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dc.contributor.authorFloresta, Giuseppe
dc.contributor.authorCatalani, Valeria
dc.contributor.authorAbbate, Vincenzo
dc.date.accessioned2024-03-25T13:34:09Z
dc.date.available2024-03-25T13:34:09Z
dc.date.issued2024-12
dc.identifier.citationFloresta , 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.issn2667-1182
dc.identifier.otherJisc: 1822935
dc.identifier.urihttp://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.abstractIn 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.extent6
dc.format.extent708326
dc.language.isoeng
dc.relation.ispartofEmerging Trends in Drugs, Addictions and Health
dc.subjectDesigner fentanyl-like molecules
dc.subjectFentanyl
dc.subjectNew psychoactive substances
dc.subjectNovel synthetic opioids
dc.subjectOpioid binding affinity
dc.subjectQSAR
dc.subjectµOR
dc.subjectDrug Discovery
dc.subjectPsychiatry and Mental health
dc.subjectClinical Psychology
dc.subjectMedicine (miscellaneous)
dc.subjectPharmacology
dc.titleEvidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like moleculesen
dc.contributor.institutionSchool of Life and Medical Sciences
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85187364842&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.etdah.2024.100143
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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