Recursive Neural Networks for Cheminformatics: QSPR for Polymeric Compounds (Towards Biomaterials Design)

Authors:
A. Micheli;C. Bertinetto;C. Duce;R. Solaro;M. R. Tiné
Affiliations:
Department of Computer Science, University of Pisa, Italy;Department of Chemistry and Industrial Chemistry, University of Pisa, Italy;Department of Chemistry and Industrial Chemistry, University of Pisa, Italy;Department of Chemistry and Industrial Chemistry, University of Pisa, Italy;Department of Chemistry and Industrial Chemistry, University of Pisa, Italy
Venue:
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Year:
2009

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Abstract

We report on the latest advances and applications of an innovative cheminformatics approach based on neural networks for structures (Recursive Neural Networks), which has recently been employed for the QSPR analysis of polymeric compounds with diverse structures. This work presents a comprehensive survey of the obtained prediction results on acrylic and methacrylic polymers, including statistical copolymers. The flexibility of the described method supports its exploitation for the study and development of new molecules and materials of biomedical interest.