Quantitative structure–retention relationship study of analgesic drugs by application of combined data splitting-feature selection strategy and genetic algorithm-partial least square

2012 ◽  
Vol 9 (1) ◽  
pp. 53-60 ◽  
Author(s):  
Bahram Hemmateenejad ◽  
Katayoun Javidnia ◽  
Ramin Miri ◽  
Maryam Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Partial Least Square ◽  
Least Square ◽  
Selection Strategy ◽  
Quantitative Structure ◽  
Analgesic Drugs ◽  
Structure Retention ◽  
Relationship Study ◽  
Quantitative Structure Retention Relationship ◽  
Combined Data

Quantitative Structure–Retention Relationship Study of Some 5-Substituted-5-Phenylhydantoins

2011 ◽  
Vol 73 (1-2) ◽  
pp. 51-57 ◽  
Author(s):  
Tatjana Djaković-Sekulić ◽  
Zagorka Lozanov-Crvenković ◽  
Anamarija Mandić ◽  
Gordana Uščumlić ◽  
Svetlana Keleman
Keyword(s):  
Quantitative Structure ◽  
Structure Retention ◽  
Relationship Study ◽  
Quantitative Structure Retention Relationship

scholarly journals A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of Ammoides atlantica

2011 ◽  
Vol 76 (6) ◽  
pp. 891-902 ◽  
Author(s):  
Aberomand Azar ◽  
Mehdi Nekoei ◽  
Siavash Riahi ◽  
Mohamad Ganjali ◽  
Karim Zare
Keyword(s):  
Genetic Algorithm ◽  
Essential Oil ◽  
Essential Oils ◽  
Retention Indices ◽  
Quantitative Structure ◽  
Data Set ◽  
Prediction Of Retention ◽  
Structure Retention ◽  
Oil Components ◽  
Quantitative Structure Retention Relationship

A simple, descriptive and interpretable model, based on a quantitative structure-retention relationship (QSRR), was developed using the genetic algorithm-multiple linear regression (GA-MLR) approach for the prediction of the retention indices (RI) of essential oil components. By molecular modeling, three significant descriptors related to the RI values of the essential oils were identified. A data set was selected consisting of the retention indices for 32 essential oil molecules with a range of more than 931 compounds. Then, a suitable set of the molecular descriptors was calculated and the important descriptors were selected with the aid of the genetic algorithm and multiple regression method. A model with a low prediction error and a good correlation coefficient was obtained. This model was used for the prediction of the RI values of some essential oil components which were not used in the modeling procedure.

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