title: pK(a)  modelling and prediction of drug molecules through GA-KPLS and L-M ANN.
abstract: Genetic algorithm and partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between dissociation constant (pK(a) ) and descriptors for 60 drug compounds. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. Descriptors of GA-KPLS model were selected as inputs in L-M ANN model. The results indicate that L-M ANN can be used as an alternative modeling tool for quantitative structure-property relationship (QSPR) studies.
authors.affiliation: Faculty of Science, Islamic Azad University, Ilam Branch, Ilam, Iran. hadinoorizadeh@yahoo.com
authors.forename: H
authors.initials: H
authors.lastname: Noorizadeh
authors.forename: A
authors.initials: A
authors.lastname: Farmany
authors.forename: M
authors.initials: M
authors.lastname: Noorizadeh
mesh_terms: D000465:Algorithms; D002845:Chromatography; D016018:Least-Squares Analysis; D008081:Liposomes / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D008081:Liposomes; D004364:Pharmaceutical Preparations