α-Glucosidase is one of the important enzymes in glucose digestion and its inhibitors are known to possess a large number of therapeutic effects. In this present investigation, we have performed structural feature analysis of some of these inhibitors namely, chromenone derivatives using the Molecular Operating Environment (MOE) software. The results of the QSAR study show that the derived models are statistically significant and were validated by external (test set) and internal (leave one out) methods. The crossvalidated correlation coefficients (Q2) of the models show that the training and test sets have the values > 0.6687. The physicochemical descriptors contributed for the models building in training set and complete data set show that the log of aqueous solubility (LogS) and the molar refractivity on the van der Waals surface area of the molecules (SMR_VSA4) positively contributed for the inhibitory activity. Further, the study also reveals that the polarizability and hydrogen bond acceptor/donor groups are important for the α-glucosidase inhibitory activity and these results are in agreement with the earlier studies obtained in our laboratory on α-glucosidase inhibitors which have shows that the polar surface area of the molecule is important for the interaction. The pharmacophore contours of the molecule also showed the importance of the polar surface property on the molecules. This computational analysis will help in the development of novel α-glucosidase inhibitors for various diseases.
Keywords: -glucosidase inhibitors, QSAR, chromenone, LogS, SMR, VSA4, enzymes, glucose digestion, Molecular Operating Environment (MOE), physicochemical descriptors
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