Improved Automated Reaction Mapping

  • Tina Kouri
  • Dinesh Mehta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6630)


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-Complete and may be formulated as an optimization problem. In this paper we present three algorithms that continue to obtain optimal solutions to this problem, but with significantly improved runtimes over the previous CCV algorithm. Our algorithmic improvements include (a) the use of a fast (but not 100% accurate) canonical labeling algorithm, (b) name reuse (i.e., storing intermediate results rather than recomputing), and (c) an incremental approach to canonical name computation. Experimental results on chemical reaction databases demonstrate our 2-CCV NR FDN algorithm usually performs over ten times faster than previous fastest automated reaction mapping algorithms.


Applied Algorithms Automated Reaction Mapping Cheminformatics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Muharam, Y., Warnatz, J.: Kinetic Modelling of the Oxidation of Large Aliphatic Hydrocarbons Using an Automatic Mechanism Generation. Phys. Chem. Chem. Phys. 9, 4218–4229 (2007)CrossRefGoogle Scholar
  2. 2.
    Matheu, D., Grenda, J.: A Systematically Generated, Pressure-Dependent Mechanism for High-Conversion Ethane Pyrolysis. 1. Pathways to the Minor Products. J. Phys. Chem. 109, 5332–5342 (2005)CrossRefGoogle Scholar
  3. 3.
    Cartensen, H., Dean, A.M.: Rate Constant Rules for the Automated Generation of Gas-Phase Reaction Mechanisms. J. Phys. Chem. 113, 367–380 (2009)CrossRefGoogle Scholar
  4. 4.
    Nemeth, A., Vidoczy, T., Heberger, K., Kuti, Z., Wagner, J.: MECHGEN: Computer Aided Generation and Reduction of Reaction Mechanisms. J. Chem. Inf. Comput. Sci. 42, 208–214 (2002)CrossRefGoogle Scholar
  5. 5.
    Buda, F., Bounaceur, R., Warth, V., Glaude, P.A., Fournet, R., Battin-Leclerc, F.: Progress Toward a Unified Detailed Kinetic Model for the Autoignition of Alkanes from C4 to C10 Between 600 and 1200 K. Combust. Flame. 142, 170–186 (2005)CrossRefGoogle Scholar
  6. 6.
    Straube, R., Flockerzi, D., Muller, S.C., Hauser, J.B.: Reduction of Chemical Reaction Networks Using Quasi-Integrals. J. Phys. Chem. 109, 441–450 (2005)CrossRefGoogle Scholar
  7. 7.
    Pepiot-Desjardins, P., Pitsch, H.: An Efficient Error-propagation-based Reduction Method for Large Chemical Kinetic Mechanisms. Combust. Flame. 154, 67–81 (2008)CrossRefzbMATHGoogle Scholar
  8. 8.
    Liang, L., Stevens, J., Raman, S., Farrell, J.: The Use of Dynamic Adaptive Chemistry in Combustion Simulation of Gasoline Surrogate Fuels. Combust. Flame. 156, 1493–1502 (2009)CrossRefGoogle Scholar
  9. 9.
    Nagy, T., Turanyi, T.: Reduction of Very Large Reaction Mechanisms Using Methods Based on Simulation Error Minimization. Combust. Flame. 156, 417–428 (2009)CrossRefGoogle Scholar
  10. 10.
    Sun, W., Chen, Z., Gou, X., Yiguang, J.: A Path Flux Analysis Method for the Reduction of Detailed Chemical Kinetic Mechanisms. Combust. Flame. 157, 1298–1307 (2010)CrossRefGoogle Scholar
  11. 11.
    Shi, Y., Ge, H., Brakora, J., Reitz, R.: Automatic Chemistry Mechanism Reduction of Hydrocarbon Fuels for HCCI Engines Based on DRGEP and PCA Methods with Error Control. Energy & Fuels 24, 1646–1654 (2010)CrossRefGoogle Scholar
  12. 12.
    Kovacs, T., Zsely, I., Kramarics, A., Turanyi, T.: Kinetic Analysis of Mechanisms of Complex Pyrolytic Reactions. J. Anal. Appl. Pyrolysis. 79, 252–258 (2007)CrossRefGoogle Scholar
  13. 13.
    Crabtree, J.D., Mehta, D.P.: Automated Reaction Mapping. J. Exp. Algorithmics. 13, 1.15–1.29 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Akutsu, T.: Efficient Extraction of Mapping Rules of Atoms from Enzymatic Reaction Data. J. Comput. Biol. 11, 449–462 (2004)CrossRefGoogle Scholar
  15. 15.
    Crabtree, J., Mehta, D., Kouri, T.: An Open-Source Java Platform for Automated Reaction Mapping. J. Chem. Inf. Model. 50(9), 1751–1756 (2010)CrossRefGoogle Scholar
  16. 16.
    Pemmaraju, S., Skiena, S.: Computational Discrete Mathematics: Combinatorics and Graph Theory with Mathematica. Cambridge University Press, New York (2003)CrossRefzbMATHGoogle Scholar
  17. 17.
    Babai, L., Luks, E.: Canonical labeling of graphs. In: STOC 1983: Proceedings of the Fifteenth Annual ACM Symposium on Theory of Computing, pp. 171–183. ACM, New York (1983)CrossRefGoogle Scholar
  18. 18.
    Morgan, H.L.: The Generation of a Unique Machine Description for Chemical Structures - A Technique Developed at Chemical Abstracts Service. J. Chem. Doc. 5(2), 107–113 (1965)CrossRefGoogle Scholar
  19. 19.
    B. McKay. No automorphisms, yes? (2004),
  20. 20.
    McKay, B.: Practical Graph Isomorphism. Congr. Numer. 30, 45–87 (1981)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Faulon, J.-L., Collins, M.J., Carr, R.D.: The Signature Molecular Descriptor. 4. Canonizing Molecules Using Extended Valence Sequences. J. Chem. Inf. Model. 44(2), 427–436 (2004)Google Scholar
  22. 22.
    Babai, L., Erdos, P., Selkow, S.: Random Graph Isomorphism. Siam J. Comput. 9(3), 628–635 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Czajka, T., Panduranga, G.: Improved Random Graph Isomorphism. Journal of Discrete Algorithms 6, 85–92 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Felix, L., Valiente, G.: Efficient Validation of Metabolic Pathway Databases. In: Proc. 6th Int. Symp. Computational Biology and Genome Informatics, pp. 1209–1212 (2005)Google Scholar
  25. 25.
    Arita, M.: Metabolic Reconstruction Using Shortest Paths. Simulation Practice and Theory 8(2), 109–125 (2000)CrossRefGoogle Scholar
  26. 26.
    Hattori, M., Okuno, Y., Goto, S., Kanehisa, M.: Development of a Chemical Structure Comparison Method for Integrated Analysis of Chemical and Genomic Information in the Metabolic Pathways. J. Am. Chem. Soc. 125(1), 11853–11865 (2003)CrossRefGoogle Scholar
  27. 27.
    Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W.H. Freeman & Co., New York (1990)zbMATHGoogle Scholar
  28. 28.
    Korner, R., Apostolakis, J.: Automatic Determination of Reaction Mappings and Reaction Center Information. 1. The Imaginary Transition State Energy Approach. Journal of Chemical Information and Modeling 48(6), 1181–1189 (2008)CrossRefGoogle Scholar
  29. 29.
    Apostolakis, J., Sacher, O., Korner, R., Gasteiger, J.: Automatic Determination of Reaction Mappings and Reaction Center Information. 2. Validation on a Biochemical Reaction Database. Journal of Chemical Information and Modeling 48(6), 1190–1198 (2008)CrossRefGoogle Scholar
  30. 30.
    Gas Research Institute. Gri-mech 3.0,
  31. 31.
    Naik, C.V., Dean, A.M.: Detailed Kinetic Modeling of Ethane Oxidation. Combust. Flame. 145, 16–37 (2006)CrossRefGoogle Scholar
  32. 32.
    Randolf, K.L., Dean, A.M.: Hydrocarbon Fuel Effects in Solid-oxide Fuel Cell Operation: An Experimental and Modeling Study of n-hexane Pyrolysis. Phys. Chem. Chem. Phys. 9, 4245–4258 (2007)CrossRefGoogle Scholar
  33. 33.
    Curran, H.J., Gaffuri, P., Pitz, W.J., Westbrook, C.K.: A comprehensive modeling study of n-heptane oxidation. Combust. Flame. 114(1-2), 149–177 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tina Kouri
    • 1
  • Dinesh Mehta
    • 1
  1. 1.Colorado School of MinesUSA

Personalised recommendations