Identification of new dihydrophenanthrene derivatives as promising anti-SARS-CoV-2 drugs through in silico investigations

Abstract

To research, evaluate, and invent novel compounds that inhibit SARS-CoV-2 activity, a series of reported 39 substituted 9, 10-dihydrophenanthrene derivatives were subjected to a quantitative structure-activity relationship (QSAR) study. Gaussian 09 and ChemOffice programs were used to calculate the molecular descriptors employed to determine their impact on the studied activity. Then we reduced the number of descriptors by eliminating the redundant information using principal component analysis (PCA). The creation of molecular models was done by using multiple linear regression (MLR) according to the principles established by the Organization for Economic Co-operation and Development (OECD) and the validation by using external and internal validation, Y-randomization tests, and domain of applicability. Moreover, we evaluated the toxicity of developed compounds using ADMET and Molecular docking to determine their optimal position to form a stable complex. As a result, four molecules may be used to develop a novel drug that can inhibit SARS-CoV-2 without causing the side effect.