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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

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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.

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Correspondence to Hiua Daraei.

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Maleki, A., Daraei, H., Alaei, L. et al. 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. Russ J Bioorg Chem 40, 61–75 (2014). https://doi.org/10.1134/S106816201306006X

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Keywords

  • human carbonic anhydrase II
  • dissociation constants
  • QSAR
  • genetic algorithm
  • artificial neural network