| Autor: |
Artur Meller, Michael Ward, Jonathan Borowsky, Meghana Kshirsagar, Jeffrey M. Lotthammer, Felipe Oviedo, Juan Lavista Ferres, Gregory R. Bowman |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-023-36699-3 |
| Popis: |
Cryptic pockets enable targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. Here, the authors develop a graph neural network that accurately predicts cryptic pockets in static structures by training using molecular simulation data alone. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink