Cited by 65Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction

Sadowski, Peter, Fooshee, David, Subrahmanya, Niranjan, Baldi, Pierre·2016·Journal of Chemical Information and Modeling

Abstract

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions.

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  • Outputs of theory-based models may be used as training samples in data science components, creating a two-way synergy between them.

    “Further, the outputs of theory-based models may also be used as training samples in data science components [72], thus creating a two-way synergy between them.”

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Authors
Sadowski, P., Fooshee, D., Subrahmanya, N., & Baldi, P.
Affiliations
  • University of California, Irvine (US)
  • ExxonMobil (United States) (US)
Corresponding author
  • Sadowski, Peter
  • Baldi, Pierre
Venue
Journal of Chemical Information and Modeling
Published
2016-10-25
Volume
56
Issue
11
Pages
2125-2128
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
OpenAlex
W2538573120
Cited by
65
References
41
Citations by year
2026: 2 · 2025: 6 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 8 · 2020: 13 · 2019: 7 · 2018: 7 · 2017: 4 · 2016: 1
FWCI
2.82
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation
  • Defense Advanced Research Projects Agency
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
reaction mechanism prediction, activation energy prediction
MeSH terms
Machine Learning, Models, Molecular, Molecular Conformation, Quantum Theory, Thermodynamics

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