Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks

Carlo Bertinetto, Celia Duce, Alessio Micheli, Roberto Solaro, Antonina Starita, Maria Rosaria Tiné*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

This paper reports some recent results from the empirical evaluation of different types of structured molecular representations used in QSPR analysis through a recursive neural network (RNN) model, which allows for their direct use without the need for measuring or computing molecular descriptors. This RNN methodology has been applied to the prediction of the properties of small molecules and polymers. In particular, three different descriptions of cyclic moieties, namely group, template and cycle break have been proposed. The effectiveness of the proposed method in dealing with different representations of chemical structures, either specifically designed or of more general use, has been demonstrated by its application to data sets encompassing various types of cyclic structures. For each class of experiments a test set with data that were not used for the development of the model was used for validation, and the comparisons have been based on the test results. The reported results highlight the flexibility of the RNN in directly treating different classes of structured input data without using input descriptors.

Original languageEnglish
Pages (from-to)797-802
Number of pages6
JournalJOURNAL OF MOLECULAR GRAPHICS AND MODELLING
Volume27
Issue number7
DOIs
Publication statusPublished - Apr 2009
MoE publication typeA1 Journal article-refereed

Keywords

  • Cheminformatics
  • Cyclic structure
  • Ionic liquids
  • Molecular representation
  • Poly(meth)acrylates
  • QSPR
  • Recursive neural network

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