Combined QSAR model and chemical similarity search for novel HMG-CoA reductase inhibitors for coronary heart disease.

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ID: 42010
2019
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Ranked #10 of 126 articles by views in current computer-aided drug design

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Abstract
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. Series of atorvasatin analogs with HMGCR inhibition activity have been synthesised experimentally which would be expensive and time consuming.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 atoravastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search.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, R2ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarily search and the inhibition activities of the compounds were calculated using QSAR model, which were close in the range of experimentally observed threshold.The present study suggests that 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.
Reference Key
rajathei2019combinedcurrent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rajathei, David Mary;Parthasarathy, Subbiah;Selvaraj, Samuel;
Journal current computer-aided drug design
Year 2019
DOI
10.2174/1573409915666190904114247
URL
Keywords Keywords not found

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