European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2005: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 1002-1013

Molecular Fragment Mining for Drug Discovery

  • Christian Borgelt
  • Michael R. Berthold
  • David E. Patterson
Conference paper

DOI: 10.1007/11518655_84

Volume 3571 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Borgelt C., Berthold M.R., Patterson D.E. (2005) Molecular Fragment Mining for Drug Discovery. In: Godo L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science, vol 3571. Springer, Berlin, Heidelberg

Abstract

The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules in order to find structural properties of molecules that determine whether a molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. A promising approach to this task was presented in [2]: an algorithm for finding molecular fragments that discriminate between active and inactive molecules. In this paper we review this approach as well as two extensions: a special treatment of rings and a method to find fragments with wildcards based on chemical expert knowledge.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christian Borgelt
    • 1
  • Michael R. Berthold
    • 2
  • David E. Patterson
    • 3
  1. 1.School of Computer ScienceOtto-von-Guericke-University of MagdeburgMagdeburgGermany
  2. 2.Department of Computer ScienceUniversity of KonstanzKonstanzGermany
  3. 3.Tripos Inc.St LouisUSA