Generalization Rules for Binarized Descriptors | SpringerLink
Skip to main content

Generalization Rules for Binarized Descriptors

  • Conference paper
Biological and Medical Data Analysis (ISBMDA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4345))

Included in the following conference series:

  • 950 Accesses

Abstract

Virtual screening of molecules is one of the hot topics in life science. Often, molecules are encoded by descriptors with numerical values as a basis for finding regions with a high enrichment of active molecules compared to non-active ones. In this contribution we demonstrate that a simpler binary version of a descriptor can be used for this task as well with similar classification performance, saving computational and memory resources. To generate binary valued rules for virtual screening, we used the GenIntersect algorithm that heuristically determines common properties of the binary descriptor vectors. The results are compared to the ones achieved with numerical rules of a neuro-fuzzy system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ajay: Predicting Drug-Likeness: Why and How? Current Topics in Medicinal Chemistry 2(12), 1273–1286 (2002)

    Article  Google Scholar 

  2. Xu, H.: Retrospect and Prospect of Virtual Screening in Drug Discovery. Current Topics in Medicinal Chemistry 2(12), 1305–1320 (2002)

    Article  Google Scholar 

  3. Böhm, H.-J., Schneider, G.: Virtual Screening for Bioactive Molecules. Wiley VCH, Weinheim (2000)

    Book  Google Scholar 

  4. Lyne, P.D.: Structure-Based Virtual Screening: An Overview. Drug Discovery Today 7(20), 1047–1055 (2002)

    Article  Google Scholar 

  5. Schneider, G., Böhm, H.-J.: Virtual Screening and Fast Automated Docking Methods. Drug Discovery Today 7(1), 64–70 (2002)

    Article  Google Scholar 

  6. Borgelt, C., Berthold, M.R.: Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In: Proc. of the 2nd IEEE Int. Conf. on Data Mining (ICDM), Maebashi City, Japan, pp. 51–58 (2002)

    Google Scholar 

  7. Todeschini, T., Consonni, V.: Handbook of Molecular Descriptors. Wiley-VCH, Weinheim (2000)

    Book  Google Scholar 

  8. Schneider, G., Neidhart, W., Giller, T., Schmid, G.: Scaffold Hopping by Topological Pharmacophore Search: A Contribution to Virtual Screening, Angewandte Chemie. International Edition 38(19), 2894–2895 (1999)

    Article  Google Scholar 

  9. Schneider, P., Schneider, G.: Collection of Bioactive Reference Compounds for Focused Library Design. QSAR & Combinatorial Science 22, 713–718 (2003)

    Article  Google Scholar 

  10. Huber, K.-P., Berthold, M.R.: Building Precise Classifiers with Automatic Rule Extraction. In: Proc. of the IEEE Int. Conf. on Neural Networks (ICNN), Perth, Western Australia, pp. 1263–1268. Univ. of Western Australia (1995)

    Google Scholar 

  11. Paetz, J.: Metric Rule Generation with Septic Shock Patient Data. In: Proc. of the 1st Int. Conf. on Data Mining (ICDM), San Jose, CA, USA, pp. 637–638 (2001)

    Google Scholar 

  12. Paetz, J.: Knowledge Based Approach to Septic Shock Patient Data Using a Neural Network with Trapezoidal Activation Functions, Artificial Intelligence in Medicine. Special Issue on Knowledge-Based Neurocomputing in Medicine 28(2), 207–230 (2003)

    Google Scholar 

  13. Berthold, M.R.: Mixed Fuzzy Rule Formation. International Journal of Approximate Reasoning 32, 67–84 (2003)

    Article  MATH  Google Scholar 

  14. Fechner, U., Paetz, J., Schneider, G.: Comparison of Three Holographic Fingerprint Descriptors and Their Binary Counterparts. QSAR & Combinatorial Science 24, 961–967 (2005)

    Article  Google Scholar 

  15. Paetz, J.: Intersection Based Generalization Rules for the Analysis of Symbolic Septic Shock Patient Data. In: Proc. of the 2nd IEEE Int. Conf. on Data Mining (ICDM), Maebashi City, Japan, pp. 673–676 (2002)

    Google Scholar 

  16. Beyer, H.-G.: An Alternative Explanation for the Manner in Which Genetic Algorithms Operate. BioSystems 41, 1–15 (1997)

    Article  Google Scholar 

  17. Paetz, J.: Durchschnittsbasierte Generalisierungsregeln Teil I: Grundlagen. Frankfurter Informatik-Berichte Nr. 1/02, Institut für Informatik, Fachbereich Biologie und Informatik, J.W. Goethe-Univ. Frankfurt am Main, Germany (2002) ISSN 1616–9107

    Google Scholar 

  18. Agrawal, R., Skrikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int. Conf. on Very Large Databases (VLDB), Santiago de Chile, Chile, pp. 487–499 (1994)

    Google Scholar 

  19. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  20. Paetz, J., Schneider, G.: Virtual Screening Using Local Neuro-Fuzzy Rules. In: Proc. of the 13th. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), Budapest, Hungary, pp. 861–866 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Paetz, J. (2006). Generalization Rules for Binarized Descriptors. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_7

Download citation

  • DOI: https://doi.org/10.1007/11946465_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68063-5

  • Online ISBN: 978-3-540-68065-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics