Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.
Int J Mol Sci 2019;
20:ijms20092311. [PMID:
31083294 PMCID:
PMC6539757 DOI:
10.3390/ijms20092311]
[Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 12/22/2022] Open
Abstract
Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (μOR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new μOR ligands with fentanyl-like structures.
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