A quantitative structure–property relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the ion-molecules rate constants for proton transfer reaction between hydronuim ion (H3O+) and some important volatile organic compounds (VOCs). A collection of 50 VOCs was chosen as data set that was randomly divided into three groups, training, internal and external test sets consist of 40, 5 and 5 molecules, respectively. A total of five independent variables selected by stepwise multilinear regression are electronic, geometric, topological type descriptors. The ANN model was developed by using the five descriptors appearing in the MLR model as inputs. Among developed models, the best QSPR model was the ANN model that produced a reasonable level of mean square error MSEtrain = 0.021, MSEexternal = 0.186, MSEinternal = 0.110. The rate constants calculated by this model are in very good agreement with experimental values. The result of this study reveals the applicability of QSPR approaches in prediction of ion-molecules rate constants for proton transfer reaction of VOCs from their molecular structural descriptors
