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Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.

Background: Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results: Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions: The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. [ABSTRACT FROM AUTHOR]