Dataset Information

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (M^pro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.

ABSTRACT: Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 M^pro. Compounds such as cinanserin, nelfinavir, baicalin, baicalein, candesartan cilexetil, chloroquine, dipyridamole, and hydroxychloroquine have the ability to prevent SARS-CoV-2 M^pro from facilitating COVID 19 infection; thus, they treat COVID 19. However, these drugs ma

SUBMITTER: Onyango H

PROVIDER: S-EPMC9576439 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Dataset Information

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (M^pro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (Mpro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.

OmicsDI is part of the ELIXIR infrastructure

Tweets

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (M^pro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.