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...

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Detalles Bibliográficos
Autores principales: Wang, Yu, Pang, Chao, Wang, Yuzhe, Jin, Junru, Zhang, Jingjie, Zeng, Xiangxiang, Su, Ran, Zou, Quan, Wei, Leyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
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

Internet

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

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