title: 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods.
abstract: Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models.
authors.affiliation: Drug Applied Research center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Ghanbarzadeh
authors.affiliation: Department of Medicinal Chemistry, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Ghasemi
authors.affiliation: Department of Medicinal Chemistry, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Shayanfar
authors.affiliation: Department of Medicinal Chemistry, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran.
authors.forename: Heshmatollah
authors.initials: H
authors.lastname: Ebrahimi-Najafabadi
keywords: QSAR; artificial neural network; multiple linear regression; support vector machine