title: Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors.forename: Pei-pei
authors.initials: PP
authors.lastname: Dong
authors.forename: Yan-yan
authors.initials: YY
authors.lastname: Zhang
authors.forename: Guang-bo
authors.initials: GB
authors.lastname: Ge
authors.forename: Chun-zhi
authors.initials: CZ
authors.lastname: Ai
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.forename: Ling
authors.initials: L
authors.lastname: Yang
authors.forename: Chang-xiao
authors.initials: CX
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms; D045744:Cell Line, Tumor; D000077143:Docetaxel; D018432:Drug Resistance, Multiple / Q000187:drug effects*; D005260:Female; D006801:Humans; D020128:Inhibitory Concentration 50; D008958:Models, Molecular; D015394:Molecular Structure; D016571:Neural Networks, Computer; D017239:Paclitaxel / Q000737:chemistry; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D012984:Software; D043823:Taxoids / Q000737:chemistry* / Q000145:classification*
chemical_list: D043823:Taxoids; D000077143:Docetaxel; D017239:Paclitaxel