Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints

Authors:
Ian Walsh;Alessandro Vullo;Gianluca Pollastri
Affiliations:
School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland
Venue:
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2009

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Abstract

Accurately predicting the endpoints of chemical compounds is an important step towards drug design and molecular screening in particular. Here we develop a recursive architecture that is capable of mapping Undirected Graphs into individual labels, and apply it to the prediction of a number of different properties of small molecules. The results we obtain are generally state-of-the-art. The final model is completely general and may be applied not only to prediction of molecular properties, but to a vast range of problems in which the input is a graph and the output is either a single property or (with small modifications) a set of properties of the nodes.