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N-甲基-D-天冬氨酸受体(NMDAR)拮抗剂用于治疗患者的疼痛,常用于缓解癌痛,近期文献中报道了NMDAR信号通路可以促进肿瘤生长和侵袭的能力,目的:本文中运用3D-QSAR建模的方法对NMDAR拮抗剂进行构效关系分析并对其化合物结构进行优化改造。方法:基于共同骨架对分子进行叠合,并在此基础上采用Sybyl-X2.1中的三维定量构效关系(3D-QSAR)模块建立了Co MFA和Co MSIA模型。结果:其中,基于公共骨架叠合方法所得3D-QSAR模型的评价参数中最佳结果如下所示,Co MFA:Q~2=0.691,R~2=0.995,F=511.269,SEE=0.083;Co MSIA:Q~2=0.715,R~2=0.998,F=1396.317,SEE=0.051,(Q~2为交叉验证系数,R~2为非交叉验证系数)。结论:数据证明模型具有较好的预测能力,可以较好地指导四氢喹啉类NMDAR拮抗剂的设计和改造,得到活性更好地化合物。
N-methyl-D-aspartate receptor (NMDAR) antagonists for the treatment of pain in patients, commonly used to relieve cancer pain, recently reported in the literature NMDAR signaling pathway can promote tumor growth and invasion ability, the purpose: In this paper, the 3D-QSAR modeling method is used to analyze the structure-activity relationship of NMDAR antagonists and to optimize the structure of the compound. Methods: Based on the common framework, the molecules were superimposed, and based on this, the Co MFA and Co MSIA models were established by using 3D quantitative QSAR (Syndrome-Based Structure-Activity Relationship) in Sybyl-X2.1. Results: Among them, the best results of the evaluation parameters of the 3D-QSAR model based on the public skeleton overlap method are as follows: Co MFA: Q ~ 2 = 0.691, R ~ 2 = 0.995, F = 511.269, SEE = MSIA: Q ~ 2 = 0.715, R ~ 2 = 0.998, F = 1396.317, SEE = 0.051 (Q ~ 2 is the cross validation coefficient, R ~ 2 is the non cross validation coefficient). Conclusion: The data prove that the model has good predictive ability, which can better guide the design and modification of tetrahydroquinoline NMDAR antagonist and get better activity compound.