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An adaptive model for learning molecular endpoints

Authors Ian Walsh, Alessandro Vullo, Gianluca Pollastri



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Ian Walsh
Alessandro Vullo
Gianluca Pollastri

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Ian Walsh, Alessandro Vullo, and Gianluca Pollastri. An adaptive model for learning molecular endpoints. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2009)
https://doi.org/10.4230/DagSemProc.09081.3

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.
Keywords
  • Recursive neural networks
  • qsar
  • qspr
  • small molecules

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