Modular Multi-Source Prediction of Drug Side-Effects With DruGNN

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects.

Automated summary

Drug side-effects have a significant impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects before their occurrence is crucial to reduce this impact, especially in drug discovery. By integrating heterogeneous data into a graph dataset, this study successfully utilizes Graph Neural Networks (GNNs) to predict drug side-effects, showing promising results. The experimental results highlight the significance of utilizing relationships between data entities and suggest potential future developments in this field.