2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

Smita Suhane; A. G. Nerkar; Kumud Modi; Sanjay D. Sawant

doi:10.22159/ijpps.2019v11i2.21067

Authors

Smita Suhane Department of Applied Science, Dr. D.Y. Patil Institute of Technology, Pimpri, Pune 411018 India
A. G. Nerkar Department of Pharmaceutical Chemistry, R.C. Patel Institute of Pharmaceutical Education & Research, Karwand Naka, Shirpur - 425 405 India
Kumud Modi P.M.B Gujarati Science College, 1, Nasia Road, Indore - 452001 India
Sanjay D. Sawant Department of Pharmaceutical Chemistry, Smt. Kashibai Navale College of Pharmacy, Kondhwa- Saswad Road, Kondhwa (Bk.), Pune-411 048

DOI:

https://doi.org/10.22159/ijpps.2019v11i2.21067

Keywords:

QSAR, k-Nearest Neighbour Molecular Field Analysis, amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives, factor Xa

Abstract

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models.

The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors.

Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA).

Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant.

The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q²value 0.8790, q2__se value 0.0794, pred r² value 0.9340 and pred_r²se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds.

Conclusion: The developed 2D and 3D QSAR models have shown good r² and q² values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties.

The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

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