https://libeldoc.bsuir.by/handle/123456789/45873| Title: | Development of Molecular Autoencoders as Generators of Protein Inhibitors: Application for Prediction of Potential Drugs Against Coronavirus SARS-CoV-2 |
| Authors: | Shuldau, M. Yushkevich, A. Bosko, I. Tuzikov, A. Andrianov, A. |
| Keywords: | материалы конференций;conference proceedings;SARS-CoV-2;main protease;deep learning;generative autoencoder;semi-supervised learning;virtual screening;molecular docking |
| Issue Date: | 2021 |
| Publisher: | UIIP NASB |
| 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. |
| 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. |
| URI: | https://libeldoc.bsuir.by/handle/123456789/45873 |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021) |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Shuldau_Development.pdf | 1.14 MB | Adobe PDF | View/Open |
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