Improved Automated Reaction Mapping

Kouri, Tina M.; Mehta, Dinesh P.

doi:10.1007/978-3-642-20662-7_14

Cited by 5 publications

(9 citation statements)

References 27 publications

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“…Degree Neighborhoods (DN): DN is a simple labeling algorithm that does not guarantee uniqueness, that is two non-isomorphic molecules may have the same DN name (i.e., there may be a Type 1 Error) [23,25]. DN assigns each atom a name based on its label and degree and the label and degree of each of its neighbors.…”

Section: Practical Canonical Naming Algorithms For Chemical Graphsmentioning

confidence: 99%

“…DN was used to speed up automated reaction mapping (an important tool in bioinformatics and cheminformatics) computations [23][24][25]. The automated reaction mapping algorithms which utilize the DN were tested on a variety of mechanisms, including a Colorado School of Mines (CSM) mechanism, a Lawrence Livermore National Laboratory (LLNL) [18] mechanism and the KEGG/LIGAND v57 database [16].…”

Section: Practical Canonical Naming Algorithms For Chemical Graphsmentioning

confidence: 99%

See 1 more Smart Citation

Random Models and Analyses for Chemical Graphs

Kouri

Pascua

Mehta

2015

Int. J. Found. Comput. Sci.

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This paper describes a random model for chemical graphs that captures the notion of valence along with algorithms to generate chemical graphs using this model. An approach for computing the probability that a particular chemical graph is generated under this model is provided. The model is also used to provide theoretical bounds on the accuracy of a class of canonical labeling algorithms for a class of hydrocarbons.

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Section: Practical Canonical Naming Algorithms For Chemical Graphsmentioning

confidence: 99%

Section: Practical Canonical Naming Algorithms For Chemical Graphsmentioning

confidence: 99%

Random Models and Analyses for Chemical Graphs

Kouri

Pascua

Mehta

2015

Int. J. Found. Comput. Sci.

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“…We investigate this approach and discuss it briefly in this paper. However, it turns out that due to the size of each isomer (usually over 30 atoms), the number of bonds broken between pairs of isomers (often over 10 bonds per pair) and the number of isomers for a given set of atoms (usually over 10,000 isomers), it is impractical to use our ARM algorithms that were used for validating combustion mechanisms − (which usually contain a few thousand reactions and breaks fewer than three bonds per reaction).…”

Section: Algorithm Backgroundmentioning

confidence: 99%

“…The algorithm presented is inspired by the random graph canonical labeling algorithms presented in refs 27 and 28 and the primitive refinement step used in Nauty 19 and was used to speed up ARM computations. 6,7 The main idea of the degree neighborhood (DN) algorithm is to assign each atom a name based on its symbol and degree and the symbol and degree of each of its neighbors. The names of each atom are then used to assign a name to the molecule.…”

Section: ■ Introductionmentioning

confidence: 99%

“Social” Network of Isomers Based on Bond Count Distance: Algorithms

Kouri

Awale

Slyby

et al. 2014

J. Chem. Inf. Model.

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This paper introduces the concept of an isomer network based on the reaction step counts between pairs of isomers as an alternative means to view and analyze isomer space. The computation of isomer networks is computationally expensive with respect to both run time and memory. Accordingly, this paper focuses on the design of algorithms to compute isomer networks and their analysis on structurally diverse subsets of isomers of nicotine, tyrosine, and phenmetrazine generated using molecular quantum number nearest neighbors. An analysis correlating isomer networks to extended connectivity fingerprints is also provided.

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“…Maximum common substructure (MCS) searches on graphs of general class are computationally challenging, being classified under the larger class of maximum subgraph isomorphism problems with nondeterministic polynomial time complexity (NP-hard). A large number of efforts have addressed this challenge with varying degrees of success and scope. ,− These algorithms can identify a maximum substructure between most molecular graphs but they have distinctive failure modes. For example, the algorithm by Lynch and Willet, an adaptation of the extended connectivity (EC) algorithm fails to identify a matching when the compared molecular graphs are too small (<7 atoms).…”

Section: Introductionmentioning

confidence: 99%

CLCA: Maximum Common Molecular Substructure Queries within the MetRxn Database

Kumar

Maranas

2014

J. Chem. Inf. Model.

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The challenge of automatically identifying the preserved molecular moieties in a chemical reaction is referred to as the atom mapping problem. Reaction atom maps provide the ability to locate the fate of individual atoms across an entire metabolic network. Atom maps are used to track atoms in isotope labeling experiments for metabolic flux elucidation, trace novel biosynthetic routes to a target compound, and contrast entire pathways for structural homology. However, rapid computation of the reaction atom mappings remains elusive despite significant research. We present a novel substructure search algorithm, canonical labeling for clique approximation (CLCA), with polynomial run-time complexity to quickly generate atom maps for all the reactions present in MetRxn. CLCA uses number theory (i.e., prime factorization) to generate canonical labels or unique IDs and identify a bijection between the vertices (atoms) of two distinct molecular graphs. CLCA utilizes molecular graphs generated by combining atomistic information on reactions and metabolites from 112 metabolic models and 8 metabolic databases. CLCA offers improvements in run time, accuracy, and memory utilization over existing heuristic and combinatorial maximum common substructure (MCS) search algorithms. We provide detailed examples on the various advantages as well as failure modes of CLCA over existing algorithms.

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Improved Automated Reaction Mapping

Cited by 5 publications

References 27 publications

Random Models and Analyses for Chemical Graphs

Random Models and Analyses for Chemical Graphs

“Social” Network of Isomers Based on Bond Count Distance: Algorithms

CLCA: Maximum Common Molecular Substructure Queries within the MetRxn Database

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