Abstract
We present a novel approach based on neural networks for structures to QSPR (quantitative structure-property relationships) and QSAR (quantitative structure-activity relationships) analysis. We face two quite different chemical applications using the same model, i.e. predicting the boiling point of a class of alkanes and QSAR of a class of benzodiazepines. The model, Cascade Correlation for structures, is a class of recursive neural networks recently proposed for the processing of structured domains. Through the use of this model we can represent and process the structure of chemical compounds in the form of labeled trees. We report our results on preliminary applications to show that the model, adopting this representation of molecular structure, can adaptively capture significant topological aspects and chemical fnnctionalities for each specific class of the molecules, just on the basis of the association between the molecular morphology and the target property.
Partially supported by MURST grant 9903244848 and MM09308497.
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Bianucci, A.M., Micheli, A., Sperduti, A., Starita, A. (2003). A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures. In: Cartwright, H.M., Sztandera, L.M. (eds) Soft Computing Approaches in Chemistry. Studies in Fuzziness and Soft Computing, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36213-5_10
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DOI: https://doi.org/10.1007/978-3-540-36213-5_10
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