Expansion of bond dissociation prediction with machine learning to medicinally and environmentally relevant chemical space-Reference-Cited by-同舟云学术

Expansion of bond dissociation prediction with machine learning to medicinally and environmentally relevant chemical space

Published:2023 Issue:6 Volume:2 Page:1900-1910
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

S. V. Shree Sowndarya¹^ORCID,Kim Yeonjoon¹^ORCID,Kim Seonah¹^ORCID,St. John Peter C.²^ORCID,Paton Robert S.¹^ORCID

Affiliation:

1. Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA

2. Biosciences Center, National Renewable Energy Laboratory, Golden, CO 80401, USA

Abstract

Bond dissociation energetics underpin the thermodynamics of chemical transformations where bonds are broken or formed and can also be used to predict reaction rates and selectivities.

Funder

National Science Foundation

University of Colorado Boulder

University of Colorado

Colorado State University

Office of Advanced Cyberinfrastructure

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00169E

Reference50 articles.

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2. Bond Dissociation Energies of Organic Molecules

3. Theoretical Bond Dissociation Energies of Halo-Heterocycles: Trends and Relationships to Regioselectivity in Palladium-Catalyzed Cross-Coupling Reactions

4. Using dissociation energies to predict observability of b- and y-peaks in mass spectra of short peptides

5. Computational Study of Bond Dissociation Enthalpies for a Large Range of Native and Modified Lignins

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