dc.contributor.author | Floresta, Giuseppe | |
dc.contributor.author | Catalani, Valeria | |
dc.contributor.author | Abbate, Vincenzo | |
dc.date.accessioned | 2024-03-25T13:34:09Z | |
dc.date.available | 2024-03-25T13:34:09Z | |
dc.date.issued | 2024-12 | |
dc.identifier.citation | Floresta , G , Catalani , V & Abbate , V 2024 , ' Evidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like molecules ' , Emerging Trends in Drugs, Addictions and Health , vol. 4 , 100143 , pp. 1-6 . https://doi.org/10.1016/j.etdah.2024.100143 | |
dc.identifier.issn | 2667-1182 | |
dc.identifier.other | Jisc: 1822935 | |
dc.identifier.uri | http://hdl.handle.net/2299/27645 | |
dc.description | © 2024 The Author(s). Published by Elsevier Ltd on behalf of International Society for the Study of Emerging Drugs. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/ | |
dc.description.abstract | In 2019, we published three innovative quantitative structure-activity relationship models (QSAR) for predicting the affinity of mu-opioid receptor (µOR) ligands. The three different models were then combined to produce a consensus model used to explore the chemical landscape of 3000 virtual fentanyl-like structures, also generated by us by a theoretical scaffold-hopping approach to explore potential novel active substances and predict their activity. Interestingly, five years have passed, and some of the virtual predicted compounds have been identified/reported to e.g. the EU Early Warning System or the United Nations Office on Drugs and Crime, thus confirming our warning hypothesis that new emerging drugs from our screen would find way to the market. | en |
dc.format.extent | 6 | |
dc.format.extent | 708326 | |
dc.language.iso | eng | |
dc.relation.ispartof | Emerging Trends in Drugs, Addictions and Health | |
dc.subject | Designer fentanyl-like molecules | |
dc.subject | Fentanyl | |
dc.subject | New psychoactive substances | |
dc.subject | Novel synthetic opioids | |
dc.subject | Opioid binding affinity | |
dc.subject | QSAR | |
dc.subject | µOR | |
dc.subject | Drug Discovery | |
dc.subject | Psychiatry and Mental health | |
dc.subject | Clinical Psychology | |
dc.subject | Medicine (miscellaneous) | |
dc.subject | Pharmacology | |
dc.title | Evidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like molecules | en |
dc.contributor.institution | School of Life and Medical Sciences | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85187364842&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1016/j.etdah.2024.100143 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |