Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition—In silico approach. (April 2019)

• Record Type: Journal Article
• Authors: Ćirić Zdravković, Snezana; Pavlović, Milan; Apostlović, Svetlana; Koraćević, Goran; Šalinger Martinović, Sonja; Stanojević, Dragana; Sokolović, Dušan; Veselinović, Aleksandar M.
• Is Part Of: Computational biology and chemistry. Volume 79(2019)
• Page Start: 55
• Page End: 62
• Publication Date: 2019-04
• Subjects: Rho kinase inhibitors -- Cardiovascular therapy -- QSAR -- Molecular modeling -- Drug design; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.01.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: QSAR models for ROCK inhibitory action of urea derivates were developed. Monte Carlo method with SMILES notation and molecular graph descriptors was used. Different methods were applied for the determination of the robustness of the model. Molecular fragments with influence on inhibitory action were determined. Presented study can be useful in the search for novel cardiovascular therapeutics. Abstract: Rho kinases, one of the best-known members of the serine/threonine (Ser/Thr) protein kinase family, can be used as target enzymes for the treatment of many diseases such as cancer or multiple sclerosis, and especially for the treatment of cardiovascular diseases. This study presents QSAR modeling for a series of 41 chemical compounds as Rho kinase inhibitors based on the Monte Carlo method. QSAR models were developed for three random splits into the training and test set. Molecular descriptors used for QSAR modeling were based on the SMILES notation and local invariants of the molecular graph. The statistical quality of the developed model, including robustness and predictability, was tested with different statistical approaches and satisfying results were obtained. The best calculated QSAR model had the following statistical parameters: r 2 = 0.8825 and q 2 = 0.8626 for the training set and r 2 = 0.9377 and q 2 = 0.9124 for the test set. Novel statistical metric entitled as the index of ideality of correlation was used for the final model assessment, and the obtained results were 0.6631 for the training and 0.9683 for the test set. Molecular fragments responsible for the increases and decreases of the studied activity were defined and they were further used for the computer-aided design of new compounds as potential Rho kinase inhibitors. The final assessment of the developed QSAR model and designed inhibitors was achieved with the application of molecular docking. An excellent correlation between the results from QSAR and molecular docking studies was obtained.