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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 exter-nal 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 exteal 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,exteal 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.