In silico investigations of dihydrophenanthrene derivatives as potential inhibitors of SARS-CoV-2

Since its appearance in Wuhan on December 2019, finding ways to manage the COVID19 pandemic becomes the biggest challenge the world is facing. In this investigation, we used quantitative structure-activity relationship (QSAR) study, Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis and computational molecular docking simulations to screen and assess the efficacy of thirty-nine bioactive 9,10-dihydrophenanthrene analogues.

The density functional theory (DFT) calculations using B3LYP/6-31G(d, p) level was used for the calculations of molecular descriptors and the principal component analysis (PCA) was used to eliminated redundant and non-significant descriptors. After that, statistically robust models were developed using multiple linear regression (MLR) method. All derived models were then subjected to thorough external as well as internal statistical validations, Y-randomization and applicability domain analysis. These validations were carried out as per the OECD principles. The best built model is used to design new molecules that have good values of the inhibitory activity against SARS-CoV-2. Pharmacokinetics properties were then determined using ADMET analysis to weed out any that would be harmful to the human body or cause adverse effects. Through the use of computational molecular docking simulations, in silico research was conducted on deigned compounds to forecast their SARS-CoV-2 activity and determine the stability of the evaluated ligands during their contacts with the protein of desired activity.