Application of genetic algorithm-Multiple linear regression for prediction of dopamine receptor 4 (D4R) antagonists of alkoxymethyl morpholines

Document Type : Original Article

Authors

• Samira Masoomi Aladezgeh ¹
• Haniye Ghaffari Jajin ¹
• Eslam Pourbasheer ²

¹ Department of Chemistry, Faculty of Science, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil. Iran

² Department of Chemistry, Faculty of Science, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran

Abstract

In this research, by using the structural descriptors and multiple linear regression method, the quantitative structure-activity relationship studies have been carried out to predict the dopamine 4 receptors activity of, alkoxyphenylmorpholine derivatives. Appropriate descriptors were selected using the genetic algorithm method. Then a simple and strong model with a high correlation coefficient was built. The results showed that the linear techniques such as multiple linear regression coupled with a suitable variable selection method are able to provide suitable models for predicting the activity of compounds. The values of correlation coefficient (R2) and root mean square error (RMSE) for the training set were 0.729 and 0.285, respectively, and for the test set, they were 0.820 and 0.237, respectively. The presented model showed high statistical parameters that can be used to predict the activity of same compounds.

Keywords

• QSAR
• Alkoxymethyl morpholines
• MLR
• Genetic algorithms

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