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QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Noorizadeh, Hadi Farmany, Abbas Narimani, Hojat Noorizadeh, Mehrab
Description	Country affiliation: Iran Author Affiliation: Noorizadeh H ( Faculty of Science, Islamic Azad University, Ilam Branch, Ilam, Iran. hadinoorizadeh@yahoo.com)
Abstract	A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN.
File Format	HTM / HTML
ISSN	19427603
Issue Number	5
Volume Number	5
e-ISSN	19427611
Journal	Drug Testing and Analysis
Language	English
Publisher	Wiley
Publisher Date	2013-05-01
Publisher Place	Great Britain (UK)
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Pharmacology Chromatography, High Pressure Liquid Methods Hair Chemistry Neural Networks (computer) Pharmaceutical Preparations Analysis Street Drugs Substance Abuse Detection Humans Least-squares Analysis Models, Chemical Journal Article
Content Type	Text
Resource Type	Article
Subject	Spectroscopy Environmental Chemistry Analytical Chemistry Pharmaceutical Science

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QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

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