Literature DB >> 8933054

Prediction of the rodent carcinogenicity of organic compounds from their chemical structures using the FALS method.

I Moriguchi1, H Hirano, S Hirono.   

Abstract

Fuzzy adaptive least-squares (FALS), a pattern recognition method recently developed in our laboratory for correlating structure with activity rating, was used to generate quantitative structure-activity relationship (QSAR) models on the carcinogenicity of organic compounds of several chemical classes. Using the predictive models obtained from the chemical class-based FALS QSAR approach, the rodent carcinogenicity or noncarcinogenicity of a group of organic chemicals currently being tested by the U.S. National Toxicology Program was estimated from their chemical structures.

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Year:  1996        PMID: 8933054      PMCID: PMC1469684          DOI: 10.1289/ehp.96104s51051

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


  5 in total

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Journal:  Mutat Res       Date:  1994-02-01       Impact factor: 2.433

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Journal:  Mutat Res       Date:  1991-05       Impact factor: 2.433

3.  The micronucleus assay with mouse peripheral blood reticulocytes using acridine orange-coated slides with triethylenemelamine.

Authors:  E Yamamura; H Hirono; M Takeuchi; M Kojima; S Aoki
Journal:  Mutat Res       Date:  1992 Feb-Mar       Impact factor: 2.433

4.  [Shigella dysenteriae strains having a provisional serovar isolated from imported diarrheal cases in Tokyo].

Authors:  S Matsushita; S Yamada; Y Kudoh
Journal:  Kansenshogaku Zasshi       Date:  1992-07

Review 5.  Sixth plot of the carcinogenic potency database: results of animal bioassays published in the General Literature 1989 to 1990 and by the National Toxicology Program 1990 to 1993.

Authors:  L S Gold; N B Manley; T H Slone; G B Garfinkel; B N Ames; L Rohrbach; B R Stern; K Chow
Journal:  Environ Health Perspect       Date:  1995-11       Impact factor: 9.031

  5 in total
  4 in total

1.  Prediction of genotoxicity of various environmental pollutants by artificial neural network simulation.

Authors:  Ryo Shoji; Masato Kawakami
Journal:  Mol Divers       Date:  2006-06-27       Impact factor: 2.943

2.  The NIEHS Predictive-Toxicology Evaluation Project.

Authors:  D W Bristol; J T Wachsman; A Greenwell
Journal:  Environ Health Perspect       Date:  1996-10       Impact factor: 9.031

Review 3.  In silico prediction of drug toxicity.

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

4.  Data mining in the U.S. National Toxicology Program (NTP) database reveals a potential bias regarding liver tumors in rodents irrespective of the test agent.

Authors:  Matthias Ring; Bjoern M Eskofier
Journal:  PLoS One       Date:  2015-02-06       Impact factor: 3.240

  4 in total

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