Computational Modeling of ACE2-Mediated Cell Entry Inhibitors for the Development of Drugs Against Coronaviruses

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which may cause acute respiratory infection and severe pneumonia. As of October 06, 2020, the number of cases worldwide was about 35,786,851 with 1,049,816 deaths. Unfortunately, no drug or vaccine has yet been approved to treat human coronaviruses. Several options can be envisaged to control or prevent emerging infections of COVID-19, including vaccines, monoclonal antibodies, oligonucleotide-based therapies, peptides, interferon therapies, and small molecule drugs. However, new interventions are likely to require months to years to develop. Human angiotensin converting enzyme 2 (ACE2) is a functional receptor for SARS coronavirus (SARS-CoV). Development of medications preventing the entry of the virus into host cells through ACE2 is a promising approach. Drug discovery and development is a challenging, time consuming, and expensive process. Computational tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. During this pandemic situation, in silico tools have proven their efficacy in easy identification of novel inhibitors. In the present chapter, recent researches on the application of computational modeling approaches for the design and identification of candidates targeted against ACE2-mediated corona viral entry have been reviewed. Computational works encompassing homology modelling, molecular docking, molecular dynamics simulation, pharmacophore mapping, and quantitative structure–activity relationship (QSAR) studies involving potential inhibitors of ACE2 facilitated entry of virus have been covered in this chapter.