PREDICTION OF POTENTIAL SARS-CoV-2 INHIBITOR: A COMPUTATIONAL APPROACH

Abstract

The recent global fight against the spread of novel coronavirus has not been successful. This
has resulted in public health emergencies across the world. Meanwhile, no effective drug or
vaccine has been reported. Hence, maximum effort from stakeholders at various scales must
be invoked. This study used Computer-Aided Drug Design (CADD) based approach to
identify the drug-like compounds with possible capacity to inhibit SARS-CoV-2. Online tools
(ZINC pharmer) was used to search for drug-like compounds, followed by molecular docking
of the predicted hit compounds. The five-best ligand-receptor complex was selected based on
the S-score of the predicted compounds that are higher than the reference inhibitor
(chloroquine). After evaluating the binding energies, five compounds namely ZINC72170473,
ZINC89801760, ZINC72435450, ZINC07987472 and ZINC63855480 were noticed to have -
6.9 kcal/mol, -6.8 kcal/mol, -6.6 kcal/mol, -6.5 kcal/mol, and -6.5 kcal/mol docking score
rescpectively. These docking scores were higher than that of chloroquine (-4.9 kcal/mol). The
pharmacokinetics and drug-likeness of the lead molecule (ZINC72170473) were assessed by
making use of Swiss ADME a free web tool. The result revealed that the oral bioavailability
of the lead molecule was fine. Its solubility was also moderate for all class considered. The
toxicity prediction on the ProTox-II webserver revealed that ZINC72170473 has an LD50
value of 600 mg/kg and belong to toxicity class 4. The quantum mechanical calculations also
showed that the lead molecule (ZINC72170473) demonstrated better global reactive than the
reference molecule (chloroquine). Hence, ZINC72170473 have shown drug-like characteristic
and may have the capacity to inhibit the function of some
essential protein(s) that are key to the viral life cycle if given a trial.