QSAR modeling of naphthalene derivatives for inhibition of aldosterone synthase as potential target for cardiovascular diseases

Ghasem Ghasemi¹

1) Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, Iran

Publication : International Conference on Science and Engineering(icesconf.com)
Abstract :
The aim of this study was to explore data from different families of compounds through the use of a variety of machine learning techniques. The robust quantitative structure-activity relationship (QSAR)-based models were developed to further guide in the quest for new potent anti-cardiovascular disease compounds. QSAR study was conducted on 1-Phenylsulfinyl-3-(pyridin-3-yl)naphthalen-2-ols and related compounds to determine their efficacy as anti-cardiovascular disease drugs. Multiple linear regressions (stepwise-MLR) and Imperialist Competitive Algorithm (ICA), were used to create nonlinear and linear QSAR models. The root-mean square errors of the training set and test set for ICA models, were 0.2013, 0.1402 and R = 0.951. The results obtained from this study indicate that ICA models are more effective than other statistical methods and exhibit reasonable prediction capabilities. Also, the best descriptors were Jhetp, JGI3, G(N..S), RDF065u and R78v+. Radial distribution function, atomic van der Waals volumes, atomic polarizabilities and Balaban-type index are important descriptors in this study.
Keywords : Key words: Cardiovascular diseases quantitative structure-activity relationship Imperialist Competitive Algorithm and Multiple linear regressions.