*6.5.2.4 Results*

The results of the primary screening are presented in **Table 5**. The results demonstrated that Amprenavir, Tipranavir, and Fosamprenavir had a higher


#### **Table 4.**

*Chemical formula and drug bank accession number of nine FDA-approved antiviral protease inhibitors subjected for repurposing against SARS-CoV-2.*


*The results of blind molecular docking between the standard antiviral protease inhibitors and the 3CLpro from SARS-CoV-2. Amprenavir, Tipranavir, and Tipranavir showed high binding* 

*affinity to 3CLpro.*

*Evaluation of Drug Repositioning by Molecular Docking of Pharmaceutical Resources… DOI: http://dx.doi.org/10.5772/intechopen.101395*

**Figure 2.**

*Graphical representation of the targeted molecular docking between the 3CLpro from SARS-CoV-2 and (a) Fosamprenavir, (b) Amprenavir, and (c) Tipranavir. Fosamprenavir showed the most binding affinity in the subjected docking grid box followed by Amprenavir and Tipranavir respectively.*

binding affinity to the 3CLpro than the other tested viral protease inhibitors with Moldock scores of −160.384, −158.307, and −146.601 respectively. Furthermore, it was clear that GLN 189 is a key amino acid in the 3CLpro interactions with different proteases. Therefore, a targeted molecular docking between the three top-scoring standard protease inhibitors (Amprenavir, Tipranavir, and Fosamprenavir) were also performed in a grid box with the center of GLN189. As depicted in **Figure 2**, the subjected standard drugs also showed high affinity to the 3CLpro with binding energies of −5.3, −5.1, and −6.2 kcal/mol respectively. Subsequently, due to the high affinity of Fosamprenavir to the 3CLpro, this antiviral protease inhibitor could be considered for further in silico, in vitro, and in vivo evaluation to develop as a repurposed anti *SARS-CoV-2* treatment.

## **7. Conclusion**

To date, the only approved anti-COVID-19 treatment is a repurposed antiviral drug (Remdesivir). Hence, drug repurposing might be an effective approach for accelerating drug discovery against COVID-19. Computational drug repositioning offers a noteworthy reduction in time and costs of new drug development and increases success rates in comparison to traditional methods. Therefore, to date, different computational methods such as data mining, machine learning, network analysis, and molecular docking have successfully been used for drug repurposing.

Molecular docking is a popular bioinformatics method that recently has been highly regarded for studying the drug ability of biological entities, protein-ligand interactions, mechanism action of drug candidates, and drug repositioning. Retrieval drug candidates from standard databases or previous reports, lead and target optimization, running the molecular docking process, and results analysis are the main steps in molecular docking-based drug repositioning. The binding affinity of a drug candidate to key amino acid(s) of the identified target molecule can be considered a decision factor in the drug repositioning process.

Despite the advantages of computational drug repositioning, studying drug-target interactions by in silico methods is still far from reality.

*Evaluation of Drug Repositioning by Molecular Docking of Pharmaceutical Resources… DOI: http://dx.doi.org/10.5772/intechopen.101395*
