Modular multi-source prediction of drug side-effects with DruGNN

TL;DR

This paper introduces DruGNN, a graph neural network model that integrates heterogeneous biological data into a graph to predict drug side-effects, aiming to improve early detection and reduce costs in drug development.

Contribution

The work presents a novel graph dataset representing relational biological data and applies GNNs for drug side-effect prediction, highlighting the importance of data relationships.

Findings

Relational graph data improves prediction accuracy.

Certain data subsets are crucial for identifying drug-side effect associations.

GNNs outperform traditional models in this task.

Abstract

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.