Structure−Odor Relationships: Using Neural Networks in the Estimation of Camphoraceous or Fruity Odors and Olfactory Thresholds of Aliphatic Alcohols
Structure−odor relationships were established for a sample of 99 aliphatic alcohols using a three-layer backpropagation neural network. The molecular structure was described using a common skeleton with six possible substitutions. Substituents were described using only their van der Waals volumes. T...
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Published in | Journal of Chemical Information and Computer Sciences Vol. 36; no. 1; pp. 108 - 113 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
American Chemical Society
01.01.1996
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Subjects | |
Online Access | Get full text |
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Summary: | Structure−odor relationships were established for a sample of 99 aliphatic alcohols using a three-layer backpropagation neural network. The molecular structure was described using a common skeleton with six possible substitutions. Substituents were described using only their van der Waals volumes. The discrimination between fruity and camphoraceous odors of 67 compounds gave good results in classification (100%) and prediction (85%) phases. With the global set, the network correctly classified and predicted the camphoraceous character of compounds (100% and 95% respectively) but gave poorer results for the fruity character (87% and 74% respectively). Calculations of pOLs (pOL = −log (olfactory threshold expressed in mol/L)) of 45 camphoraceous compounds were also made. When all camphoraceous compounds were used to establish the model, 91% of the pOLs were correctly estimated. When attempts were made to predict the pOL values of 10% of the compounds from a model designed using 90% of the sample, only 74% of the pOLs were correctly estimated. |
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Bibliography: | U10 9606025 ark:/67375/TPS-LBRL3XVB-3 Abstract published in Advance ACS Abstracts, January 1, 1996. istex:71D061EB1418E8716846FC7CC93590B9AAF252B1 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0095-2338 1549-960X 1520-5142 |
DOI: | 10.1021/ci950154b |