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    
Abstract/OtherAbstract:
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.
Authors:
Pei-pei Dong; Yan-yan Zhang; Guang-bo Ge; Chun-zhi Ai; Yong Liu; Ling Yang; Chang-xiao Liu
Related Documents :
14527555 - The 3d-qsar study of antitumor arylsulfonylimidazolidinone derivatives by comfa and com...
15809315 - Development of cyp3a4 inhibition models: comparisons of machine-learning techniques and...
19046675 - Validated quantitative structure-activity relationship analysis of a series of 2-aminot...
23285315 - Unsupervised automatic white matter fiber clustering using a gaussian mixture model.
1988205 - Multi-factor designs. iv. how multi-factor designs improve the estimate of total error ...
25113335 - Mature larva of stenichnus collaris (müller & kunze) (coleoptera: staphylinidae: scydm...
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    
Affiliation:
Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms
Breast Neoplasms
Cell Line, Tumor
Drug Resistance, Multiple / drug effects*
Female
Humans
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
Software
Taxoids / chemistry*,  classification*
Chemical
Reg. No./Substance:
0/Taxoids; 15H5577CQD/docetaxel; 33069-62-4/Paclitaxel

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


Previous Document:  Identification of ginkgolide B metabolites in urine and rat liver cytochrome P450 enzymes responsibl...
Next Document:  A novel high-throughput format assay for HIV-1 integrase strand transfer reaction using magnetic bea...