Structure-musk odour relationship studies of tetralin and indan compounds using neural networks

Abstract

Models of the relationships between structure and musk odour of tetralin and indan compounds were elaborated with a multilayer neural network using the back-propagation algorithm. The neural network was used to classify the compounds studied into two categories (musk or non-musk). The cross-validation procedure was used to assess the predictive power of the network. Each molecule was described by eight global parameters: five steric and three electronic descriptors. The neural network's results were successfully compared to those given by the k-Nearest Neighbours and the Bayesean methods, both in the classification and prediction tests. The contribution of each descriptor to the structure-odour relationships was evaluated. Three out of the eight descriptors were thus found to be the most relevant in the molecular description for the prediction of musk odour. This research points out that neural networks are likely to become a useful technique for structure-odour relationships.