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Docking Based 3D-QSAR Modelling of the Thiophene Analogues as a PTP1B Inhibitors

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Abstract: PTP1B inhibitors have been largely studied because of their promising therapeutic effects, PTP1B dephosphorylate (removal of Phosphate group) the insulin receptor (IR), Insulin Receptor Substrate (IRS), Janus Kinase-2(JAK2) & STAT3 which is present in the downstream of insulin and leptin receptor respectively. PTP1B inhibitors are used for the treatment of diabetes and obesity as a singly drug therapy due to simultaneously acting in both insulin and leptin signalling pathway. The clinical use of PTP1B inhibitors was restricted Selectivity is one of the major issues in the development of PTP1B inhibitors as drugs. Because all PTPs share a high degree of structural conservation in the active site, the pTyr (phosphotyrosine)-binding pocket, The various research results indicate that there are sub pockets adjacent to the PTP active site that can also be targeted for inhibitor development. These studies also provide a molecular basis for addressing and manipulating PTP inhibitor potency and specificity, and suggest a novel paradigm for the design of potent and specific PTP inhibitors Therefore, this work deals with the development of 3D-QSAR models, supported by molecular docking studies, to identify the key requirements underlying selective PTP1B inhibition. Our structure-based effort resulted in significant improvements in potency and selectivity. Docking based alignments and 3D-QSAR study have been performed on 46 molecules based on Thiophene ring scaffold derived from published literature. The QSAR model was selected having highest value of Q2 (0.6005) and Pearson-r (0.8539). The selected model also displayed the highest values of R2 (0.9750) and F-value (179.1) and the lowest SD (0.2230). The contour plots developed on the basis of the best model helped to reveal the essential structural features of Thiophene derivatives responsible for inhibition of PTP1B. The generated model and information revealed from it was utilized to design and predict new congeneric molecules that can be used as potential therapeutic agents.

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