化学学报 ›› 2008, Vol. 66 ›› Issue (18): 2052-2058. 上一篇    下一篇

研究论文

一种新型手性分子电性矩边矢量(Vmedc)的设计及其应用

陈国华*,a,b 夏之宁*,a 陆 瑶b 廖立敏a
舒 茂a 孙家英a 李志良a

  

  1. (a重庆大学生物工程学院/化学化工学院 重庆 400044)
    (b四川理工学院材料与化学工程系 四川自贡 643000)

  • 收稿日期:2007-12-26 修回日期:2008-04-16 出版日期:2008-09-28 发布日期:2008-09-28
  • 通讯作者: 陈国华

A Vector of Molecular Electronegative Distance for Chiral Compounds (Vmedc) and Its Applications to Codification of Central Chirality

CHEN, Guo-Hua *,a,b XIA, Zhi-Ning *,a LU, Yao b LIAO, Li-Min a
SHU, Mao a SUN, Jia-Ying a LI, Zhi-Liang a
  

  1. (aCollege of Bioengineering/College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044)
    (bDepartment of Materials and Chemical Engineering, Sichuan University of Science and Engineering, Zigong 643000)
  • Received:2007-12-26 Revised:2008-04-16 Online:2008-09-28 Published:2008-09-28
  • Contact: CHEN, Guo-Hua

根据分子中不同类型原子间电相互作用的不同, 文中提出了一种手性分子电矩边矢量(Vmedc), 进一步拓展分子电矩边性矢量(Vmed)使用范围. 为检测该手性描述矢量的结构表达特性和模型预测能力, 分别对32个培哚普利拉类血管紧张素转化酶(ACE)抑制剂的对映结构体和7对苯基哌啶类σ-受体抑制剂进行考察. 32个ACE抑制剂多元逐步回归系数R=0.913 (R2=0.834, SD=0.768, F=33.875), 留一法交互检验为Rcv=0.877 (Rcv2=0.769, SDcv=0.906, Fcv=22.473), 具有较强预测能力; 继而用BP神经网络, 对60组随机样本(23∶9)进行留分法分析取得较好结果, 训练集平均为: RTraining=0.931 (RTraining2=0.967), 预测集为: Rcv=0.918 (Rcv2=0.842); 而对14个σ-受体抑制剂多元回归(R=0.955, Rcv2=0.849)获得与文献一致结果. 再用Fisher线性判别方法和BP神经网络对ACE抑制剂进行判别分析, 其活性分类88.89%正确(仅9号错误), 非活性分类100.0%正确, 总分类正确率为96.87%. 两个数据集测试证明该方法与其它文献方法相当, 这为定量构效关系(QSAR)研究提供一种新选择, 扩充了Vmed描述矢量应用范围.

关键词: ACE抑制剂, 手性, Vmedc, 苯基哌啶类, 反传神经网络, Fisher判别分析

Based on the interaction between different atomic types, Vmedc, a novel vector of molecular electronegative distance (Vmed) has been defined and generalized in order to further codify chemical structural information for chiral drugs. Some quantitative structure-activity relationships (QSAR) have been modeled by Vmedc for both 32 stereoisomers of perindoprilate as angiotensin-converting enzyme ACE inhibitors and 7 pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines that bind σ-receptors. Stepwise linear regression analysis was made forward to the 32 stereoisomers with good modeling results: R=0.913 (R2=0.834, SD=0.768, F=33.875); Rcv=0.877 (Rcv2=0.769, SDcv=0.906, Fcv=22.473). Furthermore, average correlation coefficients (R) for random 60 groups with 23 training compounds for all the 32 ACE stereoisomers by backpropagation neural network (BPNN) were Rtr=0.931 (Rtr2=0.967) and Rcv=0.918 (Rcv2=0.842), except for four groups sampled unreasonably. Compared with literatures, Vmedc has also been applied to obtain good results for 14 samples with correlation coefficient being Rcv=0.955 (Rcv2=0.849). Through both Fisher’ linear discriminant analysis and BPNN, the 32 ACE stereoisomers were classified correctly into 88.89% active with one (#9) wrongly classified, 100.00% nonactive with no wrongly classified, and average classification of 96.87% globally. Good results obtained here were compared to those obtained with other chiral descriptors, when it was applied to the same 2 datasets, which shows that the Vmedc approach provides a powerful alternative QSAR technique for chiral compounds.

Key words: ACE inhibitor, chiral, Vmedc, N-alkylated-3-(3-hydroxyphenyl)piperidine, backpropagation neural network, linear discriminant analysis