Document Detail


SAMFA: simplifying molecular description for 3D-QSAR.
MedLine Citation:
PMID:  18503264     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper we consider the following question: How much can we simplify molecular description without sacrificing too much quality of 3D-QSAR models. We compare the performance of the newly developed Simple Atom Mapping Following Alignment (SAMFA) descriptors with CoMFA using nine different data sets from the literature, by using three regression approaches (PLS, SVM, RandomForest), as implemented in R, and Monte Carlo cross-validation (MCCV) numerical experiments. The results indicate that SAMFA descriptors, despite their simplicity, perform surprisingly well when compared to the much more refined CoMFA descriptors. Moreover, their simplicity makes them readily interpretable and applicable to the difficult problem of inverse QSAR.
Authors:
John Manchester; Ryszard Czermiński
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Publication Detail:
Type:  Journal Article     Date:  2008-05-27
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  48     ISSN:  1549-9596     ISO Abbreviation:  -     Publication Date:  2008 Jun 
Date Detail:
Created Date:  2008-06-24     Completed Date:  2008-07-30     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1167-73     Citation Subset:  IM    
Affiliation:
AstraZeneca Pharmaceuticals R&D Boston, Waltham, Massachusetts 02451, USA.
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MeSH Terms
Descriptor/Qualifier:
Computer Simulation*
Humans
Least-Squares Analysis
Models, Molecular*
Monte Carlo Method
Quantitative Structure-Activity Relationship*
Reproducibility of Results

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