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Updated: Jan 25, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like
Giuseppe Floresta1, Antonio Rescifina2, Vincenzo Abbate
1Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. giuseppe.floresta@unict.it.
Abstract:
Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) 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 OR ligands with fentanyl-like structures.
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