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Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - approach. | LitMetric

AI Article Synopsis

  • The study focuses on developing a quantitative structure-activity relationship (QSAR) model to identify compounds that can inhibit cholesteryl ester transfer protein (CETP), which is relevant for treating cardiovascular diseases.
  • Multiple statistical methods were employed to assess the model's quality, demonstrating strong correlation between different versions of the QSAR models and promising robustness and predictability.
  • The research also identified specific molecular fragments influencing CETP activity and utilized molecular docking to validate the designed inhibitors, confirming good alignment with the QSAR model results.

Article Abstract

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

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http://dx.doi.org/10.1080/07391102.2019.1630319DOI Listing

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