Original Article

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

Pei-pei Dong, Yan-yan Zhang, Guang-bo Ge, Chun-zhi Ai, Yong Liu, Ling Yang, Chang-xiao Liu


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 Q2cv0.62, external testing R2 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.