Molecular graph convolutions: moving beyond fingerprints
Venue
Journal of Computer-Aided Molecular Design (2016), pp. 1-14
Publication Year
2016
Authors
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley
BibTeX
Abstract
Molecular “fingerprints” encoding structural information are the workhorse of
cheminfor- matics and machine learning in drug discovery applications. However,
fingerprint representa- tions necessarily emphasize particular aspects of the
molecular structure while ignoring others, rather than allowing the model to make
data- driven decisions. We describe molecular graph convolutions, a fully
integrated machine learn- ing architecture for learning from undirected graphs,
such as small molecules. Graph convo- lutions use a simple encoding of the
molecular graph (atoms, bonds, distances, etc.), allowing the model to take full
advantage of information in the graph structure.
