title: A QSAR study of some cyclobutenediones as CCR1 antagonists by artificial neural networks based on principal component analysis.
abstract: OBJECTIVE
  A quantitative structure activity relationship (QSAR) model based on artificial neural networks (ANN) was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl)-2-oxoethoxy) phenylamino) cyclobutenedione as C-C chemokine receptor type 1(CCR1) inhibitors.
  
  
  METHODS
  A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons.
  
  
  RESULTS
  Good results were obtained with a Root Mean Square Error (RMSE) and correlation coefficients (R(2)) of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively.
  
  
  CONCLUSIONS
  The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.
authors.affiliation: Department of Medicinal Chemistry, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah.
authors.forename: M
authors.initials: M
authors.lastname: Shahlaei
authors.forename: A
authors.initials: A
authors.lastname: Fassihi
authors.forename: L
authors.initials: L
authors.lastname: Saghaie
authors.forename: E
authors.initials: E
authors.lastname: Arkan
authors.forename: A
authors.initials: A
authors.lastname: Pourhossein