Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
Molecules. 2018 Aug 30;23(9):2183. doi: 10.3390/molecules23092183.
Two 3D quantitative structure⁻activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB₁ and CB₂) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB₁ and CB₂ ligands. A set of 312 molecules have been used to build the model for the CB₁ receptor, and a set of 187 molecules for the CB₂ receptor. All of the molecules were recovered from the literature among those possessing measured values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB₁ and CB₂ ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB₁ and CB₂ ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB₁ and CB₂ receptors.
已经建立了两个用于预测大麻素受体 1 和 2（CB₁和 CB₂）配体的三维定量构效关系（3D-QSAR）模型，这是为了设计和优化 CB₁和 CB₂配体的药物而创建的实用工具。使用了一组 312 个分子来为 CB₁受体建立模型，使用了一组 187 个分子来为 CB₂受体建立模型。所有分子均从文献中回收，其中包含已测量的 值，并且使用 Forge 作为软件。该模型显示出高且稳健的预测潜力，这得到了统计分析质量的证实，并且具有足够的描述能力。通过构建 3D 图谱，可以直观地理解突出 CB₁和 CB₂配体主要相互作用的疏水性、静电和形状特征。然后，使用该模型的预测能力对两种选定化合物进行了支架跳跃研究，生成了对两种受体具有高亲和力的新化合物库。在这里，我们报告了两个新的 3D-QSAR 模型，该模型包含了大量化学性质不同的 CB₁和 CB₂配体，并很好地解释了各个配体的亲和力。这些特征将有助于识别对 CB₁和 CB₂受体具有新的高效和选择性的分子。
