard Ottermanns; Ursula Rings; Tilman Floehr; Henner Hollert; Junli Hou; Bo Li; Ling Ling Wu; Xingzhong Yuan; Katrin Strauch; Hu Wei; Stefan Norra; Andreas Holbach; Bernhard Westrich; Andreas Schäffer; Martina Roß-Nickoll
Journal:  Environ Sci Pollut Res Int       Date:  2013-02-01       Impact factor: 4.223

5.  Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors.

Authors:  A R Cunningham; S Qamar; C A Carrasquer; P A Holt; J M Maguire; S L Cunningham; J O Trent
Journal:  SAR QSAR Environ Res       Date:  2010-07       Impact factor: 3.000

6.  Chemical structure determines target organ carcinogenesis in rats.

Authors:  C A Carrasquer; N Malik; G States; S Qamar; S L Cunningham; A R Cunningham
Journal:  SAR QSAR Environ Res       Date:  2012-10-16       Impact factor: 3.000

7.  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

8.  Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity.

Authors:  C A Carrasquer; K Batey; S Qamar; A R Cunningham; S L Cunningham
Journal:  SAR QSAR Environ Res       Date:  2014-04-04       Impact factor: 3.000

9.  DPRESS: Localizing estimates of predictive uncertainty.

Authors:  Robert D Clark
Journal:  J Cheminform       Date:  2009-07-14       Impact factor: 5.514

10.  SuperToxic: a comprehensive database of toxic compounds.

Authors:  Ulrike Schmidt; Swantje Struck; Bjoern Gruening; Julia Hossbach; Ines S Jaeger; Roza Parol; Ulrike Lindequist; Eberhard Teuscher; Robert Preissner
Journal:  Nucleic Acids Res       Date:  2008-11-12       Impact factor: 16.971

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