Processing and classification of chemical data inspired by insect olfaction

Michael Schmuker, Gisbert Schneider

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)


The chemical sense of insects has evolved to encode and classify odorants. Thus, the neural circuits in their olfactory system are likely to implement an efficient method for coding, processing, and classifying chemical information. Here, we describe a computational method to process molecular representations and classify molecules. The three-step approach mimics neurocomputational principles observed in olfactory systems. In the first step, the original stimulus space is sampled by "virtual receptors," which are chemotopically arranged by a self-organizing map. In the second step, the signals from the virtual receptors are decorrelated via correlation-based lateral inhibition. Finally, in the third step, olfactory scent perception is modeled by a machine learning classifier. We found that signal decorrelation during the second stage significantly increases the accuracy of odorant classification. Moreover, our results suggest that the proposed signal transform is capable of dimensionality reduction and is more robust against overdetermined representations than principal component scores. Our olfaction-inspired method was successfully applied to predicting bioactivities of pharmaceutically active compounds with high accuracy. It represents a way to efficiently connect chemical structure with biological activity space.

Original languageEnglish
Pages (from-to)20285-9
Number of pages5
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number51
Publication statusPublished - 18 Dec 2007


  • Animals
  • Computer Simulation
  • Insects
  • Neural Networks (Computer)
  • Olfactory Receptor Neurons
  • Pharmaceutical Preparations
  • Smell
  • Structure-Activity Relationship


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