Extension of Partial Atom-To-Atom Maps: Uniqueness and Algorithms

Chemical reaction databases typically report the molecular structures of reactant and product compounds, as well as their stoichiometry, but lack information, in particular, on the correspondence of reactant and product atoms. These atom-to-atom maps (AAM), however, are crucial for applications including chemical synthesis planning in organic chemistry and the analysis of isotope labeling experiments in modern metabolomics. AAMs therefore need to be reconstructed computationally. This situation is aggravated, furthermore, by the fact that chemically correct AAMs are, fundamentally, determined by quantum-mechanical phenomena and thus cannot be reliably computed by solving graph-theoretical optimization problems defined by the reactant and product structures. A viable solution for this problem is to shift the focus into first identifying a partial AAM containing the reaction center, i.e., covering the atoms incident with all bonds that change during a reaction. This then leads to the problem of extending the partial map to the full reaction. The AAM of a reaction is faithfully represented by the Imaginary Transition State (ITS) graph, providing a convenient graph-theoretic framework to address the questions of when and how a partial AAM can be extended. We show that an unique extension exists whenever, and only if, these partial AAMs cover the reaction center. In this case their extension can be computed by solving a constrained graph-isomorphism search between specific subgraphs of ITS graphs. We close by benchmarking different tools for this task.

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Publication Details

Journal
University of Southern Denmark Research Portal (University of Southern Denmark)
Published
2025-01-01
DOI
https://doi.org/10.4230/lipics.wabi.2025.12
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Extension of Partial Atom-To-Atom Maps: Uniqueness and Algorithms

University of Southern Denmark Research Portal (University of Southern Denmark)
Machine Learning in Materials Science
article

Extension of Partial Atom-To-Atom Maps: Uniqueness and Algorithms

article en

Abstract

Chemical reaction databases typically report the molecular structures of reactant and product compounds, as well as their stoichiometry, but lack information, in particular, on the correspondence of reactant and product atoms. These atom-to-atom maps (AAM), however, are crucial for applications including chemical synthesis planning in organic chemistry and the analysis of isotope labeling experiments in modern metabolomics. AAMs therefore need to be reconstructed computationally. This situation is aggravated, furthermore, by the fact that chemically correct AAMs are, fundamentally, determined by quantum-mechanical phenomena and thus cannot be reliably computed by solving graph-theoretical optimization problems defined by the reactant and product structures. A viable solution for this problem is to shift the focus into first identifying a partial AAM containing the reaction center, i.e., covering the atoms incident with all bonds that change during a reaction. This then leads to the problem of extending the partial map to the full reaction. The AAM of a reaction is faithfully represented by the Imaginary Transition State (ITS) graph, providing a convenient graph-theoretic framework to address the questions of when and how a partial AAM can be extended. We show that an unique extension exists whenever, and only if, these partial AAMs cover the reaction center. In this case their extension can be computed by solving a constrained graph-isomorphism search between specific subgraphs of ITS graphs. We close by benchmarking different tools for this task.

University of Southern Denmark Research Portal (University of Southern Denmark)
University of Vienna (AT), Santa Fe Institute (US), University of Southern Denmark (DK), Universidad Nacional de Colombia (CO), Telstra (Australia) (AU), Max Planck Institute for Mathematics in the Sciences (DE), Max Planck Institute for Mathematics (DE), Institute of Bioinformatics (IN)
European Commission, Deutscher Akademischer Austauschdienst, Bundesministerium für Bildung und Forschung, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 61%
Machine Learning in Materials Science
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