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Application of neural networks to structure–sandalwood odour relationships
Author(s) -
Zakarya Driss,
Cherqaoui Driss,
Esseffar M'Hamed,
Villemin Didier,
Cense JeanMichel
Publication year - 1997
Publication title -
journal of physical organic chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 66
eISSN - 1099-1395
pISSN - 0894-3230
DOI - 10.1002/(sici)1099-1395(199708)10:8<612::aid-poc923>3.0.co;2-y
Subject(s) - sandalwood , chemistry , artificial neural network , steric effects , artificial intelligence , biological system , stereochemistry , traditional medicine , computer science , medicine , biology
Abstract Neural networks have proved to be particularly successful in their ability to identify non‐linear relationships. This paper shows that a three‐layer back‐propagation neural network is able to learn the relationship between the sandalwood odour and molecular structures of 85 organic compounds belonging to acyclic, cyclohexyl, norbornyl, campholenyl and decalin derivatives. Four steric and three electronic parameters were used to describe each molecular structure. Odour was coded by a binary variable. The neural network was used to classify the compounds into two groups and to predict their odours (sandalwood or non‐sandalwood). The results obtained were compared with those given by discriminant analysis, and found to be better. The most important descriptors were revealed on the basis of correlation analysis. © 1997 John Wiley & Sons, Ltd.