Negative Data in Data Sets for Machine Learning Training-Reference-Cited by-同舟云学术

Negative Data in Data Sets for Machine Learning Training

Published:2023-04-26 Issue:17 Volume:25 Page:2945-2947
ISSN:1523-7060
Container-title:Organic Letters
language:en
Short-container-title:Org. Lett.

Author:

Maloney Michael P.¹^ORCID,Coley Connor W.²^ORCID,Genheden Samuel³^ORCID,Carson Nessa⁴^ORCID,Helquist Paul¹^ORCID,Norrby Per-Ola⁵^ORCID,Wiest Olaf¹^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States

2. Department of Chemical Engineering and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden

4. Early Chemical Development, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield SK10 2NA, U.K.

5. Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden

Publisher

American Chemical Society (ACS)

Subject

Organic Chemistry,Physical and Theoretical Chemistry,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.orglett.3c01282

Reference9 articles.

1. Excellence in Industrial Organic Synthesis 2019: The Past, Present, and Future

2. Bayesian reaction optimization as a tool for chemical synthesis

3. PubMed search, “machine learning” and “organic” and “synthesis”, 2015–2022 as part of total number of publications in PubMed, performed on 3/11/2023. Note that PubMed has a time lag for inclusion, so 2022 publications will not be complete. Search performed using: Sperr E. PubMed by Year. 2016. Available from http://esperr.github.io/pubmed-by-year/

4. Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

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