recently a group of researchers published a paper at the WHO, describing a "virtual screening" of existing drugs. They used a "supercomputer" to simulate how good these drugs would bind to the Coronavirus' protein.
Paper at WHO's: http://dx.doi.org/10.2471/BLT.20.255943Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As
yet, there are no established drugs or vaccines available. Speeding up drug discovery is urgently
required. We applied a workflow of combined in silico methods (virtual drug screening, molecular
docking and supervised machine learning algorithms) to identify novel drug candidates against
COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug
repositioning and of natural compound datasets from literature mining and the ZINC database to
select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid
protein, and 2’-o-ribose methyltransferase). Supported by the supercomputer MOGON II,
candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several
approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir,
simeprevir, grazoprevir, and velpatasvir) as well as drugs against transmissible diseases, against
cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is
supported by reports that anti-HCV compounds are also active against Middle East Respiratory
Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to
speed up the drug development against SARS-CoV-2.
Are there any projects at F@H, which use the same method?
Greetings