Topology Preserving Neural Networks for Peptide Design in Drug Discovery

  • Jörg D. Wichard
  • Sebastian Bandholtz
  • Carsten Grötzinger
  • Ronald Kühne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5488)


We describe a construction method and a training procedure for a topology preserving neural network (TPNN) in order to model the sequence-activity relation of peptides. The building blocks of a TPNN are single cells (neurons) which correspond one-to-one to the amino acids of the peptide. The cells have adaptive internal weights and the local interactions between cells govern the dynamics of the system and mimic the topology of the peptide chain. The TPNN can be trained by gradient descent techniques, which rely on the efficient calculation of the gradient by back-propagation. We show an example how TPNNs could be used for peptide design and optimization in drug discovery.


Neural Network Training Sample Drug Discovery Training Error Peptide Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jörg D. Wichard
    • 1
  • Sebastian Bandholtz
    • 2
  • Carsten Grötzinger
    • 2
  • Ronald Kühne
    • 1
  1. 1.FMP BerlinBerlinGermany
  2. 2.Department of Hepatology and GastroenterologyCharité Universitätsmedizin BerlinBerlinGermany

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