Abstract
In machine learning, there has been an increased interest in metrics on structured data. The application we focus on is drug discovery. Although graphs have become very popular for the representation of molecules, a lot of operations on graphs are NP-complete. Representing the molecules as outerplanar graphs, a subclass within general graphs, and using the block-and-bridge preserving subgraph isomorphism, we define a metric and we present an algorithm for computing it in polynomial time. We evaluate this metric and more generally also the block-and-bridge preserving matching operator on a large dataset of molecules, obtaining favorable results.
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Schietgat, L., Ramon, J., Bruynooghe, M., Blockeel, H. (2008). An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_20
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DOI: https://doi.org/10.1007/978-3-540-88411-8_20
Publisher Name: Springer, Berlin, Heidelberg
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