Computational Library Enables Pattern Recognition of Noncovalent Interactions and Application as a Modern Linear Free Energy Relationship

A quantitative and predictive understanding of how attractive noncovalent interactions (NCIs) influence functional outcomes is a long-standing goal in mechanistic chemistry. In that context, better comprehension of how substituent effects influence NCI strengths, and the origin of those effects, is still needed. We sought to build a resource capable of elucidating fundamental origins of substituent effects in NCIs and diagnosing NCIs in chemical systems. To accomplish this, a library of 893 NCI energies was calculated encompassing cation-π, anion-π, CH-π, and π-π interactions across 60 different arenes and heteroarenes. The interaction energies (IEs) were calculated using symmetry-adapted perturbation theory (SAPT), which identifies electrostatic, inductive, exchange-repulsive, and dispersive contributions to total IE. This descriptor library provides a comprehensive platform for evaluating substituent effect trends beyond traditional molecular descriptors such as Hammett values, frontier molecular orbital energies, and electrostatic potential, thereby expanding the tools available to analyze modern chemical processes that involve NCIs. To demonstrate the application of this library, three case studies in asymmetric catalysis and supramolecular chemistry are presented. These case studies informed the development of an automated NCI analysis tool, which employs statistical analyses to diagnose a particular NCI in a chemical system of interest.

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Publication Details

Journal
The Journal of Organic Chemistry
Published
2024-11-24
DOI
https://doi.org/10.1021/acs.joc.4c01790
Citations
11
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.98

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article

Computational Library Enables Pattern Recognition of Noncovalent Interactions and Application as a Modern Linear Free Energy Relationship

Matthew S. Sigman, Beck Miller, Tyler E. Ball, Jacquelyne A. Read et al.
11 citations
The Journal of Organic Chemistry
Computational Drug Discovery Methods
3.98
article

Computational Library Enables Pattern Recognition of Noncovalent Interactions and Application as a Modern Linear Free Energy Relationship

Matthew S. Sigman, Beck Miller, Tyler E. Ball, Jacquelyne A. Read, Eric N. Jacobsen
article en
11 citations

Abstract

A quantitative and predictive understanding of how attractive noncovalent interactions (NCIs) influence functional outcomes is a long-standing goal in mechanistic chemistry. In that context, better comprehension of how substituent effects influence NCI strengths, and the origin of those effects, is still needed. We sought to build a resource capable of elucidating fundamental origins of substituent effects in NCIs and diagnosing NCIs in chemical systems. To accomplish this, a library of 893 NCI energies was calculated encompassing cation-π, anion-π, CH-π, and π-π interactions across 60 different arenes and heteroarenes. The interaction energies (IEs) were calculated using symmetry-adapted perturbation theory (SAPT), which identifies electrostatic, inductive, exchange-repulsive, and dispersive contributions to total IE. This descriptor library provides a comprehensive platform for evaluating substituent effect trends beyond traditional molecular descriptors such as Hammett values, frontier molecular orbital energies, and electrostatic potential, thereby expanding the tools available to analyze modern chemical processes that involve NCIs. To demonstrate the application of this library, three case studies in asymmetric catalysis and supramolecular chemistry are presented. These case studies informed the development of an automated NCI analysis tool, which employs statistical analyses to diagnose a particular NCI in a chemical system of interest.

The Journal of Organic ChemistryVol. 89(23)
Dartmouth College (US), Harvard University (US), University of Utah (US)
National Science Foundation, Auburn University, North Carolina State University, National Institutes of Health, Center for High Performance Computing, National Institute of General Medical Sciences, Division of Chemistry
Affordable and clean energy
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
3.98
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