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Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors
Pei-pei Dong1, Yan-yan Zhang, Guang-bo Ge
1Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
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.
