Recent computational drug repositioning strategies against SARS-CoV-2
Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still e...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022004640 |
| _version_ | 1828087858913607680 |
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| author | Lu Lu Jiale Qin Jiandong Chen Na Yu Satoru Miyano Zhenzhong Deng Chen Li |
| author_facet | Lu Lu Jiale Qin Jiandong Chen Na Yu Satoru Miyano Zhenzhong Deng Chen Li |
| author_sort | Lu Lu |
| collection | DOAJ |
| description | Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning. |
| first_indexed | 2024-04-11T05:18:05Z |
| format | Article |
| id | doaj.art-1502dd45b7ba4efeb2ae2a7f803f253a |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:18:05Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-1502dd45b7ba4efeb2ae2a7f803f253a2022-12-24T04:54:48ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012057135728Recent computational drug repositioning strategies against SARS-CoV-2Lu Lu0Jiale Qin1Jiandong Chen2Na Yu3Satoru Miyano4Zhenzhong Deng5Chen Li6Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, ChinaDepartment of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, ChinaDepartment of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, JapanXinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China; Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China; Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.http://www.sciencedirect.com/science/article/pii/S2001037022004640COVID-19Drug repositioningDrug combinationNeural networkSignature matchingMolecular docking |
| spellingShingle | Lu Lu Jiale Qin Jiandong Chen Na Yu Satoru Miyano Zhenzhong Deng Chen Li Recent computational drug repositioning strategies against SARS-CoV-2 Computational and Structural Biotechnology Journal COVID-19 Drug repositioning Drug combination Neural network Signature matching Molecular docking |
| title | Recent computational drug repositioning strategies against SARS-CoV-2 |
| title_full | Recent computational drug repositioning strategies against SARS-CoV-2 |
| title_fullStr | Recent computational drug repositioning strategies against SARS-CoV-2 |
| title_full_unstemmed | Recent computational drug repositioning strategies against SARS-CoV-2 |
| title_short | Recent computational drug repositioning strategies against SARS-CoV-2 |
| title_sort | recent computational drug repositioning strategies against sars cov 2 |
| topic | COVID-19 Drug repositioning Drug combination Neural network Signature matching Molecular docking |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022004640 |
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