Automated reaction mapping

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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“…Each label denotes a chemical atom (e.g., C, O, H). Each edge in E corresponds to a chemical bond [20].…”

Section: Graph Representation Of Moleculesmentioning

confidence: 99%

Random Models and Analyses for Chemical Graphs

Kouri

Pascua

Mehta

2015

Int. J. Found. Comput. Sci.

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“…The ITS thus can be derived from the rewriting rule provided the mapping of the vertices (atoms) between the left graph and the right graph is known. A variant of the cut successive largest (CSL) algorithm [36] is used to predict the atom mapping from the educt and product molecular graphs automatically. The performance of the original CSL algorithm was drastically improved by replacing the expensive complete subgraph isomorphism check with an efficient subgraph isomorphism check [37] augmented by a chemically motivated heuristic scoring schema for bond breaking energies.…”

Section: Chemical Reactions: Graph Rewritingmentioning

confidence: 99%

Evolution of metabolic networks: a computational frame-work

et al. 2010

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Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can be selected for. Results: We describe here a simulation environment that implements all these components in a simplified ways so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.

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“…The reactionmapping problem may be formulated as that of finding a mapping from the atoms of the reactant graphs to the atoms of the product graphs that minimizes the number of bonds broken or formed [Crabtree and Mehta 2009]. For example, consider the reaction OH + C H 4 ⇔ H 2 O + C H 3 shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal mapping will break a C-H reactant bond and form an O-H product bond. The general automated reaction mapping problem is known to be NP-hard [Akutsu 2004;Crabtree and Mehta 2009]. (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.…”

Section: Introductionmentioning

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%

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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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Automated reaction mapping

Cited by 27 publications

References 28 publications

Random Models and Analyses for Chemical Graphs

Random Models and Analyses for Chemical Graphs

Evolution of metabolic networks: a computational frame-work

Faster reaction mapping through improved naming techniques

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