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Cited by 357Asymmetric Synthesis and CatalysisAsymmetric Hydrogenation and CatalysisAxial and Atropisomeric Chirality Synthesis

The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond

Sigman, Matthew S., Harper, Kaid C., Bess, Elizabeth N., Milo, Anat·2016·Accounts of Chemical Research
DOI 10.1021/acs.accounts.6b00194
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Abstract

In most modern organic chemistry reports, including many of ours, reaction optimization schemes are typically presented to showcase how reaction conditions have been tailored to augment the reaction's yield and selectivity. In asymmetric catalysis, this often involves evaluation of catalyst, solvent, reagent, and, sometimes, substrate features. Such an article will then detail the process's scope, which mainly focuses on its successes and briefly outlines the "limitations". These limitations or poorer-performing substrates are occasionally the result of obvious, significant changes to structure (e.g., a Lewis basic group binds to a catalyst), but frequently, a satisfying explanation for inferior performance is not clear. This is one of several reasons such results are not often reported. These apparent outliers are also commonplace in the evaluation of catalyst structure, although most of this information is placed in the Supporting Information. These practices are unfortunate because results that appear at first glance to be peculiar or poor are considerably more interesting than ones that follow obvious or intuitive trends. In other words, all of the data from an optimization campaign contain relevant information about the reaction under study, and the "outliers" may be the most revealing. Realizing the power of outliers as an entry point to entirely new reaction development is not unusual. Nevertheless, the concept that no data should be wasted when considering the underlying phenomena controlling the observations of a given reaction is at the heart of the strategy we describe in this Account. The idea that one can concurrently optimize a reaction to expose the structural features that control its outcomes would represent a transformative addition to the arsenal of catalyst development and, ultimately, de novo design. Herein we outline the development of a recently initiated program in our lab that unites optimization with mechanistic interrogation by correlating reaction outputs (e.g., electrochemical potential or enantio-, site, or chemoselectivity) with structural descriptors of the molecules involved. The ever-evolving inspiration for this program is rooted in outliers of classical linear free energy relationships. These outliers encouraged us to ask questions about the parameters themselves, suggest potential interactions at the source of the observed effects, and, of particular applicability, identify more sophisticated physical organic descriptors. Throughout this program, we have integrated techniques from disparate fields, including synthetic methodology development, mechanistic investigations, statistics, computational chemistry, and data science. The implementation of many of these strategies is described, and the resulting tools are illustrated in a wide range of case studies, which include data sets with simultaneous and multifaceted changes to the reagent, substrate, and catalyst structures. This tactic constitutes a modern approach to physical organic chemistry wherein no data are wasted and mechanistic hypotheses regarding sophisticated processes can be developed and probed.

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  • Successful descriptors were generally demonstrated in linear regression or MLR models.

    “In this case, successful descriptors were generally demonstrated in linear regression or MLR models (59,60).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [59]

  • As quantum chemical modeling became tractable, first-principles descriptors were used, with frontier orbital energies or partial charges playing an important role in building transferable models capable of predicting experimental catalyst activities.

    “As quantum chemical modeling became tractable, first-principles descriptors were used, with frontier orbital energies or partial charges playing an important role in building transferable models capable of predicting experimental catalyst activities (59,61).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [59]

  • Because fitness evaluation dictates the overall time for optimization, multiple linear regression or quantitative structure-property relationship models, force field or semiempirical models, or machine learning regression models are preferable to full density functional theory evaluation.

    “Because fitness evaluation dictates the overall time for optimization, MLR (i.e., quantitative structure-property relationship, or QSPR) models (59,60), force field or semiempirical models (36), or ML regression models (e.g., with ANNs) (120) are preferable in comparison to full DFT evaluation.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [59]

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Authors
Sigman, M. S., Harper, K. C., Bess, E. N., & Milo, A.
Affiliations
University of Utah (US)
Corresponding author
Sigman, Matthew S.
ORCID
Venue
Accounts of Chemical Research
Publisher
American Chemical Society (ACS)
Published
2016-05-24
Volume
49
Issue
6
Pages
1292-1301
ISSN
  • 0001-4842 (ISSN-L)
  • 1520-4898
DOI
10.1021/acs.accounts.6b00194
OpenAlex
W2396220592
Cited by
357
References
58
Citations by year
2026: 20 · 2025: 26 · 2024: 28 · 2023: 42 · 2022: 46 · 2021: 48 · 2020: 39 · 2019: 36 · 2018: 36 · 2017: 32 · 2016: 4
FWCI
16.54
Type
journal article
Language
en
Open access
closed
Locations
  • Accounts of Chemical Research (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Army Research Office (MURI (#W911NF1410263))
  • National Science Foundation (CHE-0132905, CHE-0749506, CHE-1361296, CHE-1205646)
Topics
  • Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
  • Physical Sciences › Chemistry › Inorganic Chemistry › Asymmetric Hydrogenation and Catalysis
  • Physical Sciences › Chemistry › Organic Chemistry › Axial and Atropisomeric Chirality Synthesis
Keywords
asymmetric catalysis, reaction condition optimization, structural descriptors, enantioselectivity, site selectivity, chemoselectivity, electrochemical potential, physical organic chemistry, multidimensional analysis

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