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Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Automating retrosynthesis with artificial intelligence expedites organic chemistry research in digital laboratories. However, most existing deep-learning approaches are hard to explain, like a “black box” with few insights. Here, we propose RetroExplainer, formulizing the retrosynthesis task into a...
| 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/PMC10547708/ https://www.ncbi.nlm.nih.gov/pubmed/37788995 http://dx.doi.org/10.1038/s41467-023-41698-5 |