King's College London

Research portal

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

Research output: Contribution to journalArticle

Giuseppe Floresta, Antonio Rescifina, Vincenzo Abbate

Original languageEnglish
Article number2311
JournalInternational Journal of Molecular Sciences
Issue number9
Publication statusPublished - 10 May 2019


King's Authors


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.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454