An in silico drug repositioning workflow for host-based antivirals
Summary: Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166721003609 |
| _version_ | 1818719722420568064 |
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| author | Zexu Li Yingjia Yao Xiaolong Cheng Wei Li Teng Fei |
| author_facet | Zexu Li Yingjia Yao Xiaolong Cheng Wei Li Teng Fei |
| author_sort | Zexu Li |
| collection | DOAJ |
| description | Summary: Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set.For complete details on the use and execution of this protocol, please refer to Li et al. (2021). |
| first_indexed | 2024-12-17T20:11:27Z |
| format | Article |
| id | doaj.art-e205aac446894f3b9a2fa19040fe8d90 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-12-17T20:11:27Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-e205aac446894f3b9a2fa19040fe8d902022-12-21T21:34:13ZengElsevierSTAR Protocols2666-16672021-09-0123100653An in silico drug repositioning workflow for host-based antiviralsZexu Li0Yingjia Yao1Xiaolong Cheng2Wei Li3Teng Fei4College of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China; Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of China; Corresponding authorCollege of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China; Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of ChinaCenter for Genetic Medicine Research, Children’s National Hospital, 111 Michigan Ave NW, Washington, DC 20010, USA; Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Ave NW, Washington, DC 20010, USACenter for Genetic Medicine Research, Children’s National Hospital, 111 Michigan Ave NW, Washington, DC 20010, USA; Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Ave NW, Washington, DC 20010, USACollege of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China; Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of China; Corresponding authorSummary: Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set.For complete details on the use and execution of this protocol, please refer to Li et al. (2021).http://www.sciencedirect.com/science/article/pii/S2666166721003609BioinformaticsHigh Throughput ScreeningImmunologyMicrobiologyMolecular BiologyStructural Biology |
| spellingShingle | Zexu Li Yingjia Yao Xiaolong Cheng Wei Li Teng Fei An in silico drug repositioning workflow for host-based antivirals STAR Protocols Bioinformatics High Throughput Screening Immunology Microbiology Molecular Biology Structural Biology |
| title | An in silico drug repositioning workflow for host-based antivirals |
| title_full | An in silico drug repositioning workflow for host-based antivirals |
| title_fullStr | An in silico drug repositioning workflow for host-based antivirals |
| title_full_unstemmed | An in silico drug repositioning workflow for host-based antivirals |
| title_short | An in silico drug repositioning workflow for host-based antivirals |
| title_sort | in silico drug repositioning workflow for host based antivirals |
| topic | Bioinformatics High Throughput Screening Immunology Microbiology Molecular Biology Structural Biology |
| url | http://www.sciencedirect.com/science/article/pii/S2666166721003609 |
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