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Uncertainty-aware prediction of chemical reaction yields with graph neural networks
In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750748/ https://www.ncbi.nlm.nih.gov/pubmed/35012654 http://dx.doi.org/10.1186/s13321-021-00579-z |