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

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.

Cytokines in terms of QSAR. Review, evaluation and comparative studies

“…Our 2D-QSAR findings on the role of a) lipophilicity and b) steric properties are supported by the published results: i) 3D-QSAR studies [88,89] and ii) by the conclusions derived from three different modelling methods: SMLR, PLS and GA-PLS [90]. In some cases there was a considerable co-linearity, an overlap between substituent bulk and hydrophobicity (a most common problem in drug development).…”

“…The statistical parameters used to evaluate the prediction ability of the model were root mean square error of prediction, relative error of prediction, standard error of residual in prediction and squared regression coefficient. [90] published their results regarding QSAR models for CXCR2 receptor antagonists by using three different modelling methods: stepwise multiple linear regression (SMLR), partial least squares (PLS) and genetic algorithm coupled with partial least squares (GA-PLS). Quantitative relationships between 130 antagonists of CXCR2 were investigated by the PLS method.…”

Evolutionary Computation and QSAR Research

“…A genetic algorithm was proposed by Asadollahi et al [173] to improve the performance of partial least squares (PLS) modeling. In this work, several methods are compared, demonstrating the superiority of the GA-PLS approach in terms of prediction capacity.…”

In silico design of hydrazone antioxidants and analysis of their free radical-scavenging mechanism by thermodynamic studies

“…The use of mathematical models such as those generated from QSAR studies, for screening new chemical compounds through the technique of ligand-based virtual screening is gaining popularity in the development of novel compounds with improved biological activities [5,6,26]. This research entails a systematic investigation of the free radical-scavenging mechanisms of newly designed hydrazone derivatives by thermodynamic studies.…”