License
When quoting this document, please refer to the following
URN: urn:nbn:de:0030-drops-20367
URL: http://drops.dagstuhl.de/opus/volltexte/2009/2036/
Go to the corresponding Portal


Walsh, Ian ; Vullo, Alessandro ; Pollastri, Gianluca

An adaptive model for learning molecular endpoints

pdf-format:
Document 1.pdf (625 KB)


Abstract

I will describe a recursive neural network that deals with undirected graphs, and its application to predicting property labels or activity values of small molecules. The model is entirely general, in that it can process any undirected graph with a finite number of nodes by factorising it into a number of directed graphs with the same skeleton. The model's only input in the applications I will present is the graph representing the chemical structure of the molecule. In spite of its simplicity, the model outperforms or matches the state of the art in three of the four tasks, and in the fourth is outperformed only by a method resorting to a very problem-specific feature.

BibTeX - Entry

@InProceedings{walsh_et_al:DSP:2009:2036,
  author =	{Ian Walsh and Alessandro Vullo and Gianluca Pollastri},
  title =	{An adaptive model for learning molecular endpoints},
  booktitle =	{Similarity-based learning on structures},
  year =	{2009},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  number =	{09081},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2009/2036},
  annote =	{Keywords: Recursive neural networks, qsar, qspr, small molecules}
}

Keywords: Recursive neural networks, qsar, qspr, small molecules
Seminar: 09081 - Similarity-based learning on structures
Issue Date: 2009
Date of publication: 23.06.2009


DROPS-Home | Fulltext Search | Imprint Published by LZI