Cited by 132Machine Learning in Materials ScienceAsymmetric Hydrogenation and CatalysisAsymmetric Synthesis and Catalysis

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

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Amar, Yehia, Schweidtmann, Artur M., Deutsch, Paul, Cao, Liwei, Lapkin, Alexei·2019·Chemical Science

Abstract

Rational solvent selection remains a significant challenge in process development.

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Research works printed but not linked: 5

  1. Inza I. Larrañaga P. Saeys Y. Bioinformatics 2007 23 2507 2517 17720704
  2. Grethe G. Blanke G. Kraut H. Goodman J. M. J. Cheminf. 2018 10 22 PMC5940998 29740723
  3. Martha Cornelius T. Heemskerk A. Hoogendoorn J. C. Elders N. Niessen Wilfried M. A. Orru Romano V. A. Irth H. Chem.–Eur. J. 2009 15 7368 7375 19551778
  4. Olson R. S., Bartley N., Urbanowicz R. J. and Moore J. H., Proceedings of the Workshop on Automatic Machine Learning, 2016, vol. 64, pp. 66–74.
  5. Rasmussen C. E. and Williams C. K. I., Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, 2005.

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Authors
Amar, Y., Schweidtmann, A. M., Deutsch, P., Cao, L., & Lapkin, A.
Affiliations
  • University of Cambridge (GB)
  • RWTH Aachen University (DE)
  • UCB Pharma S.A. Allée de la Recherche
Corresponding author
Lapkin, Alexei
Venue
Chemical Science
Published
2019
Volume
10
Issue
27
Pages
6697-6706
ISSN
  • 2041-6520 (ISSN-L)
  • 2041-6539
OpenAlex
W2946850705
Cited by
132
References
57
Citations by year
2026: 14 · 2025: 15 · 2024: 18 · 2023: 20 · 2022: 23 · 2021: 25 · 2020: 12 · 2019: 5
FWCI
5.87
Type
journal article
Language
en
Open access
Locations
  • Chemical Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
  • RWTH Publications (RWTH Aachen)
Funders
  • UCB
  • BASF
  • National Research Foundation Singapore
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Inorganic Chemistry › Asymmetric Hydrogenation and Catalysis
  • Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
Keywords
solvent selection, asymmetric catalysis, asymmetric hydrogenation, multi-objective optimization, diastereomeric excess, Kriging surrogate model, Bayesian optimization, genetic programming, molecular descriptors, robotics laboratory, experimental design

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