Literature DB >> 16721627

Discrimination between modes of toxic action of phenols using rule based methods.

Ulf Norinder1, Per Lidén, Henrik Boström.   

Abstract

Rule-based ensemble modelling has been used to develop a model with high accuracy and predictive capabilities for distinguishing between four different modes of toxic action for a set of 220 phenols. The model not only predicts the majority class (polar narcotics) well but also the other three classes (weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles) of toxic action despite the severely skewed distribution among the four investigated classes. Furthermore, the investigation also highlights the merits of using ensemble (or consensus) modelling as an alternative to the more traditional development of a single model in order to promote robustness and accuracy with respect to the predictive capability for the derived model.

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Year:  2006        PMID: 16721627     DOI: 10.1007/s11030-006-9019-3

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


  6 in total

Review 1.  Comparative QSAR: on the toxicology of the phenolic OH moiety.

Authors:  R Garg; A Kurup; C Hansch
Journal:  Crit Rev Toxicol       Date:  2001-03       Impact factor: 5.635

Review 2.  Computer systems for the prediction of toxicity: an update.

Authors:  Nigel Greene
Journal:  Adv Drug Deliv Rev       Date:  2002-03-31       Impact factor: 15.470

3.  QSAR of ecotoxicological data on the basis of data-driven if-then-rules.

Authors:  Stefan Pudenz; Rainer Brüggemann; Hans-Georg Bartel
Journal:  Ecotoxicology       Date:  2002-10       Impact factor: 2.823

Review 4.  Progress in toxinformatics: the challenge of predicting acute toxicity.

Authors:  Donatas Zmuidinavicius; Pranas Japertas; Alanas Petrauskas; Remigijus Didziapetris
Journal:  Curr Top Med Chem       Date:  2003       Impact factor: 3.295

5.  Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: a preliminary study.

Authors:  Shijin Ren
Journal:  Chemosphere       Date:  2003-12       Impact factor: 7.086

Review 6.  In silico prediction of drug toxicity.

Authors:  John C Dearden
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

  6 in total
  4 in total

1.  QSAR classification of metabolic activation of chemicals into covalently reactive species.

Authors:  Chin Yee Liew; Chuen Pan; Andre Tan; Ke Xin Magneline Ang; Chun Wei Yap
Journal:  Mol Divers       Date:  2012-02-28       Impact factor: 2.943

2.  QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors.

Authors:  A A Toropov; A P Toropova; E Benfenati
Journal:  Mol Divers       Date:  2009-05-19       Impact factor: 2.943

3.  Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Authors:  Chin Yee Liew; Yen Ching Lim; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2011-09-06       Impact factor: 3.686

4.  GEOM, energy-annotated molecular conformations for property prediction and molecular generation.

Authors:  Simon Axelrod; Rafael Gómez-Bombarelli
Journal:  Sci Data       Date:  2022-04-21       Impact factor: 8.501

  4 in total

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