Chemistry Reference
In-Depth Information
[9] J. Huang, W. Wang and J. Prins, Efficient mining of frequent subgraphs in the presence of iso-
morphism,
in Proceedings of the 3rd IEEE International. Conference on Data Mining (ICDM)
,
IEEE Press, Piscataway, NJ, 2004.
[10] S. Nijssen and J.N. Kok, A quickstart in frequent structure mining can make a difference, in
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining
, ed. R. Kohavi, J. Gehrke, W. DuMouchel and J. Ghosh, ACM Press, New
York, 2004.
[11] M. Wörlein, T. Meinl, I. Fischer and M. Philippsen, A quantitative comparison of the subgraph
miners MoFa, gSpan, FFSM and Gaston, in
Proceedings of the 3rd European Confer-
ence of Principles of Knowledge Discovery and Data Mining (PKDD)
, 2005, pp. 392-403.
http://www.springerlink.com/content/f510121050061j54/fulltext.pdf.
[12]
Index Chemicus
, Institute for Scientific Information (ISI), Philadelphia, PA, subset from 1993.
[13] HIV assay, dtp.nci.nih.gov/docs/aids/aids_data.html.
[14] National Cancer Institute database (NCI 3D), dtp.nci.nih.gov (Developmental Therapeutics
Program NCI/NIH).
[15] H. Hofer, C. Borgelt and M.R. Berthold, Large scale mining of molecular fragments with
wildcards, in
Intelligent Data Analysis
8
, 495-504 (2004).
[16] T. Meinl, C. Borgelt and M.R. Berthold, Mining fragments with fuzzy chains in molecular data-
bases, in
Proceedings of theWorkshopW7 on Mining Graphs
,
Trees and Sequences (MGTS '04)
,
ed. J.N. Kok and T. Washio, Pisa, 2004.
[17] T. Meinl, M. Wörlein, O. Urzova, I. Fischer and M. Philippsen, The ParMol package for
frequent subgraph mining, in
Proceedings of the 3rd International Workshop on Graph
Based Tools
, ed. T. Margaria-Steffen, J. Padberg and G. Taentzer, Electronic Communica-
tions of EASST 1, European Association of Software Science and Technology, Berlin, 2006;
http://www2.informatik.uni-erlangen.de/Forschung/Projekte/ParMol/?language=en.
[18] C. Helma, S. Kramer and L. De Raedt, The molecular feature Miner MOLFEA, in
Proceed-
ings of the Beilstein-Institut Workshop
, ed. M.G. Hicks and C. Kettner, Molecular Informatics:
Confronting Complexity, Logos Verlag, Berlin, 2003.
[19] R.D. King, A. Srinivasan and L. Dehaspe, Warmr: a data mining tool for chemical data,
J. Comput.-Aided Mol. Des
.,
15
, 173-181 (2001).
[20] L. Dehaspe and H. Toivonen, Discovery of frequent DATALOG patterns,
Data Min. Knowl.
Discov
.,
3
, 7-36 (1999).
[21] R.P. Sheridan and M.D. Miller, Amethod for visualizing recurrent topological substructures in
sets of active molecules,
J. Chem. Inf. Comput. Sci
.,
38
, 915-924 (1998).
[22] R.P. Sheridan, The most common chemical replacements in drug-like compounds,
J. Chem. Inf.
Comput. Sci
.,
42
, 103-108 (2002).
[23] R.P. Sheridan, Finding multiactivity substructures by mining databases of drug-like compounds,
J. Chem. Inf. Comput. Sci
.,
43
, 1037-1059 (2003).
[24] M. Vinkers, M. De Jonge, F. Daeyaert, J. Heeres, L. Koymans, J. Van Lenthe, P. Lewi,
H. Timmerman, K. Van Aken and P. Janssen, SYNOPSIS: SYNthesize and OPtimize System
in Silico,
J. Med. Chem
.,
46
, 2765-2773 (1998).
[25] M.H. Todd, Computer-aided organic synthesis,
Chem. Soc. Rev
.,
34
, 247-266 (2005).
[26] M. Vieth and M. Siegel, Structural fragments in marketed oral drugs, in
Fragment-
based Approaches in Drug Discovery
, ed. W. Jahnke and D.A. Erlanson, Methods and
Principles in Medicinal Chemistry, Vol. 34, Wiley-VCH Verlag GmbH, Weinheim, 2006,
pp. 113-124.
[27] E.-W. Lameijer, J.N. Kok, T. Bäck andA.P. IJzerman, Mining a chemical database for fragment
co-occurrence: discovery of 'chemical clichés',
J. Chem. Inf. Model
.,
46
, 553-562 (2006).
[28] G.W. Bemis and M.A. Murcko, The properties of known drugs. 1. Molecular frameworks,
J. Med. Chem
.,
39
, 2887-2893 (1996).