QSAR study of IKKβ inhibitors by the genetic algorithm: multiple linear regressions

2013 ◽  
Vol 23 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi
Keyword(s):  
Genetic Algorithm ◽  
Multiple Linear Regressions ◽  
Qsar Study ◽  
Linear Regressions

scholarly journals QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions

2015 ◽  
Vol 127 (7) ◽  
pp. 1243-1251 ◽  
Author(s):  
ESLAM POURBASHEER ◽  
SAADAT VAHDANI ◽  
REZA AALIZADEH ◽  
ALIREZA BANAEI ◽  
MOHAMMAD REZA GANJALI
Keyword(s):  
Genetic Algorithm ◽  
Multiple Linear Regressions ◽  
Qsar Study ◽  
Linear Regressions

QSAR study of mGlu5 inhibitors by genetic algorithm-multiple linear regressions

2013 ◽  
Vol 23 (6) ◽  
pp. 3082-3091 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi ◽  
Alireza Banaei
Keyword(s):  
Genetic Algorithm ◽  
Multiple Linear Regressions ◽  
Qsar Study ◽  
Linear Regressions

scholarly journals Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

2015 ◽  
Vol 80 (2) ◽  
pp. 187-196 ◽  
Author(s):  
Zhila Avval ◽  
Eslam Pourbashir ◽  
Mohammad Ganjali ◽  
Parviz Norouzi
Keyword(s):  
Genetic Algorithm ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Stability Robustness ◽  
Comparison Of The Results ◽  
New Compounds ◽  
Linear Regressions ◽  
Mlr Model

This paper deals with developing a linear quantitative structure-activity relationship (QSAR) model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-?] indole, diazepino [1,2-?] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR) technique combined with the stepwise (SW) and the genetic algorithm (GA) methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

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