Document Detail

A QSPR study on the GC retention times of a series of fatty, dicarboxylic and amino acids by MLR and ANN.
MedLine Citation:
PMID:  17970308     Owner:  NLM     Status:  MEDLINE    
Quantitative structure-property relationship (QSPR) analysis has been carried out to a series of fatty, amino and dicarboxylic acids to model their GC retention times. A genetic partial least square method (GAPLS) was applied as a variable selection tool. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The neural network employed here is a connected back-propagation system with a 3-4-1 architecture. Three topological indices for these compounds, namely, mean information index on atomic composition (AAC), average connectivity index chi-0 (X0A) and total information index of atomic composition (IAC) taken as inputs for the regression models. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that ANN has better prediction ability than MLR. The statistical figure of merits of the two models showed the successful modeling of the retention times with molecular descriptors.
Ahmad Rouhollahi; Hooshang Shafieyan; Jahan Bakhsh Ghasemi
Related Documents :
9463948 - Cross-validatory selection of test and validation sets in multivariate calibration and ...
22381538 - A mathematical model of the process of ligament repair: effect of cold therapy and mech...
9046468 - Efficacy of different ovitraps and binomial sampling in aedes albopictus surveillance a...
14632968 - Application of artificial neural network modelling to identify severely ill patients wh...
23012258 - Empirical bayes correction for the winner's curse in genetic association studies.
17479768 - A new technique for ordering asymmetrical three-dimensional data sets in ecology.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Annali di chimica     Volume:  97     ISSN:  0003-4592     ISO Abbreviation:  Ann Chim     Publication Date:  2007 Sep 
Date Detail:
Created Date:  2007-10-31     Completed Date:  2008-01-08     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7610375     Medline TA:  Ann Chim     Country:  Italy    
Other Details:
Languages:  eng     Pagination:  925-33     Citation Subset:  IM    
Chemistry Department, Faculty of Sciences, K. N. Toosi University of Technology, Tehran, Iran.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Amino Acids / chemistry*
Chromatography, Gas / methods*
Dicarboxylic Acids / chemistry*
Fatty Acids / chemistry*
Quantitative Structure-Activity Relationship
Reg. No./Substance:
0/Amino Acids; 0/Dicarboxylic Acids; 0/Fatty Acids

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

Previous Document:  Partitioning study of polycyclic aromatic hydrocarbons between water and some selected water-insolub...
Next Document:  Pre-capillary derivatisation and capillary zone electrophoresis for amino acids analysis in beverage...