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Study on the antagonists for the orphan G protein-coupled receptor GPR55 by quantitative structure-activity relationship

机译:定量构效关系研究孤儿G蛋白偶联受体GPR55的拮抗剂

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The orphan G protein-coupled receptor GPR55 has been proposed as a new potential drug target for the treatment of diabetes, Parkinson's disease, neuropathic pain, and cancer. Coumarin and 8-amido-chromen-4-one-2-carboxylic acid derivatives were identified as novel antagonists for GPR55. In this study, we established reliable models and explored the valuable information by quantitative structure-activity relationship (QSAR). Firstly, we obtained a quite reliable multiple linear regression (MLR) model with correlation coefficient (R~2) of 0.8204 for the training set, and R~2 of 0.7770 for the test set. Next, we built a better model with R~2 of 0.8763 for the training set, and R~2 of 0.8179 for the test set based on the simplified molecular input line entry system (SMILES). Lastly, we established dependable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. Then we validated these models' predictive ability using various validation methods for the test set. Besides, we designed and confirmed six new potential inhibitors with higher predicted activities and tested twenty-eight compounds without clear activities by the established models. The results obtained from molecular modeling not only provide models to predict the activities of inhibitors but also lead to a better understanding of the essential features that should be considered when designing novel inhibitors with desired activities.
机译:孤儿G蛋白偶联受体GPR55已被提议作为治疗糖尿病,帕金森氏病,神经性疼痛和癌症的新的潜在药物靶标。香豆素和8-酰胺基-色烯-4-一-2-羧酸衍生物被鉴定为GPR55的新型拮抗剂。在这项研究中,我们建立了可靠的模型,并通过定量构效关系(QSAR)探索了有价值的信息。首先,我们获得了一个非常可靠的多元线性回归(MLR)模型,训练集的相关系数(R〜2)为0.8204,测试集的相关系数(R〜2)为0.7770。接下来,我们基于简化的分子输入线输入系统(SMILES)建立了一个更好的模型,训练集的R〜2为0.8763,测试集的R〜2为0.8179。最后,我们建立了可靠的比较分子场分析(CoMFA)和比较分子相似性指标分析(CoMSIA)模型。然后,我们使用测试集的各种验证方法验证了这些模型的预测能力。此外,我们设计并确认了六种具有较高预测活性的新型潜在抑制剂,并通过已建立的模型测试了28种没有明确活性的化合物。从分子模型获得的结果不仅提供了预测抑制剂活性的模型,而且还使人们更好地了解了设计具有所需活性的新型抑制剂时应考虑的基本特征。

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