Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches.
Title: | Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches. |
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Authors: | Hazemann, Julien, Kimmerlin, Thierry, Lange, Roland, Sweeney, Aengus Mac, Bourquin, Geoffroy, Ritz, Daniel, Czodrowski, Paul |
Source: | RSC Medicinal Chemistry; Jun2024, Vol. 15 Issue 6, p2146-2159, 14p |
Database: | Complementary Index |
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RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1039/d4md00106k Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2146 Titles: – TitleFull: Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hazemann, Julien – PersonEntity: Name: NameFull: Kimmerlin, Thierry – PersonEntity: Name: NameFull: Lange, Roland – PersonEntity: Name: NameFull: Sweeney, Aengus Mac – PersonEntity: Name: NameFull: Bourquin, Geoffroy – PersonEntity: Name: NameFull: Ritz, Daniel – PersonEntity: Name: NameFull: Czodrowski, Paul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 26328682 Numbering: – Type: volume Value: 15 – Type: issue Value: 6 Titles: – TitleFull: RSC Medicinal Chemistry Type: main |
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