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

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 characterize 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 composition 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(cv)(2)0.62, 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.