  Predicting drug side-effects before they occur is a key task in keeping the
number of drug-related hospitalizations low and to improve drug discovery
processes. Automatic predictors of side-effects generally are not able to
process the structure of the drug, resulting in a loss of information. Graph
neural networks have seen great success in recent years, thanks to their
ability of exploiting the information conveyed by the graph structure and
labels. These models have been used in a wide variety of biological
applications, among which the prediction of drug side-effects on a large
knowledge graph. Exploiting the molecular graph encoding the structure of the
drug represents a novel approach, in which the problem is formulated as a
multi-class multi-label graph-focused classification. We developed a
methodology to carry out this task, using recurrent Graph Neural Networks, and
building a dataset from freely accessible and well established data sources.
The results show that our method has an improved classification capability,
under many parameters and metrics, with respect to previously available
predictors.
