Document Detail


A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds.
MedLine Citation:
PMID:  15032558     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The three-dimensional quantitative structure-activity relationship (QSAR) technique of comparative molecular field analysis (CoMFA) has demonstrated the ability to provide accurate predictions for diverse chemical compounds when trained with molecules of diverse chemical type. Although predictive, the derivation and utilization of models of this type are quite computationally and person power intensive. It is this intensity that pragmatically limits the widespread implementation of these models as predictive tools. In this study, two newer QSAR techniques were evaluated as possible alternatives to CoMFA based QSAR models for the purpose of rapidly identifying estrogen receptor ligands from diverse collections of molecules. The first of these is Hologram QSAR, or HQSAR. HQSAR utilizes Tripos molecular fingerprints as descriptors in conjunction with partial least squares (PLS) regression and cross-validation routines. The HQSAR technique demonstrated the ability to rapidly develop QSAR models independent of the intense user input (i.e. geometry optimization, conformational analysis, and molecular superposition were not required). Second, a newly developed QSAR paradigm that utilizes Molecular Design Limited (MDL) substructure keys (SKEYS) as descriptors in combination with an evolutionary algorithm, Fast Random Elimination of Descriptors (FRED), was evaluated. By utilizing the FRED/SKEYS algorithm, a simple substructure-based QSAR model was derived that was comparable in statistical robustness and predictive ability to both CoMFA and HQSAR derived models. A comparison of the utility of these three approaches as computational tools for the rapid identification of estrogen receptor ligands as potential endocrine disruptors as assessed by model predictive ability will be described.
Authors:
Chris L Waller
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Journal of chemical information and computer sciences     Volume:  44     ISSN:  0095-2338     ISO Abbreviation:  J Chem Inf Comput Sci     Publication Date:    2004 Mar-Apr
Date Detail:
Created Date:  2004-03-22     Completed Date:  2004-12-22     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:  758-65     Citation Subset:  IM    
Affiliation:
Pfizer Global Research and Development, Ann Arbor, Michigan 48105, USA. Chris.waller@pfizer.com
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence
Chemistry, Physical
Drug Design
Estrogens / chemistry,  pharmacology
Estrogens, Non-Steroidal / chemistry,  pharmacology
Least-Squares Analysis
Ligands
Models, Chemical
Physicochemical Phenomena
Quantitative Structure-Activity Relationship*
Random Allocation
Receptors, Estrogen / drug effects
Reproducibility of Results
Chemical
Reg. No./Substance:
0/Estrogens; 0/Estrogens, Non-Steroidal; 0/Ligands; 0/Receptors, Estrogen

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


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