Document Detail

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors.
MedLine Citation:
PMID:  18298905     Owner:  NLM     Status:  MEDLINE    
AIM: To develop an artificial neural network model for predicting the resistance index (RI) of taxoids.
METHODS: A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models.
RESULTS: The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933.
CONCLUSION: The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.
Pei-pei Dong; Yan-yan Zhang; Guang-bo Ge; Chun-zhi Ai; Yong Liu; Ling Yang; Chang-xiao Liu
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Acta pharmacologica Sinica     Volume:  29     ISSN:  1745-7254     ISO Abbreviation:  Acta Pharmacol. Sin.     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2008-02-26     Completed Date:  2009-04-22     Revised Date:  2013-05-28    
Medline Journal Info:
Nlm Unique ID:  100956087     Medline TA:  Acta Pharmacol Sin     Country:  China    
Other Details:
Languages:  eng     Pagination:  385-96     Citation Subset:  IM    
Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
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MeSH Terms
Breast Neoplasms
Cell Line, Tumor
Drug Resistance, Multiple / drug effects*
Inhibitory Concentration 50
Models, Molecular
Molecular Structure
Neural Networks (Computer)*
Paclitaxel / chemistry
Predictive Value of Tests
Principal Component Analysis
Quantitative Structure-Activity Relationship
Reproducibility of Results
Taxoids / chemistry*,  classification*
Reg. No./Substance:
0/Taxoids; 15H5577CQD/docetaxel; 33069-62-4/Paclitaxel

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

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