CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
Abstract Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning ap...
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| Автори: | , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Nature Portfolio
2024-08-01
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| Серія: | Scientific Data |
| Онлайн доступ: | https://doi.org/10.1038/s41597-024-03698-y |
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