Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors  

作　　者：Pei-pei DONG Yan-yan ZHANG Guang-bo GE Chun-zhi AI Yong LIU Ling YANG Chang-xiao LIU 

机构地区：[1]Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China [2]Graduate School of Chinese Academy of Sciences, Beijing 100049, China [3]Tianjin Key Laboratory of Pharmacodynamics and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, China

出　　处：《Acta Pharmacologica Sinica》2008年第3期385-396,共12页中国药理学报（英文版）

基　　金：Project supported by the National Natural Science Foundation of China （No 30640066 and 30630075）, and the Innovation Youth Foundation of Dalian Institute of Chemical Physics （No_ S200612）.

摘　　要：Aim： To develop an artificial neural network model for predicting the resistance index （RI） of taxoids. Methods： A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state （E-state） indices were calculated to charac- terize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composi- tion in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. Results： The final model proved to be good with the cross-validation Q^2cv0.62, external testing R^2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933. Conclusion： The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.Aim： To develop an artificial neural network model for predicting the resistance index （RI） of taxoids. Methods： A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state （E-state） indices were calculated to charac- terize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composi- tion in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. Results： The final model proved to be good with the cross-validation Q^2cv0.62, external testing R^2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933. Conclusion： The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.

关 键 词：artificial neural network model TAXOIDS multidrug resistance resistance index electrotopological state indices principle component analysis quantitative structureactivity relationship 

分 类 号：R962[医药卫生—药理学]