Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for <i>de novo</i> drug design and computational chemistry approaches-Reference-Cited by-同舟云学术

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Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

Published:2024 Issue:6 Volume:15 Page:2146-2159
ISSN:2632-8682
Container-title:RSC Medicinal Chemistry
language:en
Short-container-title:RSC Med. Chem.

Author:

Hazemann Julien¹²^ORCID,Kimmerlin Thierry²,Lange Roland²,Sweeney Aengus Mac²^ORCID,Bourquin Geoffroy²,Ritz Daniel²,Czodrowski Paul¹^ORCID

Affiliation:

1. Physical Chemistry, Chemistry Department, Johannes Gutenberg University, Duesbergweg 10-14, 55128 Mainz, Germany

2. Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 91, 4123 Allschwil, Switzerland

Abstract

A pragmatic approach to the discovery of new SARS-COV-2 Mpro inhibitors by combining generative chemistry and computational chemistry approaches.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MD/D4MD00106K

Reference46 articles.

1. “WHO Coronavirus (COVID-19) Dashboard”, can be found under https://covid19.who.int/

2. Therapeutics for COVID-19

3. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

4. SARS-CoV-2 Mpro: A Potential Target for Peptidomimetics and Small-Molecule Inhibitors

5. An oral SARS-CoV-2 M pro inhibitor clinical candidate for the treatment of COVID-19

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