Inferring candidate drug side effects via Heterogeneous Graph Neural Networks on multilayer networks

Artificial Intelligence (AI) has become a central methodology in health science, enabling data-driven modelling and inference in several biomedical domains. In recent years, AI-based approaches have been increasingly employed in pharmacology and pharmacovigilance to detect, characterize, and predict Drug Side-Effects (DSEs). Concurrently, network science has provided a principled framework for representing the highly interconnected nature of biological systems, including drug–target, protein–protein, and disease–gene interaction networks. In this context, Graph Neural Networks (GNNs) have emerged as a powerful tool for operating on graph-structured data, by propagating and aggregating information across nodes and edges. In this paper, we present a method for inferring candidate DSEs by using a Heterogeneous GNN and a multilayer network model. Specifically, we treated the issue as a link prediction problem by applying a GNN to a heterogeneous topology, consisting of drug-drug , DSE and chemical drug-gene associations, modelled on a multilayer network. It jointly exploits drug-drug interactions, drug-gene relationships, and known DSE associations, to effectively integrate information across layers. Our method was based on a relation-weighted heterogeneous GNN model that learns the relative importance of different association types while enabling multi-hop message passing. The experimentation demonstrated that the proposed method consistently outperforms baseline GNN architectures, achieving improved discrimination and robustness on large-scale data. Furthermore, results highlight the effectiveness of relation-aware multilayer modelling for candidate DSE inference, e.g., in supporting pharmacovigilance and drug development.