COMPARISON OF QSAR MODELS BASED ON COMBINATIONS OF GENETIC ALGORITHM, STEPWISE MULTIPLE LINEAR REGRESSION, AND ARTIFICIAL NEURAL NETWORK METHODS TO PREDICT K_d OF SOME DERIVATIVES OF AROMATIC SULFONAMIDES AS CARBONIC ANHYDRASE II INHIBITORS

Afshin Maleki*, Hiua Daraei*^,#, Loghman Alaei**, Aram Faraji***

^#Tel./fax: +98 871 6625131, e-mail: [email protected]

*Kurdistan Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran;
**Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;
***Faculty of Pharmacy, Kermanshah Medical Sciences University, Kermanshah, Iran

Abstract. Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (K_d) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.

Keywords: human carbonic anhydrase II, dissociation constants, QSAR, genetic algorithm, artificial neural network.

Биоорг. химия 2014, 40 (1): 70-84