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Cited by 75Catalytic Cross-Coupling ReactionsMachine Learning in Materials ScienceComputational Drug Discovery Methods

Combinatorial Explosion in Homogeneous Catalysis: Screening 60,000 Cross‐Coupling Reactions

Burello, Enrico, Farrusseng, David, Rothenberg, Gadi·2004·Advanced Synthesis & Catalysis
DOI 10.1002/adsc.200404170
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Abstract

A new approach to the selection of Heck cross‐coupling catalysts and reaction conditions is presented, based on a quantitative structure‐activity relationship (QSAR) descriptor set that is coupled to linear and non‐linear analysis models. A set of steric and electronic descriptors is defined and calculated. The correlations between ligands, substrates, catalyst precursors and reaction conditions in a dataset of 412 Heck reactions are then analyzed using artificial neural networks, classification tree methods, and linear models. The regression and classification models are powerful, with prediction confidence levels as high as 93%. These models are then used to predict the performance (turnover numbers and turnover frequencies) of 60,000 combinations of virtual catalysts and reaction conditions in silico. Furthermore, the models are used to pinpoint mechanistic effects, in this case the important role that small palladium clusters play in Heck cross‐coupling. The pros and cons of linear and non‐linear models and the application of this approach to the selection of new experiments in high‐throughput systems are discussed.

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Crossref · 10.1002/adsc.200404170

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  • After training an ANN on 412 experimental catalysts and reaction conditions, 66,000 new combinations were predicted with the trained ANN.

    “after training an ANN on 412 experimental catalysts and reaction conditions, 66,000 new combinations were then predicted with the trained ANN (6).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [6]

  • Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s bear striking similarity to many modern applications.

    “Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s (5-8, 42) bear striking similarity to many modern applications (32,43,(72)(73)(74).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [6]

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Authors
Burello, E., Farrusseng, D., & Rothenberg, G.
Affiliations
  • University of Amsterdam (NL)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Farrusseng, David
  • Rothenberg, Gadi
ORCID
Venue
Advanced Synthesis & Catalysis
Publisher
Wiley
Published
2004-12
Volume
346
Issue
13-15
Pages
1844-1853
ISSN
  • 1615-4150 (ISSN-L)
  • 1615-4169
DOI
10.1002/adsc.200404170
OpenAlex
W2154289461
Cited by
75
References
32
Citations by year
2025: 2 · 2024: 3 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 2 · 2015: 2 · 2014: 1 · 2013: 3 · 2012: 5
FWCI
2.54
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 15 d)
Locations
  • Advanced Synthesis & Catalysis (publishedVersion)
  • UvA-DARE (University of Amsterdam) (publishedVersion)
  • HAL (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
Topics
  • Physical Sciences › Chemistry › Organic Chemistry › Catalytic Cross-Coupling Reactions
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
Heck reaction, catalyst screening, QSAR, QSAR descriptors, artificial neural networks, classification trees, turnover frequency, palladium clusters, reaction condition optimization, combinatorial chemistry, high-throughput experimentation, homogeneous catalysis

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