Literature DB >> 9152017

Computational predictive programs (expert systems) in toxicology.

E Benfenati1, G Gini.   

Abstract

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Entities:  

Mesh:

Year:  1997        PMID: 9152017     DOI: 10.1016/s0300-483x(97)03631-7

Source DB:  PubMed          Journal:  Toxicology        ISSN: 0300-483X            Impact factor:   4.221


  8 in total

1.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

Authors:  Natalja Fjodorova; Marjan Vračko; Marjan Tušar; Aneta Jezierska; Marjana Novič; Ralph Kühne; Gerrit Schüürmann
Journal:  Mol Divers       Date:  2009-08-15       Impact factor: 2.943

Review 2.  Paradigm shift in toxicity testing and modeling.

Authors:  Hongmao Sun; Menghang Xia; Christopher P Austin; Ruili Huang
Journal:  AAPS J       Date:  2012-04-20       Impact factor: 4.009

3.  QSAR Methods.

Authors:  Giuseppina Gini
Journal:  Methods Mol Biol       Date:  2022

4.  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

5.  Interpretation of the mechanism of acetylcholinesterase inhibition ability by organophosphorus compounds through a new conformational descriptor. an experimental and theoretical study.

Authors:  Guido Mastrantonio; Hans-Georg Mack; Carlos Omar Della Védova
Journal:  J Mol Model       Date:  2008-06-26       Impact factor: 1.810

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

7.  Predicting drug side-effect profiles: a chemical fragment-based approach.

Authors:  Edouard Pauwels; Véronique Stoven; Yoshihiro Yamanishi
Journal:  BMC Bioinformatics       Date:  2011-05-18       Impact factor: 3.169

8.  In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining.

Authors:  Feng Zhang; Kumar Ganesan; Yan Li; Jianping Chen
Journal:  Int J Mol Sci       Date:  2022-09-02       Impact factor: 6.208

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.