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Molecules 2011, 16(3), 1928-1955; doi:10.3390/molecules16031928
Article

QSAR Models for CXCR2 Receptor Antagonists Based on the Genetic Algorithm for Data Preprocessing Prior to Application of the PLS Linear Regression Method and Design of the New Compounds Using In Silico Virtual Screening

1, 1, 1, 2,*  and 2
Received: 4 January 2011 / Revised: 31 January 2011 / Accepted: 15 February 2011 / Published: 25 February 2011
(This article belongs to the Section Medicinal Chemistry)
Download PDF [350 KB, 18 June 2014; original version 18 June 2014]

Abstract

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC50 of CXCR2 receptors for which no experimental data is available.
Keywords: QSAR; CXCR2 receptor; in silico screening; estimation of pIC50 QSAR; CXCR2 receptor; in silico screening; estimation of pIC50
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Asadollahi, T.; Dadfarnia, S.; Shabani, A.M.H.; Ghasemi, J.B.; Sarkhosh, M. QSAR Models for CXCR2 Receptor Antagonists Based on the Genetic Algorithm for Data Preprocessing Prior to Application of the PLS Linear Regression Method and Design of the New Compounds Using In Silico Virtual Screening. Molecules 2011, 16, 1928-1955.

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