Abstract

Shubham Roy¹, Ratul Bhowmik², Sounok Sengupta^3*, Sameer Sharma⁴, Bharti Vyas⁵ and Imran A Khan⁶

DOI : http://dx.doi.org/10.13005/ojc/370502

DPP-IV rapidly degrades glucagon-like peptide-1 and glucose-dependent insulinotropic peptides. Delaying the breakdown of endogenous incretin hormones with DPP-IV inhibitors may help correct the physiologic deficit. The purpose of this work is to identify new compounds that inhibit the DPP-IV enzyme. The anticipated compounds were potent anti-diabetic candidates in this investigation. Two 2d QSAR models were created using 179 different substances from diverse sources. QSAR models were created using two methods. The first technique included docking score as an additional descriptor, while the second did not. Docking-based QSAR considered 74 compounds out of 179. Another approach used 40 molecules from 179 compounds. Each method had a precise strategy. Descriptors were computed using DRAGON for both training and test sets. Using DRAGON data, SYSTAT generated regression curves. The docking-based QSAR model produced R2=0.7098 (training set) and R2=0.9987 (test set), whereas the other technique produced R2=0.7644 (training set) and R2=0.9857 (test set).

Docking; Dragon; DPP IV; Molecular Dynamics Simulations; QSAR; SYSTAT

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