| DC Field | Value | Language |
|---|
| dc.contributor.author | Shuldau, M. | - |
| dc.contributor.author | Yushkevich, A. | - |
| dc.contributor.author | Bosko, I. | - |
| dc.contributor.author | Tuzikov, A. | - |
| dc.contributor.author | Andrianov, A. | - |
| dc.date.accessioned | 2021-11-08T12:44:03Z | - |
| dc.date.available | 2021-11-08T12:44:03Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | Development of Molecular Autoencoders as Generators of Protein Inhibitors: Application for Prediction of Potential Drugs Against Coronavirus SARS-CoV-2 / Shuldau M. [et al.] // Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021) : Proceedings of the 15th International Conference, 21–24 Sept. 2021, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2021. – P. 153–158. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/45873 | - |
| dc.description.abstract | A generative autoencoder for the rational design of potential inhibitors of the SARS-CoV-2 main protease able to block the catalytic site of this functionally important viral enzyme was developed. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | UIIP NASB | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | conference proceedings | ru_RU |
| dc.subject | SARS-CoV-2 | ru_RU |
| dc.subject | main protease | ru_RU |
| dc.subject | deep learning | ru_RU |
| dc.subject | generative autoencoder | ru_RU |
| dc.subject | semi-supervised learning | ru_RU |
| dc.subject | virtual screening | ru_RU |
| dc.subject | molecular docking | ru_RU |
| dc.title | Development of Molecular Autoencoders as Generators of Protein Inhibitors: Application for Prediction of Potential Drugs Against Coronavirus SARS-CoV-2 | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021)
|
