title: Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.
abstract: P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.
authors.affiliation: Student Research Committee, Kermanshah University of Medical Sciences, Kermanshah, I.R. Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Ahmadi
authors.affiliation: Meical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, I.R. Iran.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Shahlaei
keywords: Artificial neural network (ANN); Genetic algorithm (GA); P2X7 receptor antagonists; Principal component analysis (PCA); QSAR