Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Henle, Jeremy J.; Zahrt, Andrew F.; Rose, Brennan T.; Darrow, William T.; Wang, Yang; Denmark, Scott E.

doi:10.1021/jacs.0c04715

Cited by 93Machine Learning in Materials ScienceComputational Drug Discovery MethodsAsymmetric Synthesis and Catalysis

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Henle, Jeremy J., Zahrt, Andrew F., Rose, Brennan T., Darrow, William T., Wang, Yang, Denmark, Scott E.·2020·Journal of the American Chemical Society

DOI 10.1021/jacs.0c04715

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Abstract

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.

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Beyond trial-and-error or ad hoc selection of descriptors, rigorous feature selection with ML models has emerged as a powerful tool for inferring design principles.
“Beyond trial-and-error or ad hoc selection of descriptors, rigorous feature selection with ML models has emerged as a powerful tool for inferring design principles (22,32,43,52,72,90,91).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [91]

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Authors: Henle, J. J., Zahrt, A. F., Rose, B. T., Darrow, W. T., Wang, Y., & Denmark, S. E.
Affiliations: University of Illinois Urbana-Champaign (US)
Corresponding author: Denmark, Scott E.
ORCID: Henle, Jeremy J.
Zahrt, Andrew F.
Rose, Brennan T.
Darrow, William T.
Wang, Yang
Denmark, Scott E.
Venue: Journal of the American Chemical Society
Publisher: American Chemical Society (ACS)
Published: 2020-06-22
Volume: 142
Issue: 26
Pages: 11578-11592
ISSN: 0002-7863 (ISSN-L)
1520-5126
DOI: 10.1021/jacs.0c04715
OpenAlex: W3036777101
Cited by: 93
References: 63
Citations by year: 2026: 13 · 2025: 11 · 2024: 12 · 2023: 11 · 2022: 17 · 2021: 25 · 2020: 4
FWCI: 5.54
Type: journal article
Language: en
Open access: closed
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: Journal of the American Chemical Society (publishedVersion)
PubMed (publishedVersion)
Funders: Division of Chemistry (CHE1900617)
Janssen Research and Development
W. M. Keck Foundation
University of Illinois
National Science Foundation
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
Keywords: asymmetric catalysis, catalyst optimization, molecular descriptors, training data selection, data augmentation, unsupervised learning, QSAR models

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Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

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