A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs
This work addresses the challenge of reducing drug-related hospitalizations and improving drug discovery by enabling automatic predictors to process drug structure information, though it appears incremental as it builds on existing graph neural network methods.
The paper tackled the problem of predicting drug side-effects by using a deep learning approach on molecular graphs, achieving improved classification capability across various parameters and metrics compared to previous predictors.
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.