基团定位指数用于橙酮类DRAK2抑制剂活性的研究

Research on Activity of Aurone DRAK2 Inhibitor Using Group Orientation Index

DRAK2激酶在多种细胞死亡信号通路中发挥着关键的作用,为研究橙酮类DRAK2抑制剂的抑制活性与其分子结构之间的定量构效关系,根据橙酮类DRAK2抑制剂分子中原子的连接特性,提出了一种新的结构指数——基团定位指数(D),并分别计算了59个橙酮类DRAK2抑制剂分子的电性距离矢量值,筛选了电性距离矢量中的M_(2)、M_(3)、M_(9)、M_(14)、M_(15)、M_(21)和M_(33)作为理论结构的描述符,将这几个结构指数与基团定位指数(D)结合,与橙酮类DRAK2抑制剂的抑制活性进行回归分析,将8种参数作为神经网络方法的输入节点层的输入参数,采用8-4-1的神经网络的网络结构,构建了一种相关性较好的预测橙酮类DRAK2抑制剂抑制活性的神经网络法模型,预测模型的方程总相关系数(r总)达到0.9931,计算得到的抑制活性预测值与实验值(pIC50)之间的相对平均误差只有1.12%;对模型进行稳定性和离域性检验,效果良好;与别的模型相比,本模型具有多方面的优越性。结果表明,橙酮类DRAK2抑制剂分子的抑制活性与基团定位指数、电性距离矢量等结构指数具有良好的非线性关系。

DRAK2 plays a critical role in multiple apoptosis signaling pathways.In order to study the quantitative structure-activity relationship(QSAR)between the inhibited activity of aurone DRAK2 inhibitor and its molecular structure,a novel structure index—group orientation index(D)was derived based on connectivity of the atoms in aurone DRAK 2 inhibitor molecules.Moreover,the electrical distance vectors of 59 aurone DRAK2 inhibitor mole-cules were calculated,and the electrical distance vectors M2,M3,M9,M14,M15,M21 and M33 were taken as de-scriptors of theoretical structure.The electrical distance vectors were combined with group orientation index D,and they were introduced to the regression analysis of the inhibited activity of aurone DRAK2 inhibitor.Using the eight molecular parameters in the input node layer of the neural network method,and using 8-4-1 as network structure,a neural network model which predicts the inhibited activity of aurone DRAK2 inhibitors was constructed.The total correlation coefficient(rT)was 0.9931.The average relative error between the experimental value and the predicted value of inhibited activity(pIC50)was 1.12%.The stability and delocalization tests of the model showed good re-sults.Compared to other models,this model has many advantages.The results showed there was good non-linear relationship between the group orientation index,electrical distance vectors and the inhibited activity of aurone DRAK2 inhibitor molecules.