Background: Quantitative structure-activity relationship (QSAR) was carried out to study a series of aminooxadiazoles as PIM1 inhibitors having pki ranging from 5.59 to 9.62 (k i in nM). The present study was performed using Genetic Algorithm method of variable selection (GFA), multiple linear regression analysis (MLR) and non-linear multiple regression analysis (MNLR) to build unambiguous QSAR models of 34 substituted aminooxadiazoles toward PIM1 inhibitory activity based on topological descriptors.
Results: Results showed that the MLR and MNLR predict activity in a satisfactory manner. We concluded that both models provide a high agreement between the predicted and observed values of PIM1 inhibitory activity. Also, they exhibit good stability towards data variations for the validation methods. Furthermore, based on the similarity principle we performed a database screening to identify putative PIM1 candidates inhibitors, and predict their inhibitory activities using the proposed MLR model.
Conclusions: This approach can be easily handled by chemists, to distinguish, which ones among the future designed aminooxadiazoles structures could be lead-like and those that couldn't be, thus, they can be eliminated in the early stages of drug discovery process.
Keywords: Aminooxadiazoles; Applicability domain; MLR; PIM1; QSAR model; Virtual screening.