Literature DB >> 11813801

Prediction of mutagenicity utilizing a hierarchical QSAR approach.

S C Basak1, D Mills.   

Abstract

Quantitative structure-toxicity (QSTR) models for the mutagenicity of a set of 95 aromatic amines were developed using four classes of calculated molecular descriptors, viz., topostructural, topochemical, geometrical and quantum chemical indices. Topochemical indices gave the best predictive model when the different classes of parameters were used separately. When hierarchical QSTRs were developed using all four classes of descriptors, there was a significant increase in explained variance by the addition of topochemical indices to the set of independent variables. The addition of geometrical and quantum chemical indices or log P to the set of descriptors resulted in very little improvement in model quality.

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Year:  2001        PMID: 11813801     DOI: 10.1080/10629360108039830

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  2 in total

1.  ANVAS: artificial neural variables adaptation system for descriptor selection.

Authors:  Paolo Mazzatorta; Marjan Vracko; Emilio Benfenati
Journal:  J Comput Aided Mol Des       Date:  2003 May-Jun       Impact factor: 3.686

2.  Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.

Authors:  Shuxing Zhang; Alexander Golbraikh; Alexander Tropsha
Journal:  J Med Chem       Date:  2006-05-04       Impact factor: 7.446

  2 in total

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