摘要
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。
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 （R^2=0.834, SD=0.768, F=33.875）; Rcv=0.877 （Rcv^2=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 （R^2=0.967） and Roy=0.918 （Rcv^2=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 （Rcv^2= 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.
出处
《化学学报》
SCIE
CAS
CSCD
北大核心
2008年第18期2052-2058,共7页
Acta Chimica Sinica
基金
国家高技术研究发展计划(863计划)专题(No.2006AA02Z312)
重庆大学研究生创新团队项目科技创新基金(No.200711C1A0010260)资助项目
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
ACE inhibitor
chiral
Vmedc
N-alkylated-3-（3-hydroxyphenyl）piperidine
backpropagation neural network
linear discriminant analysis
