• 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




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


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 q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se 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.


Download data is not yet available.


Johnson M, Maggiora GM. Concepts and applications of molecular similarity. Wiley; 1990.

Hansch, Corwin. Exploring QSAR. Washington DC. American Chemical Society; 1995.

Hansch, Corwin. The expanding role of quantitative structure-activity relationships (QSAR) in toxicology. Toxicol Lett 1995;79:45-53.

Hansch, Corwin. Antitumor 1-(X-aryl)-3, 3-dialkyltriazenes. 2. on the role of correlation analysis in decision making in drug modification. Toxicity quantitative structure-activity relationships of 1-(X-phenyl)-3, 3-dialkyltriazenes in mice. J Med Chem 1978;21:574-7.

Bradbury SP. Predicting modes of toxic action from chemical structure: an overview. SAR and QSAR in Environ Res 1994;2:89-104.

Russom CL, SP Bradbury, AR Carlson. Use of knowledge bases and QSARs to estimate the relative ecological risk of agrichemicals: a problem formulation exercise. SAR and QSAR in Environ Res 1995;4:83-95.

Schultz, Terry W, JR Seward. Health-effects related structure–toxicity relationships: a paradigm for the first decade of the new millennium. Sci Total Environ 2000;249:73-84.

Shi Leming M. QSAR models using a large diverse set of estrogens. J Chem Inf Comput Sci 2001;41:186-95.

Tong W. An integrated computational approach for prioritizing potential estrogenic endocrine disruptors. Proceedings of the International Symposium on Environmental Endocrine Disruptors; 1999.

Wen, Yuan Hua, Jacob Kalff, Robert Henry Peters. Pharmacokinetic modeling in toxicology: a critical perspective. Environ Rev 1999;7:1-18.

Furie B, Furie BC. Mechanisms of thrombus formation. New England J Med 2008;359:938–49.

Handin Rl, Kasper DL, Braunwald E, Fauci AS. Harrison's principles of internal medicine. 16th ed. New York, NY. McGraw-Hill Medical Publishing Division; 2005. p. 337-43.

Waldo AL. Anticoagulation: stroke prevention in patients with atrial fibrillation. Med Clin North Am 2008;92:143–59.

Fuster V, Moreno PR, Fayad ZA. Atherothrombosis and high-risk plaque: part I: evolving concepts. J Am Coll Cardiol 2005;46:937–54.

Tapson VF. Acute pulmonary embolism. N Engl J Med 2008;1037–52. http://dx.doi.org/10.1136/heart.85.2.229

Colman RW, VJ Marder, AW Clowes. Overview of coagulation, fibrinolysis, and their regulation. Hemostasis and Thrombosis: Basic Principles and Clinical Practice Philadelphia; 2006. p. 17-20.

Hyers TM. Management of venous thromboembolism: past, present, and future. Arch Intern Med 2003;163:759-68.

Hirsh J, O'donnell M, Weitz JI. New anticoagulants Blood; 2005. p. 105-453.

Weitz JI, Hirsh J, Samama MM. New antithrombotic drugs: American college of chest physicians evidence-based clinical practice guidelines. 8th ed. Chest J 2008;133:234S-56S.

Turpie AG. New oral anticoagulants in atrial fibrillation. Eur Heart J 2008;29:155-65.

Leung LL, Mannucci PM, Landaw SA. Anticoagulants other than heparin and warfarin. Pharmas Guide Hematology; 2014.

Becker RC. Next-generation antithrombin therapies. J Invasive Cardiol 2009;21:179-85.

Franchini M, Mannucci PM. A new era for anticoagulants. Eur J Intern Med 2009;365:562-68.

Böhm M, Sturzebecher J, Klebe G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 1999;42:458-77.

Phillips, Gary. Design, synthesis, and activity of a novel series of factor Xa inhibitors: optimization of arylamidine groups 1, 2. J Med Chem 2002;45:2484-93.

VLife MDS. version 4.3, VLife Sciences Technologies Pvt. Ltd. Pune India; 2008.

K Baumann. An alignment-independent versatile structure descriptor for QSAR and QSPR based on the distribution of molecular features. J Chem Inf Comput Sci 2002;42:26-35.

Balajee R, Dhanarajan MS. 3D QSAR studies of identified compounds as potential inhibitors for anti-hyperglycemic targets. Asian J Pharm Clin Res 2015;8:362-4.

Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron 1980;36:3219-28.

Shen Min. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbour and simulated annealing PLS methods. J Med Chem 2002;45:2811–23.



How to Cite

Suhane, S., A. G. Nerkar, K. Modi, and S. D. Sawant. “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”. International Journal of Pharmacy and Pharmaceutical Sciences, vol. 11, no. 2, Feb. 2019, pp. 104-1, doi:10.22159/ijpps.2019v11i2.21067.



Original Article(s)