In silico identification of drug candidates against COVID-19

Yifei Wu; Kuan Y Chang; Lei Lou; Lorette G Edwards; Bly K Doma; Zhong-Ru Xie

doi:10.1016/j.imu.2020.100461

In silico identification of drug candidates against COVID-19

Inform Med Unlocked. 2020:21:100461. doi: 10.1016/j.imu.2020.100461. Epub 2020 Oct 20.

Authors

Yifei Wu¹, Kuan Y Chang², Lei Lou¹, Lorette G Edwards^{1

3}, Bly K Doma¹, Zhong-Ru Xie¹

Affiliations

¹ Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, 30602, GA, USA.
² Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, 202, Taiwan.
³ The Franklin College of Arts and Sciences, University of Georgia, Athens, 30602, GA, USA.

Abstract

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M ^pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M ^pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M ^pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M ^pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

Keywords: COVID-19; Drug repurposing; Ligand-protein docking; Main protease; RNA-dependent RNA polymerase; Remdesivir; Virtual screening.