Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Zahrt, Andrew F.; Athavale, Soumitra V.; Denmark, Scott E.

doi:10.1021/acs.chemrev.9b00425

Cited by 212Asymmetric Synthesis and CatalysisComputational Drug Discovery MethodsMolecular spectroscopy and chirality

Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

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Zahrt, Andrew F., Athavale, Soumitra V., Denmark, Scott E.·2020·Chemical Reviews

DOI 10.1021/acs.chemrev.9b00425PMC PMC7018559

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Abstract

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

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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 [60]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
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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 [60]

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
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Machine Learning for the Discovery, Design, and Engineering of Materials.
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Authors: Zahrt, A. F., Athavale, S. V., & Denmark, S. E.
Affiliations: University of Illinois Urbana-Champaign (US)
Corresponding author: Denmark, Scott E.
ORCID: Zahrt, Andrew F.
Athavale, Soumitra V.
Denmark, Scott E.
Venue: Chemical Reviews
Publisher: American Chemical Society (ACS)
Published: 2019-12-30
Volume: 120
Issue: 3
Pages: 1620-1689
ISSN: 0009-2665 (ISSN-L)
1520-6890
DOI: 10.1021/acs.chemrev.9b00425
PMC: PMC7018559
OpenAlex: W2997878828
Cited by: 212
References: 244
Citations by year: 2026: 26 · 2025: 34 · 2024: 32 · 2023: 35 · 2022: 26 · 2021: 39 · 2020: 20
FWCI: 1.58
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-045 (stm-asf)
Locations: Chemical Reviews (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
CaltechAUTHORS (California Institute of Technology) (submittedVersion)
Funders: Division of Chemistry (NSF CHE1900617)
University of Illinois
Topics: Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Chemistry › Spectroscopy › Molecular spectroscopy and chirality
Keywords: asymmetric catalysis, cheminformatics, catalyst design, linear free energy relationships, molecular interaction fields, multivariate models, machine learning, reaction mechanism prediction
MeSH terms: Machine Learning, Catalysis, Chemistry, Organic, Models, Chemical, Stereoisomerism, Multivariate Analysis, Quantitative Structure-Activity Relationship, Informatics

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Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

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