http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
3D-QSAR Studies of 2-Arylbenzoxazoles as Novel Cholesteryl Ester Transfer Protein Inhibitors
Ghasemi, Jahan B.,Pirhadi, Somayeh,Ayati, Mahnaz Korean Chemical Society 2011 Bulletin of the Korean Chemical Society Vol.32 No.2
The 3D-QSAR study of 2-arylbenzoxazoles as novel cholesteryl ester transfer protein inhibitors was performed by comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RF) for optimizing the region for the final PLS analysis, and comparative molecular similarity indices analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The best orientation was searched by all-orientation search strategy using AOS, to minimize the effect of the initial orientation of the structures. The predictive ability of CoMFARF and CoMSIA were determined using a test set of twelve compounds giving predictive correlation coefficients of 0.886, and 0.754 respectively indicating good predictive power. Further, the robustness and sensitivity to chance correlation of the models were verified by bootstrapping and progressive scrambling analyses respectively. Based upon the information derived from CoMFA(RF) and CoMSIA, identified some key features that may be used to design new inhibitors for cholesteryl ester transfer protein.
3D-QSAR Studies of 2-Arylbenzoxazoles as Novel Cholesteryl Ester Transfer Protein Inhibitors
Jahan B. Ghasemi,Mahnaz Ayati,Somayeh Pirhadi 대한화학회 2011 Bulletin of the Korean Chemical Society Vol.32 No.2
The 3D-QSAR study of 2-arylbenzoxazoles as novel cholesteryl ester transfer protein inhibitors was performed by comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RF) for optimizing the region for the final PLS analysis, and comparative molecular similarity indices analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The best orientation was searched by all-orientation search strategy using AOS, to minimize the effect of the initial orientation of the structures. The predictive ability of CoMFARF and CoMSIA were determined using a test set of twelve compounds giving predictive correlation coefficients of 0.886, and 0.754 respectively indicating good predictive power. Further, the robustness and sensitivity to chance correlation of the models were verified by bootstrapping and progressive scrambling analyses respectively. Based upon the information derived from CoMFA(RF) and CoMSIA, identified some key features that may be used to design new inhibitors for cholesteryl ester transfer protein.