Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
Int J Mol Sci. 2019 May 10;20(9):2311. doi: 10.3390/ijms20092311.
Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) 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 OR ligands with fentanyl-like structures.
已开发出三个用于预测μ-阿片受体 (OR) 配体亲和力的定量构效关系 (QSAR) 模型。这些模型利用 QSAR 建模的可及性，为研究和鉴定未分类的芬太尼样结构生成了有用的工具。模型使用 Forge 软件构建了 115 个分子的数据集，并通过统计分析确认了其质量，从而证明了其在预测和描述能力方面的有效性。然后，将这三种不同的方法结合起来生成共识模型，并利用该模型探索了通过理论支架跳跃方法生成的 3000 个芬太尼样结构的化学景观。这项研究的结果应该有助于识别和分类具有芬太尼样结构的新 OR 配体。
