Abstract
BackgroundQuantitative 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.ResultsResults 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.ConclusionsThe 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 abstractFlow chart of the methodology used in this work.
Highlights
Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors
Data set for analysis A quantitative structure activity relationship (QSAR) study was carried out for the first time on twenty-five of 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amine derivatives, in order to establish quantitative relationships between their structures and their PIM1 and PIM2 inhibitory activities
Multiple linear regressions multiple linear regression analysis (MLR) Based on the selected molecular descriptors two mathematical linear models were proposed to predict quantitatively the physicochemical effects of substituents on the PIM1 and PIM2 inhibitory activities using linear regression
Summary
Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. PIM1, PIM2 and PIM3 (proviral integration site for moloney murine leukaemia virus) kinases form a threemember subgroup of serine/threonine kinases family, which share a high level of sequence homology and exhibit some functional redundancy They attracted recent attention for their potential role in tumorigenesis, tumor cell survival and resistance to antitumor agents, these findings make them an attractive target for cancer therapy [1, 2]. Developing predictive quantitative structure activity relationship (QSAR) models to Aouidate et al Chemistry Central Journal (2017) 11:41 predict the activity of new synthesized or designed PIM inhibitors is highly desired In this context, the QSAR of thiadiazoles still receives considerable attention because these agents represent a large family of multi-biological activity substances and continue to be a source of new drugs as witnessed over recent decades. That prompted us to aim an in silico study based on it, as well as to generalize beyond the data to screen and predict inhibitory activity of other analogues molecules
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