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

Acta Pharmacol Sin. 2008 Mar;29(3):385-96. doi: 10.1111/j.1745-7254.2008.00746.x.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Breast Neoplasms
  • Cell Line, Tumor
  • Docetaxel
  • Drug Resistance, Multiple / drug effects*
  • Female
  • Humans
  • Inhibitory Concentration 50
  • Models, Molecular
  • Molecular Structure
  • Neural Networks, Computer*
  • Paclitaxel / chemistry
  • Predictive Value of Tests
  • Principal Component Analysis
  • Quantitative Structure-Activity Relationship
  • Reproducibility of Results
  • Software
  • Taxoids / chemistry*
  • Taxoids / classification*

Substances

  • Taxoids
  • Docetaxel
  • Paclitaxel