Background and Objective: The development of pharmacologically active molecules
for the treatment of hypertension and other cardiovascular diseases are important nowadays. In the
present investigation, computational techniques have been implemented on Angiotensin II Type 1
(AT1) antagonists to develop better predictive models.
Methods: Quantitative Structure Activity Relationship (QSAR) and structural patterns/fragments
analyses were performed using physicochemical descriptors and MACCS Fingerprints calculaced
from AT1 inhibitors collected from the literature.
Results: The significant models developed have been validated by Leave One Out (LOO) and test
set methods, which exhibit considerable Q2 values (>0.65 for the training set and >0.5 for the test
set) and the R2pred values for the models are also >0.5. The applicability of the contributed descriptors
in these models revealed that the chlorine atom, dipole moment, hydrogen bond donor atoms
and electrostatic potential are negatively contributing, and the presence of bond between
heavy atoms and the carbon atom connected with small side chain and topological polar vdW surface
area are favorable for the AT1 antagonistic activity. The MACCS Fingerprints showed that the
presence of atoms (kind of heavy atoms), such as N, O, and S, connected with other heteroatoms or carbon
or any other atoms, through single or double bonds are predominantly present in highly active molecules.
The presence of halogens, long chain alkanes, halogenated alkanes, and sulfur atoms attached with
nitrogen through any atoms are responsible for decreased AT1 antagonistic activity.
Conclusion: The results have provided additional information on the structural patterns of the
compounds based on its MACCS Fingerprints, which may be used for further characterization and
design of novel AT1 inhibitors.