An Open-Source Java Platform for Automated Reaction Mapping

Crabtree, John D.; Mehta, Dinesh P.; Kouri, Tina M.

doi:10.1021/ci100061d

Cited by 13 publications

(19 citation statements)

References 32 publications

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“…Since the maximum common edge subgraph problem is similar to the maximum common subgraph problem, it will have the same problems as any maximum common subgraph-based approach. Crabtree et al [2010], the authors present five algorithms for automated reaction mapping that work for any valid chemical reaction. The first algorithm is a fast greedy heuristic, which is not guaranteed to find an optimal solution.…”

Section: Subgraph Isomorphism-based Algorithmsmentioning

confidence: 99%

“…The Constructive Count Vector (CCV) algorithm is novel because it is the fastest algorithm presented in Crabtree and Mehta [2009] and Crabtree et al [2010], it is guaranteed to find the optimal solution, and it does not have the limitations that previous algorithms have placed on the problem. Other reaction mapping algorithms are not guaranteed to find the best mapping or they will not work for all classes of reactions.…”

Section: Theorem 211 Any Mapping Of a Valid Chemical Reaction Is Eqmentioning

confidence: 99%

“…Other reaction mapping algorithms are not guaranteed to find the best mapping or they will not work for all classes of reactions. Because the CCV algorithm is the fastest algorithm presented in Crabtree and Mehta [2009] and Crabtree et al [2010] and CCV provides an optimal solution for a general, well-defined optimization problem, we will be using CCV as our point of comparison. We now describe the CCV algorithm in detail to provide the necessary background information to understand our contributions.…”

Section: Theorem 211 Any Mapping Of a Valid Chemical Reaction Is Eqmentioning

confidence: 99%

“…(Akutsu [2004] previously considered a different formulation of the reaction mapping problem in which there is a bound on the number of cuts on each reactant and product molecule and showed this formulation to be NP-complete. ) In this article we present four algorithms that significantly improve the runtime of the Constructive Count Vector (CCV) algorithm presented by [Crabtree and Mehta 2009;Crabtree et al 2010], while maintaining solution optimality. The authors [Crabtree and Mehta 2009;Crabtree et al 2010] have proven their algorithms result in an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…) In this article we present four algorithms that significantly improve the runtime of the Constructive Count Vector (CCV) algorithm presented by [Crabtree and Mehta 2009;Crabtree et al 2010], while maintaining solution optimality. The authors [Crabtree and Mehta 2009;Crabtree et al 2010] have proven their algorithms result in an optimal solution. Our improvements do not affect optimality.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Faster reaction mapping through improved naming techniques

Kouri

Mehta

2013

ACM J. Exp. Algorithmics

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Automated reaction mapping is an important tool in cheminformatics where it may be used to classify reactions or validate reaction mechanisms. The reaction mapping problem is known to be NP-Hard and may be formulated as an optimization problem. In this article, we present four algorithms that continue to obtain optimal solutions to this problem, but with significantly improved runtimes over the previous Constructive Count Vector (CCV) algorithm. Our algorithmic improvements include (i) the use of a fast (but not 100% accurate) canonical labeling algorithm, (ii) name reuse (i.e., storing intermediate results rather than recomputing), and (iii) an incremental approach to canonical name computation. The time to map the reactions from the Kegg/Ligand database previously took over 2 days using CCV, but now it takes fewer than 4 hours to complete. Experimental results on chemical reaction databases demonstrate our 2-CCV FDN MS algorithm usually performs over fifteen times faster than previous automated reaction mapping algorithms. ACM Reference Format:Kouri, T. M. and Mehta, D. P. 2013. Faster reaction mapping through improved naming techniques. ACM J.

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Section: Subgraph Isomorphism-based Algorithmsmentioning

confidence: 99%

Section: Theorem 211 Any Mapping Of a Valid Chemical Reaction Is Eqmentioning

confidence: 99%

Section: Theorem 211 Any Mapping Of a Valid Chemical Reaction Is Eqmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Faster reaction mapping through improved naming techniques

Kouri

Mehta

2013

ACM J. Exp. Algorithmics

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Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites

Hadadi

Hafner

Soh

et al. 2017

Biotechnology Journal

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Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.

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RCARM: Reaction classification using automated reaction mapping

Kouri

Crabtree

Huynh

et al. 2012

Int J of Chemical Kinetics

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Detailed chemical kinetic modeling of gas-phase reactions can result in automatically generated mechanisms that contain thousands of reactions. In this paper, we describe the development of a rule-based expert system tool that organizes these reactions into classes such as hydrogen abstraction and beta scission. We have developed 29 simple classification rules, 20 complex (well-skipping) classification rules, and four second-stage classification rules. This greatly simplifies the task of the chemical kineticist who wishes to verify, analyze, and gain insights into the reactions comprising the mechanism. This system, which is based on the automated identification of the bonds that break and form in a chemical reaction (the reaction mapping problem), is used to classify reactions in three different mechanisms. C 2012 Wiley Periodicals, Inc. Int J Chem Kinet 45: 125-

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An Open-Source Java Platform for Automated Reaction Mapping

Cited by 13 publications

References 32 publications

Faster reaction mapping through improved naming techniques

Faster reaction mapping through improved naming techniques

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites

RCARM: Reaction classification using automated reaction mapping

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