Document Detail

Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family.
MedLine Citation:
PMID:  10192946     Owner:  NLM     Status:  MEDLINE    
Predictive models based on tuned molecular quantum similarity measures and their application to obtain quantitative structure-activity relationships (QSAR) are described. In the present paper, the corticosteroid-binding globulin binding affinity of a 31 steroid family is studied by means of a multilinear regression using molecular descriptors derived from mixed steric-electrostatic quantum similarity matrixes as parameters, obtaining satisfactory predictions. A systematic procedure to treat outliers by using triple-density quantum similarity measures is also presented. This method depicts an alternative to the grid-based QSAR techniques, providing a consistent approach that avoids problematic result dependency on the grid parameters.
D Robert; L Amat; R Carbó-Dorca
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Publication Detail:
Type:  Comparative Study; In Vitro; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of chemical information and computer sciences     Volume:  39     ISSN:  0095-2338     ISO Abbreviation:  J Chem Inf Comput Sci     Publication Date:    1999 Mar-Apr
Date Detail:
Created Date:  1999-05-10     Completed Date:  1999-05-10     Revised Date:  2008-11-21    
Medline Journal Info:
Nlm Unique ID:  7505012     Medline TA:  J Chem Inf Comput Sci     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  333-44     Citation Subset:  IM    
Institute of Computational Chemistry, University of Girona, Catalonia, Spain.
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MeSH Terms
Models, Chemical*
Quantum Theory
Regression Analysis
Reproducibility of Results
Static Electricity
Steroids / chemistry*,  metabolism*
Structure-Activity Relationship*
Transcortin / metabolism*
Reg. No./Substance:
0/Steroids; 9010-38-2/Transcortin

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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