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Researchers Identify Potential Active Substances Against Coronavirus by Running Supercomputer Simulations

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A simulation of an attack on a coronavirus molecule.

Researchers in Germany used the MOGON II supercomputer to identify several drugs as potential candidates against COVID-19.


Researchers at Johannes Gutenberg University Mainz (JGU) in Germany have used the MOGON II supercomputer to identify several drugs approved for treating hepatitis C viral infection as potential candidates against COVID-19.

The team simulated how approximately 42,000 different substances bind to certain proteins of the SARS-CoV-2 coronavirus, inhibiting the penetration of the virus into the human body or its multiplication.

MOGON II, able to make more than 30 billion single calculations within two months, found that compounds from the four hepatitis C drugs—simeprevir, paritaprevir, grazoprevir, and velpatasvir—have a high affinity to bind with SARS-CoV-2 and thus may be able to prevent infection.

Said JGU researcher Thomas Efferth, "This computer simulation method is known as molecular docking and it has been recognized and used for years. It is much faster and less expensive than lab experiments."

From Johannes Gutenberg University of Mainz (Germany)
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA


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