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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/4797| Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model | |
| Fan Hu. Jiaxin Jiang. Peng Yin. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2003.00728v1.pdf | |
| The outbreak of novel coronavirus pneumonia (COVID-19) caused thousands of deaths worldwide, and the number of total infections is still rising. However, the development of effective vaccine for this novel virus would take a few months. Thus it is urgent to identify some potentially effective old drugs that can be used immediately. Fortunately, some compounds that can inhibit coronavirus in vitro have been reported. In this study, the coronavirus-specific dataset was used to fine-tune our pre-trained multi-task deep model. Next we used the re-trained model to select available commercial drugs against targeted proteins of SARS-CoV-2. The results show that abacavir, a powerful nucleoside analog reverse transcriptase inhibitor used to treat HIV, is predicted to have high binding affinity with several proteins of SARS-CoV-2. Almitrine mesylate and roflumilast which are used for respiratory diseases such as chronic obstructive pulmonary disease are also predicted to have inhibitory effect. Overall, ten drugs are listed as potential inhibitors and the important sites for these binding by our model are exhibited. We hope these results would be useful in the fight against SARS-CoV-2. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2003.00728v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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