Structure-based approach for the prediction of mu-opioid binding affnity of unclassified designer fentanyl-like molecules

Giuseppe Floresta; Antonio Rescifina; Vincenzo Abbate

doi:10.3390/ijms20092311

Structure-based approach for the prediction of mu-opioid binding affnity of unclassified designer fentanyl-like molecules

Giuseppe Floresta, Antonio Rescifina, Vincenzo Abbate

Universitá di Catania

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16 Citations (Scopus)

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Abstract

Three quantitative structure-activity relationship (QSAR) models for predicting the affnity 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.

Original language	English
Article number	2311
Journal	International Journal of Molecular Sciences
Volume	20
Issue number	9
DOIs	https://doi.org/10.3390/ijms20092311
Publication status	Published - 10 May 2019

Keywords

Designer fentanyl-like molecules
Fentanyl
New psychoactive substances
Novel synthetic opioids
Opioid binding affnity
QSAR
µor

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10.3390/ijms20092311Licence: CC BY

Structure-Based Approach for_FLORESTA_Accepted 8 April 2019_GOLD VoR (CC- BY)Final published version, 5.19 MBLicence: CC BY

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abstract = "Three quantitative structure-activity relationship (QSAR) models for predicting the affnity 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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Structure-based approach for the prediction of mu-opioid binding affnity of unclassified designer fentanyl-like molecules

Abstract

Keywords

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