General structure-activity/selectivity relationship patterns for the inhibitors of the chemokine receptors (CCR1/CCR2/CCR4/CCR5) with application for virtual screening of PubChem database

Abstract

CC chemokine receptors (CCRs) form a crucial subfamily of G protein-linked receptors that play a distinct role in the onset and progression of various life-threatening diseases. The main aim of this research is to derive general structure-activity relationship (SAR) patterns to describe the selectivity and activity of CCR inhibitors. To this end, a total of 7332 molecules related to the inhibition of CCR1, CCR2, CCR4, and CCR5 were collected from the Binding Database and analyzed using machine learning techniques. A diverse set of 450 molecular descriptors was calculated for each molecule, and the molecules were classified based on their therapeutic targets and activities. The variable importance in the projection (VIP) approach was used to select discriminatory molecular features, and classification models were developed using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN). The reliability and predictability of the models were estimated using 10-fold cross-validation, an external validation set, and an applicability domain approach. We were able to identify different sets of molecular descriptors for discriminating between active and inactive molecules and model the selectivity of inhibitors towards different CCRs. The sensitivities of the predictions for the external test set for the SKN models ranged from 0.827–0.873. Finally, the developed classification models were used to screen approximately 2 million random molecules from the PubChem database, with average values for areas under the receiver operating characteristic curves ranging from 0.78–0.96 for SKN models and 0.75–0.89 for CPANN models.

Communicated by Ramaswamy H. Sarma

Keywords:

• CC chemokine receptors
• chemical space
• classification
• PubChem
• virtual screening

Acknowledgments

The authors would like to thank Tarbiat Modares University for the financial support of this project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.