Self-Organizing Neural Networks for Screening and Development of Novel Artificial Sweetener Candidates

Jaroslaw      Polanski; Johann      Gasteiger; Krystyna      Jarzembek

Abstract

The use of Kohonen feature maps for the visualization of various aspects of molecular similarity is briefly reviewed and illustrated. It is shown that a specific feature of self-organizing maps (SOM) makes them of special interest for the screening of compounds. In particular, these methods were used to design candidates for new sweeteners, which were then synthesized.

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Combinatorial Chemistry & High Throughput Screening

Title: Self-Organizing Neural Networks for Screening and Development of Novel Artificial Sweetener Candidates

Volume: 3 Issue: 6

Author(s): Jaroslaw Polanski, Johann Gasteiger and Krystyna Jarzembek

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Abstract: The use of Kohonen feature maps for the visualization of various aspects of molecular similarity is briefly reviewed and illustrated. It is shown that a specific feature of self-organizing maps (SOM) makes them of special interest for the screening of compounds. In particular, these methods were used to design candidates for new sweeteners, which were then synthesized.

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Polanski Jaroslaw, Gasteiger Johann and Jarzembek Krystyna, Self-Organizing Neural Networks for Screening and Development of Novel Artificial Sweetener Candidates, Combinatorial Chemistry & High Throughput Screening 2000; 3 (6) . https://dx.doi.org/10.2174/1386207003331427

DOI https://dx.doi.org/10.2174/1386207003331427	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402

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