Suhane, Smita
2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO -4-METHYL-6- PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DE RIVATIVES AS FACTOR XA INHIBITOR - Vol.11(2) - M P Innovare Academic Sciences Pvt Ltd 2019 - 104-114p.
Objective:
The main objective of the present study was to evol
ve a novel pharmacophore of methaniminium derivativ
es as factor Xa inhibitors by
developing best 2D and 3D QSAR models.
The models were developed for amino (3-((3, 5-diflu
oro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) m
ethaniminium derivatives as factor Xa
inhibitors.
Methods:
With the help of Marvin application, 2D structures
of thirty compounds of methaniminium derivatives we
re 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 ana
lysis was carried out using k-Nearest Neighbour Mol
ecular Field Analysis (k-NN-MFA).
Results:
The most significant 2D models of methaniminium der
ivatives calculated squared correlation coefficient
value 0.8002 using multiple linear
regression (MLR) analysis. Partial Least Square (PL
S) 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 Mol
ecular Field Analysis have predicted q
2
value 0.8790, q2_
se
value 0.0794, pred r
2
value 0.9340
and pred_r
2
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 de
termining the inhibitory activity of this class of
compounds.
Conclusion:
The developed 2D and 3D QSAR models have shown good
r
2
and q
2
values of 0.8002 and 0.8790 respectively. There is
high agreement
in inhibitory properties of experimental and predic
ted values, which suggests that derived QSAR models
have good predicting properties.
The contour plots of 3D QSAR (k-NN-MFA) method furn
ish additional information on the relationship betw
een the structure of the compound and
their inhibitory activities which can be employed t
o construct newer potent factor Xa inhibitors.
PHARMACEUTICS
2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO -4-METHYL-6- PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DE RIVATIVES AS FACTOR XA INHIBITOR - Vol.11(2) - M P Innovare Academic Sciences Pvt Ltd 2019 - 104-114p.
Objective:
The main objective of the present study was to evol
ve a novel pharmacophore of methaniminium derivativ
es as factor Xa inhibitors by
developing best 2D and 3D QSAR models.
The models were developed for amino (3-((3, 5-diflu
oro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) m
ethaniminium derivatives as factor Xa
inhibitors.
Methods:
With the help of Marvin application, 2D structures
of thirty compounds of methaniminium derivatives we
re 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 ana
lysis was carried out using k-Nearest Neighbour Mol
ecular Field Analysis (k-NN-MFA).
Results:
The most significant 2D models of methaniminium der
ivatives calculated squared correlation coefficient
value 0.8002 using multiple linear
regression (MLR) analysis. Partial Least Square (PL
S) 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 Mol
ecular Field Analysis have predicted q
2
value 0.8790, q2_
se
value 0.0794, pred r
2
value 0.9340
and pred_r
2
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 de
termining the inhibitory activity of this class of
compounds.
Conclusion:
The developed 2D and 3D QSAR models have shown good
r
2
and q
2
values of 0.8002 and 0.8790 respectively. There is
high agreement
in inhibitory properties of experimental and predic
ted values, which suggests that derived QSAR models
have good predicting properties.
The contour plots of 3D QSAR (k-NN-MFA) method furn
ish additional information on the relationship betw
een the structure of the compound and
their inhibitory activities which can be employed t
o construct newer potent factor Xa inhibitors.
PHARMACEUTICS