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摘要
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 or-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); R-cv=0.877 (R-cv(2) =0.769, SDcv=0.906, F-cv=22.473). Furthermore, average correlation coefficients (R) for random 60 groups with 23 training compounds for all the 32 ACE 0 stereoisomers by backpropagation neural network (BPNN) were R-tr=0.931 (R-tr(2)=0.967) and R-cv=0.918 (R-cv(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 R-cv=0.955 (R-cv(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.
- 出版日期2008-9-28
- 单位四川轻化工大学; 重庆大学
