tetano
Editor, Senior Moderator
Sci Rep
. 2021 Sep 9;11(1):17915.
doi: 10.1038/s41598-021-97031-x.
Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis
Nikhil Samarth[SUP] 1 [/SUP], Ritika Kabra[SUP] 1 [/SUP], Shailza Singh[SUP] 2 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (M[SUP]pro[/SUP]) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (M[SUP]pro[/SUP]) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (M[SUP]pro[/SUP]), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.
. 2021 Sep 9;11(1):17915.
doi: 10.1038/s41598-021-97031-x.
Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis
Nikhil Samarth[SUP] 1 [/SUP], Ritika Kabra[SUP] 1 [/SUP], Shailza Singh[SUP] 2 [/SUP]
Affiliations
- PMID: 34504128
- DOI: 10.1038/s41598-021-97031-x
Abstract
Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (M[SUP]pro[/SUP]) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (M[SUP]pro[/SUP]) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (M[SUP]pro[/SUP]), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.