ABSTRACT
Researchers are focused on discovering compounds that can interfere with the COVID-19 life cycle. One of the important non-structural proteins is endoribonuclease since it is responsible for processing viral RNA to evade detection of the host defense system. This work investigates a hierarchical structure-based virtual screening approach targeting NSP15. Different filtering approaches to predict the interactions of the compounds have been included in this study. Using a deep learning technique, we screened 823,821 compounds from five different databases (ZINC15, NCI, Drug Bank, Maybridge, and NCI Diversity set III). Subsequently, two docking protocols (extra precision and induced fit) were used to assess the binding affinity of the compounds, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy. Interestingly, one compound (ZINC000104379474) from the ZINC15 database has been found to have a good binding affinity of - 7.68 kcal/Mol. The VERO-E6 cell line was used to investigate its therapeutic effect in vitro. Half-maximal cytotoxic concentration and Inhibitory concentration 50 were determined to be 0.9 mg/ml and 0.01 mg/ml, respectively; therefore, the selectivity index is 90. In conclusion, ZINC000104379474 was shown to be a good hit for targeting the virus that needs further investigations in vivo to be a drug candidate.