Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules
This journal is © The Royal Society of Chemistry..
Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Chemical science - 12(2021), 48 vom: 15. Dez., Seite 15960-15974 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gentile, Francesco [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 21.09.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1039/d1sc05579h |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a This journal is © The Royal Society of Chemistry. | ||
| 520 | |a Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Fernandez, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Ban, Fuqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Ton, Anh-Tien |e verfasserin |4 aut | |
| 700 | 1 | |a Mslati, Hazem |e verfasserin |4 aut | |
| 700 | 1 | |a Perez, Carl F |e verfasserin |4 aut | |
| 700 | 1 | |a Leblanc, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Yaacoub, Jean Charle |e verfasserin |4 aut | |
| 700 | 1 | |a Gleave, James |e verfasserin |4 aut | |
| 700 | 1 | |a Stern, Abraham |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Bill |e verfasserin |4 aut | |
| 700 | 1 | |a Jean, François |e verfasserin |4 aut | |
| 700 | 1 | |a Strynadka, Natalie |e verfasserin |4 aut | |
| 700 | 1 | |a Cherkasov, Artem |e verfasserin |4 aut | |
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