Application of neural networks to structure-sandalwood odour relationships

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.