Document Detail

Quantitative structure-property relationship study of the solvent polarity using wavelet neural networks.
MedLine Citation:
PMID:  17690424     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Quantitative structure-property relationship (QSPR) studies based on artificial neural network (ANN) and wavelet neural network (WNN) techniques were carried out for the prediction of solvent polarity. Experimental S' values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. Semi-empirical quantum chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and different quantum-chemical descriptors were calculated by the HyperChem software. A stepwise MLR method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network models. The results obtained by the two methods were compared and it was shown that in WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than ANN. The average relative error in WNN was 7.9 and 6.8% for calibration and prediction set, respectively, and the results showed the ability of the WNN developed here to predict solvent polarity.
Kobra Zarei; Morteza Atabati; Malihe Ebrahimi
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Analytical sciences : the international journal of the Japan Society for Analytical Chemistry     Volume:  23     ISSN:  0910-6340     ISO Abbreviation:  Anal Sci     Publication Date:  2007 Aug 
Date Detail:
Created Date:  2007-08-10     Completed Date:  2007-10-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8511078     Medline TA:  Anal Sci     Country:  Japan    
Other Details:
Languages:  eng     Pagination:  937-42     Citation Subset:  -    
Department of Chemistry, Damghan University of Basic Sciences, Damghan, Iran.
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