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Cited by 39Catalytic Cross-Coupling ReactionsComputational Drug Discovery MethodsMachine Learning in Materials Science

Optimal Heck Cross‐Coupling Catalysis: A Pseudo‐Pharmaceutical Approach

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Burello, Enrico, Rothenberg, Gadi·2003·Advanced Synthesis & Catalysis
DOI 10.1002/adsc.200303141
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Abstract

The problem of predicting the activity of ligands and solvents in homogeneous catalysis is addressed. A systematic selection method for ligands and solvents is presented, using a combination of stereoelectronic and electrotopological descriptors to describe the ligands and solvents in a set of 500 Heck reactions. Libraries of ‘virtual ligands’ and ‘virtual solvents’ are projected on the reaction space, and their reactivity is then predicted using multivariate models. The applications of this approach to combinatorial catalysis and reducing the amount of ‘garbage data’ are discussed.

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Crossref · 10.1002/adsc.200303141

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  • Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.

    “Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [5]

    In “Choosing and Calculating the Reaction Descriptors” (page unavailable), the paper says descriptors should be simple to calculate and may use atom or fragment counts; it applies descriptors to Heck cross-coupling catalysis. It does not establish that topological descriptors specifically are often employed in transition-metal chemistry. This citation is attached to the topological-descriptor portion of the sentence, not the separately cited one-hot or MOF descriptors. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • 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 [5]

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Authors
Burello, E., & Rothenberg, G.
Affiliations
University of Amsterdam (NL)
ORCID
Rothenberg, Gadi
Venue
Advanced Synthesis & Catalysis
Publisher
Wiley
Published
2003-12
Volume
345
Issue
12
Pages
1334-1340
ISSN
  • 1615-4150 (ISSN-L)
  • 1615-4169
DOI
10.1002/adsc.200303141
OpenAlex
W2062637662
Cited by
39
References
39
Citations by year
2024: 1 · 2022: 3 · 2021: 3 · 2019: 1 · 2017: 3 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 2 · 2012: 5
FWCI
2.12
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 22 d)
Locations
Advanced Synthesis & Catalysis (publishedVersion)
Topics
  • Physical Sciences › Chemistry › Organic Chemistry › Catalytic Cross-Coupling Reactions
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
Heck reaction, homogeneous catalysis, ligand selection, solvent selection, reaction space, multivariate models, combinatorial catalysis, catalyst activity prediction

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