Graph-mining algorithm for the evaluation of bond formability

Journal of Chemical Information and Modeling

Volumn 50, Issue 2, 2010, Pages 221-239

  Pennerath, Frédéric ^a   Niel, Gilles ^b   Vismara, Philippe ^c,d   Jauffret, Philippe ^e   Laurenço, Claude ^b   Napoli, Amedeo ^f  

^a SUPELEC   (France)

^b UNIVERSITÉ DE MONTPELLIER   (France)

^c LIRMM   (France)

^d MONTPELLIER SUPAGRO   (France)

^e CNRS   (France)

^f LORIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; FORMABILITY; MOLECULES; REACTION PRODUCTS;

BOND PROPERTIES; GRAPH MINING; MOLECULAR GRAPHS; ORGANIC SYNTHESIS; REACTION DATABASE; STRUCTURAL ENVIRONMENT; TARGET MOLECULE; VIRTUAL SCREENING;

GRAPH ALGORITHMS;

EID: 77649210500     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci9003909     Document Type: Article

References (43)

1. Corey, E.; Long, A.; Rubenstein, S. Computer-Assisted Analysis in Organic Synthesis. Science 1985, 228, 408-418.
2. Corey, E.; Cheng, X. The Logic of Chemical Synthesis; John Wiley & Sons: New York, 1989.
3. Smit, W.; Bochkov, A.; Caple, R. Organic Synthesis: The Science Behind the Art; Royal Society of Chemistry: Cambridge, U.K., 1998.
4. Vleduts, G. E. Concerning one System of Classification and Codifica-tion of Organic Reactions. Inf. Storage Retr. 1963, 1, 117-146.
5. Corey, E. Computer-Assisted Analysis of Complex Synthetic Problems. O. Rev Chem. Soc. 1971, 25, 455-482.
6. Pfoertner, M.; Sitzmann, M. Computer-Assisted Synthesis Design by WODCA (CASD). In Handbook of Chemoinformatics: Gasteiger, J., Ed.; Wiley-VCH: Weinheim, Germany, 2003; pp 1457-1507.
7. Ott, M. Cheminformatics and Organic Chemistry. Computer-Assisted Synthetic Analysis. Cheminformatics 2004, 1, 83-109.
8. Hanessian, S. Man, Machine and Visual Imagery in Strategic Synthesis Planning: Computer-Perceived Precursors for Drug Candidates. Curr. Opin, Drug Discover Dev. 2005, 8, 798-819.
9. Chen, W. L. Chemoinformatics: Past, Present, and Future, J. Chem. Inf. Model. 2006, 46, 2230-2255.
10. Gund, P.; Grabowski, E. J. J.; Hoff, D. R.; Smith, G. M.; Andose, J. D.; Rhodes, J. B.; Wipke, W. T. Computer-Assisted Synthetic Analysis at Merck. J. Chem. Inf. Comput. Sci 1980, 20, 88-93.
11. Bertz, S. H.; Sommer, T. J. Rigorous Mathematical Approaches to Strategic Bonds and Synthetic Analysis Based on Conceptually Simple New Complexity Indexes. Chem. Commun. 1997 24, 2409-2410.
12. Ruecker, C.; Ruecker, G.; Bertz, S. H. Organic Synthesis-Art or Science. J. Chem. Inf. Comput. Sci 2004, 44, 378-386.
13. Hanessian, S. Total Synthesis of Natural Products: The"Chiron" Ap-proach. In Organic Chemistry Series; Pergamon Press: Oxford, U.K., 1983; Vol.3.
14. Régin, J.-C.; Gascuel, O.; Laurenço, C. Machine learning of strategic knowledge in organic synthesis from reaction databases. Proceedings of the First European Conference on Computational Chemistry; (ECCC-1), Nancy, France, May 23-27; AIP Press: Woodbury, NY, 1995; pp 618-623.
15. Régin, J.-C. Ph.D. thesis, Université des Sciences et Techniques du Languedoc, Montpellier, 1995.
16. Pennerath, F.; Polaillon, G.; Napoli, A. A Method for Classifying Vertices of Labeled Graphs Applied to Knowledge Discovery from Molecules. Proceedings of the 18th European Conference on Artificial Intelligence; (ECAI'08), Patras, Greece; IOS Press: Amsterdam, 2008; pp 147-151.
17. Vapnik, V. N. Statistical Learning Theory; Wiley: New York, 1998.
18. Inokuchi, A.; Washio, T.; Motoda, H. An a Priori-Based Algorithm for Mining Frequent Substructures from Graph Data. PKDD; Springer: Heidelberg, Germany, 2000; pp 13-23.
19. Kuramochi, M.; Karypis, G. Frequent Subgraph Discovery. Proceed-ings of the 2001 IEEE International Conference on Data Mining, 29 Nov-2 Dec 2001, San Jose, CA; IEEE Computer Society: Los Alamitos, CA, 2001; pp 313-320.
20. Yan, X.; Han, J. gSpan: Graph-Based Substructure Pattern Mining. ICDM; IEEE Computer Society: Los Alamitos, CA, 2002; pp 721-724.
21. Nijssen, S.; Kok, J. N. A Quickstart in Frequent Structure Mining Can Make a Difference. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Seattle, WA, August 22-25, 2004; ACM Press: New York, 2004; pp 647-652.
22. Cook, D. J.; Holder, L. B. Substructure Discovery Using Minimum Description Length and Background Knowledge. J. Artif. Intell. Res. 1994, 1, 231-255.
23. (23) Mahé, P.; Ueda, N.; Akutsu, T.; Perret, J. L.; Vert, J. P. Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines. J. Chem. Inf. Model. 2005, 45, 939-951. (Pubitemid 41156693)
24. (24) Fröhlich, H.; Wegner, J. K.; Sieker, F.; Zell, A. Optimal Assignment Kernels for Attributed Molecular Graphs. Machine Learning; Proceed-ings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005; ACM Press: New York, 2005; pp 225-232. (Pubitemid 43183337)
25. Mitchell, T. M. Version Spaces: A Candidate Elimination Approach to Rule Learning. Proceedings of the 5th International Joint Confer-ence on Artificial Intelligence; Cambridge, MA, August 1977; William Kaufmann: San Mateo, CA, 1977; pp 305-310.
26. Ganter, B.; Grigoriev, P. A.; Kuznetsov, S. O.; Samokhin, M. V. Concept-Based Data Mining with Scaled Labeled Graphs. ICCS; Springer: Heidelberg, Germany, 2004; pp 94-108.
27. Muggleton, S.; Srinivasan, A.; King, R. D.; Sternberg, M. J. E. Biochemical Knowledge Discovery Using Inductive Logic Program-ming. DS '98: Proceedings of the First International Conference on Discovery Science; Springer: Heidelberg, Germany, 1998; pp 326-341.
28. Helma, C.; Cramer, T.; Kramer, S.; Raedt, L. D. Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Non-congeneric Compounds. J. Chem. Inf. Comput. Sci 2004, 44, 1402-1411.
29. Jauffret, P.; Hanser, T.; Tonnelier, C.; Kaufmann, G. Machine Learning of Generic Reactions: I) Scope of the Project. Tetrahedron Comout. Methodol. 1990, 3, 323-333.
30. Agrawal, R.; Mannila, H.; Srikant, R.; Toivonen, H.; Verkamo, A. I. Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining; AAAI/MIT Press :Menlo Park, CA, 1996; pp 307-328.
31. Russell, S. J.; Norvig, P. Artificial Intelligence: A Modem Approach, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, 2002.
32. Pennerath, F.; Polaillon, G.; Napoli, A. Prétraitement des bases de données de réactions chimiques pour la fouille de schémas de réactions. Proceedings of the 8th Conference on Extraction et gestion des connaissances (EGC'2008); Sophia-Antipolis, France, January 2008; 2008; Editions Cépaduès: Toulouse, France, pp 547-558.
33. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27, 861-874.
34. van Rijsbergen, C. J. Information Retrieval, 2nd ed.; Butterworths: London, 1979.
35. Tanaka, A.; Kawaia, T.; Fujiia, M.; Matsumotoa, T.; Takabatakea, T.; Okamotob, H.; Funatsu, K. Molecular Centrality for Synthetic Design of Convergent Reactions. Tetrahedron 2008, 64, 4602-4612.
36. Tanaka, A.; Kawai, T.; Matsumoto, T.; Fujii, M.; Takabatake, T.; Okamoto, H.; Funatsu, K. Construction of a Statistical Evaluation Model Based on Molecular Centrality to Find Retrosynthetically Important Bonds in Organic Compounds, Eur. J. Org. Chem. 2008, 2008, 5995-6007.
37. (37) Baber, J. C.; Feher, M. Predicting Synthetic Accessibility: Application in Drug Discovery and Development. Mini-Rev. Med. Chem. 2004, 4, 681-692. (Pubitemid 38961233)
38. Long, A.; Chen, R.; Marby, C. A.; Sukharevsky, A. P.; Ohm, K. From Synthesis Planning to Combinatorial Chemistry: Applications of the Lhasa Suite, Presented at 228th ACS National Meeting, Philadelphia, PA, August 22-26, 2004; (accessed Dec 17, 2009).
39. Gillet, V.; Myatt, G.; Zsoldos, Z.; Johnson, P. SPROUT, HIPPO, and CAESA: Tools for De Novo Structure Generation and Estimation of Synthetic Accessibility, Perspect. Drug. Discovery Des. 1995, 3, 34-50.
40. Boda, K.; Seidel, T.; Gasteiger, J. Structure and Reaction Based Evaluation of Synthetic Accessibility. J. Comput. -Aided Mol. Des. 2007, 21, 311-325.
41. Law, J.; Zsoldos, Z.; Simon, A.; Reid, D.; Liu, Y.; Khew, S. Y.; Johnson, A. P.; Major, S.; Wade, R. A.; Ando, H. Y. Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation. J. Chem. Inf. Model. 2009, 49, 593-602.
42. Borgelt, C.; Berthold, M. R., Mining Molecular Fragments: Finding Relevant Substructures of Molecules. Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM '02); 9-12 De-cember 2002, Maebashi City, Japan; IEEE Computer Society: Los Alamitos, CA, 2002; pp 51-58.
43. (43) Fischer, I.; Meinl, T., Graph Based Molecular Data Mining-An overview. Proceedings of the IEEE International Conference on Systems, Man & Cybernetics; The Hague, Netherlands, 10-13 October 2004; IEEE Computer Society: Los Alamitos, CA, 2004; pp 4578-4582. (Pubitemid 40415749)