title: Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes.
abstract: A quantitative structure-property relationship study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the retention behavior of some terpenes on the polar stationary phase (Carbowax 20 M). A collection of 53 noncyclic and monocyclic terpenes was chosen as data set that was randomly divided into two groups, a training set and a prediction set consist of 41 and 12 molecules, respectively. A total of six descriptors appearing in the MLR model consist of one electronic, two geometric, two topological and one physicochemical descriptors. Except for the geometric parameters the remaining descriptors have a pronounced effect on the retention behavior of the terpenes. A 6-5-1 ANN was generated by using the six descriptors appearing in the MLR model as inputs. The mean of relative errors between the ANN calculated and the experimental values of the Kováts retention indexs for the prediction set was 1.88%. This is in aggreement with the relative error obtained by experiment.
authors.affiliation: Sharif University of Technology, Department of Chemistry, Tehran, Iran. jalali@sina.sharif.ac.ir
authors.forename: M
authors.initials: M
authors.lastname: Jalali-Heravi
authors.forename: M H
authors.initials: MH
authors.lastname: Fatemi
mesh_terms: D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D013729:Terpenes / Q000737:chemistry*
chemical_list: D013729:Terpenes