Background:
Coronary heart disease generally occurs due to cholesterol accumulation in
the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the
active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for
cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been
synthesized experimentally which would be expensive and time-consuming.
Methods:
In the present study, we employed both the QSAR model and chemical similarity search
for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR
model was developed by correlating the structural properties to their biological activity of a series
of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of
atorvastatin analogs was performed by using PubChem database search.
Results and Discussion:
The three-descriptor model of charge (GATS1p), connectivity (SCH-7)
and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values
of R2= 0.67, RMSEtr= 0.33, R2
ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained
by chemical similarity search and the inhibition activities of the compounds were predicted
using QSAR model, which were close in the range of experimentally observed threshold.
Conclusion:
The present study suggests that the QSAR model and chemical similarity search could
be used in combination for identification of novel compounds with activity by in silico with less
computation and effort.
Subsets and Supermajorities: Optimal Hashing-based Set Similarity Search
