Giuseppe Floresta , Valeria Catalani , Vincenzo Abbate
{"title":"用基于结构的方法预测特制芬太尼类分子的成功实证范例","authors":"Giuseppe Floresta , Valeria Catalani , Vincenzo Abbate","doi":"10.1016/j.etdah.2024.100143","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":72899,"journal":{"name":"Emerging trends in drugs, addictions, and health","volume":"4 ","pages":"Article 100143"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667118224000023/pdfft?md5=28e051206ecc436bd0d7ec6bd2516f65&pid=1-s2.0-S2667118224000023-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Evidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like molecules\",\"authors\":\"Giuseppe Floresta , Valeria Catalani , Vincenzo Abbate\",\"doi\":\"10.1016/j.etdah.2024.100143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":72899,\"journal\":{\"name\":\"Emerging trends in drugs, addictions, and health\",\"volume\":\"4 \",\"pages\":\"Article 100143\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2667118224000023/pdfft?md5=28e051206ecc436bd0d7ec6bd2516f65&pid=1-s2.0-S2667118224000023-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Emerging trends in drugs, addictions, and health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667118224000023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Emerging trends in drugs, addictions, and health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667118224000023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like molecules
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.