Publications (2)0 Total impact
Article: Quantitative structure-Odor relationship: Using of multidimensional data analysis and neural network approaches[show abstract] [hide abstract]
ABSTRACT: Structure-odor relationships (SOR) are key issues for the synthesis of new odorant molecules. But, this relation is hard to model, due to limited understanding of olfaction phenomena and the subjectivity of odor quantity and quality as stated in Rossitier's review (1996). Many molecular descriptors are used to correlate molecule's odor, but no universal rules emerge in this field. In this paper, we focus on the use of molecular descriptors as an alternative approach in the prediction of odors, by the mean of regression techniques. Principal Component Analysis (PCA) and Stepwise Collinearity Diagnosis (SCD) techniques are used to reduce the dimensionality of data, by the identification of significant molecular descriptors. Then, the chosen molecular descriptors are used with a neural networks algorithm to correlate the structure to molecular odor quality. The results are validated on balsamic flavor. Â© 2006 Elsevier B.V. All rights reserved.Computer Aided Chemical Engineering. 01/2006; 21:895-900.
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ABSTRACT: Computer Aided Product Design (CAPD) is widely used in process system engineering as a powerful tool for searching novel chemicals. The crucial steps in CAPD are the generation of candidate molecules and the estimation of properties, especially when complex molecular structures like flavors are sought. In this paper, we present a multiclass molecular knowledge framework which is based on chemical graph theory and chemical knowledge. Three kinds of functional groups are defined: elementary, basic and composed groups. These serve to generate four classes of knowledge that can be useful for property estimation and molecular design. An Input/output structure basing on XML language is defined to favor the interoperability between softwares. Â© 2006 Elsevier B.V. All rights reserved.Computer Aided Chemical Engineering. 01/2006; 21:889-894.