Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning

Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of exis...

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Detalles Bibliográficos
Autores principales: Toniato, Alessandra, Unsleber, Jan P., Vaucher, Alain C., Weymuth, Thomas, Probst, Daniel, Laino, Teodoro, Reiher, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10259370/
https://www.ncbi.nlm.nih.gov/pubmed/37312681
http://dx.doi.org/10.1039/d3dd00006k

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10259370/
https://www.ncbi.nlm.nih.gov/pubmed/37312681
http://dx.doi.org/10.1039/d3dd00006k

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