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Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network
The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques t...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227049/ https://www.ncbi.nlm.nih.gov/pubmed/37248214 http://dx.doi.org/10.1038/s41467-023-38853-3 |