Open AccessApplication of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors
Author(s) -
Zhila Mohajeri Avval,
Eslam Pourbashir,
Mohammad Reza Ganjali,
Parviz Norouzi
Publication year - 2014
Publication title -
journal of the serbian chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 45
eISSN - 1820-7421
pISSN - 0352-5139
DOI - 10.2298/jsc140523064a
Subject(s) - predictability , quantitative structure–activity relationship , robustness (evolution) , linear regression , feature selection , stepwise regression , indole test , mathematics , linear model , stability (learning theory) , biological system , algorithm , computer science , artificial intelligence , chemistry , machine learning , stereochemistry , statistics , biology , biochemistry , gene
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-α] indole, diazepino [1,2-α] 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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