Uncertainty-aware prediction of chemical reaction yields with graph neural networks
Abstract 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...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMC
2022-01-01
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| coleção: | Journal of Cheminformatics |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1186/s13321-021-00579-z |
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