四氢喹啉类NMDA受体拮抗剂的3D-QSAR研究

3D-QSAR studies on 1,2,3,4-tetradroquinoline derivatives as NMDAR antagonists

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 were used to relieve the pain of patients, especially for cancer pain patients. However, the NMDAR signal pathway can promote the growth and invasion of tumors, which has been reported in a recent paper. In the present study, 3D-QSAR modeling method was used to analysis the structure-activity relationship of NMDAR antagonists and to optimize the structures of antagonists. All the molecules were aligned with the common structures. CoMFA and CoMSIA models were built using 3D-QSAR procedure in Sybyl-X2.1 software. The parameters of the best 3D-QSAR model shown as follow： CoMFA： Q^2=0.691, R^2=0.995,F=511.269, SEE=0.083; CoMSIA： 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 validated coefficient）. It has been proved that the models have good prediction abilities and could make a better guidance for the design and transformation of NMDAR antagonists.