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ISSN 2063-5346
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PHARMACOPHORE BASED 3D QSAR STUDIES OF SOME BIPHENYLS DERIVATIVES AS PROTEIN TYROSINE PHOSPHATES 1B INHIBITORS

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Dwivedi Sunil Kumar1*, Paliwal Sarvesh Kumar2
» doi: 10.48047/ecb/2023.12.si5a.0257

Abstract

Protein tyrosine phosphates (PTPs) are enzymes that catalyze protein tyrosine dephosphorylation. Among various members of the PTP super family, Protein tyrosine phosphates 1B (PTP1B) has emerged as the bestvalidated drug target. Mounting evidence from biochemical, genetic and pharmacological studies support a role for PTP1B as a negative regulator in both insulin and leptin signaling collectively. A computer assisted methodology was applied for the construction of pharmacophore and for building 3D QSAR model of novel benzofuran and benzothiophene biphenyl derivatives as a inhibitors of PTP1B. 3D QSAR model of PTB 1B inhibitor based on common featured pharmacophore was developed using phase module of Schrodinger software. In this study A series of 136 analogues of novel benzofuran and benzothiophene biphenyl derivatives as a inhibitors of Protein Tyrosine Phosphatase 1B and their PTP1B inhibitory data (IC50) was selected and used to develop the Pharmacophore based 3D QSAR model. The statistically best model corresponding to PLS 5 (ARRRR.3895) was selected on the basis of highest values of R2 , 0.9527; SD, 0.1276; and F-value, 297.8. The best model was validated for its stability (Q2 ) and reliability to predict the biological activity of the molecules that have not been used for the development of model i.e. test set molecules (Pearson-r). The model showed good values of Q2 and pearson-r i.e. 0.3655 and 0.611 respectively. The correlation graph between experimental and predicted activities of test and training set molecules was analyze and generated QSAR models should satisfy q2> 0.5, R2 > 0.6, R2 o or R′2 o close to R2 , and the corresponding 0.85≤ k ≤ 1.15 or 0.85≤ k′ ≤1.15. The best model showed satisfactory values of k, 0.998; k ′ , 1.001; R 2 o, 0.999 andR ′2 o, 0.999. These all parameters further strengthen the stability and reliability of generated QSAR model.

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