Adverse Drug Event Prediction with a Multi-Layer Heterogeneous Graph Neural Network Architecture
Document Type
Conference Article
Publication Title
2024 IEEE 21st India Council International Conference Indicon 2024
Abstract
Drug-drug interactions (DDIs) and their associated adverse drug effects (ADEs) pose significant challenges in polypharmacy, particularly for patients with complex or co-occurring conditions. Existing methods often struggle to capture the intricate relationships between drugs, proteins, and the problem of multiple ADEs between two drug interactions. We introduce MI-GNN, a novel multi-layered graph neural network framework designed to address the multiple ADE problem between two drugs while capturing the dependencies between drug and target proteins for predicting DDIs. Our approach models the adverse events as multiple layers, allowing information flow between layers to capture inter-dependencies among ADEs. We validated MI-GNN's efficacy by performing ADE prediction tasks on the benchmark dataset while comparing it with state-of-the-art prediction algorithms. Experimental results demonstrate that MI-GNN effectively predicts ADEs while incorporating knowledge gained from drug and protein embeddings. Our proposed model outperforms the baseline established in previous work by more than 2%. MI-GNN offers a promising approach for modeling complex polypharmacy side effects by leveraging multi-modal graph structures and capturing interdependencies among drugs, proteins, and ADEs.
DOI
10.1109/INDICON63790.2024.10958347
Publication Date
1-1-2024
Recommended Citation
Dawn, Sucheta; Chakraborty, Mrittika; Maulik, Ujjwal; and Bandyopadhyay, Sanghamitra, "Adverse Drug Event Prediction with a Multi-Layer Heterogeneous Graph Neural Network Architecture" (2024). Conference Articles. 824.
https://digitalcommons.isical.ac.in/conf-articles/824