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• W3091188435 abstract "Cardiovascular diseases are the world's leading cause of death. Hypertension is an important risk factor for cardiovascular and renal diseases. Angiotensin-converting enzyme (ACE) can be a possible therapeutic target for managing angiotensin I conversion to angiotensin II and ultimately controlling hypertension. Indole is an significant fragment used in many medicines approved by FDA. For this reason, the molecules in their fragments containing indol keywords were taken from the Specs-SC (small compound) database. The predicted therapeutc activity values (TAV) of these compounds against hypertension were evaluated using binary models of QSAR by MetaCore/MetaDrug. For the 26 separate QSAR models of toxicity, molecules with measured TAV greater than 0.5 were used. 3792 non-toxic compounds were investigated by molecular docking study and molecular dynamics simulations for their ACE inhibitory activity. Glide standard precision (SP) of Maestro Molecular Modeling pocket was used to perform molecular docking. Short molecular dynamics (MD) simulations (5-ns) were carried out by initiating the top docking poses of selected 40 molecules. To quantitatively evaluate the predicted binding affinity of a screened compound, average MM/GBSA scores of screened ligands were calculated and based on their binding free energy values, hit compounds were identified for the long (100-ns) MD simulations. Root mean square deviation and root mean square fluctuations were also calculated to assess the structural characteristics and observe fluctuations of the 100-ns time scale. Thus, with the application of text mining and integrated molecular modeling we reported novel indole-based hit inhibitors for ACE-1.Communicated by Ramaswamy H. Sarma." @default.
• W3091188435 created "2020-10-08" @default.
• W3091188435 creator A5010690232 @default.
• W3091188435 date "2020-10-05" @default.
• W3091188435 modified "2023-10-17" @default.
• W3091188435 title "<i>In silico</i> identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening" @default.
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• W3091188435 doi "https://doi.org/10.1080/07391102.2020.1827038" @default.
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