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Application of Cascade Correlation Networks for Structures to Chemistry

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Abstract

We present the application of Cascade Correlation for structures to QSPR (quantitative structure-property relationships) and QSAR (quantitative structure-activity relationships) analysis. Cascade Correlation for structures is a neural network model recently proposed for the processing of structured data. This allows the direct treatment of chemical compounds as labeled trees, which constitutes a novel approach to QSPR/QSAR. We report the results obtained for QSPR on Alkanes (predicting the boiling point) and QSAR of a class of Benzodiazepines. Our approach compares favorably versus the traditional QSAR treatment based on equations and it is competitive with ‘ad hoc’ MLPs for the QSPR problem.

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Authors and Affiliations

  1. Dipartimento di Scienze Farmaceutiche, Via Bonanno 6, 56126, Pisa, Italy

    Anna Maria Bianucci

  2. Dipartimento di Informatica, Università di Pisa, Corso Italia, 40, 56125, Pisa, Italy

    Alessio Micheli, Alessandro Sperduti & Antonina Starita

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  1. Anna Maria Bianucci
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  2. Alessio Micheli
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  3. Alessandro Sperduti
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  4. Antonina Starita
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Bianucci, A.M., Micheli, A., Sperduti, A. et al. Application of Cascade Correlation Networks for Structures to Chemistry. Applied Intelligence 12, 117–147 (2000). https://doi.org/10.1023/A:1008368105614

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