On the use of real-world datasets for reaction yield prediction

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly avail...

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Detalles Bibliográficos
Autores principales: Saebi, Mandana, Nan, Bozhao, Herr, John E., Wahlers, Jessica, Guo, Zhichun, Zurański, Andrzej M., Kogej, Thierry, Norrby, Per-Ola, Doyle, Abigail G., Chawla, Nitesh V., Wiest, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189898/
https://www.ncbi.nlm.nih.gov/pubmed/37206399
http://dx.doi.org/10.1039/d2sc06041h

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189898/
https://www.ncbi.nlm.nih.gov/pubmed/37206399
http://dx.doi.org/10.1039/d2sc06041h

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