Evidence-based successful example of a structure-based approach for the prediction of designer fentanyl-like molecules

Giuseppe Floresta , Valeria Catalani , Vincenzo Abbate
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引用次数: 0

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.

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
2019年,我们发表了三个创新的定量结构-活性关系模型(QSAR),用于预测μOR受体(μOR)配体的亲和力。随后,这三个不同的模型被组合在一起,产生了一个共识模型,用于探索 3000 个类似芬太尼的虚拟结构的化学景观,这些虚拟结构也是我们通过理论支架跳转方法生成的,用于探索潜在的新型活性物质并预测其活性。有趣的是,五年过去了,一些虚拟预测化合物已被确认/报告给欧盟预警系统或联合国毒品和犯罪问题办公室等机构,从而证实了我们的预警假设,即我们筛选出的新出现药物将进入市场。
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来源期刊
Emerging trends in drugs, addictions, and health
Emerging trends in drugs, addictions, and health Pharmacology, Psychiatry and Mental Health, Forensic Medicine, Drug Discovery, Pharmacology, Toxicology and Pharmaceutics (General)
CiteScore
2.40
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0.00%
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