Quantitative structure–activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method

Abstract

A linear quantitative structure–activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.

KEYWORDS::

• QSAR
• Multiple linear regressions
• genetic algorithm
• Principal component analysis
• IRAK-4 inhibition activity
• Chemometrics

Acknowledgements

We gratefully acknowledge generous allocations of computing from the Institute of Petroleum Engineering, University of Tehran for Advanced Computing and Supercomputing Facilities.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.