Leveraging a Context-Aware Graph Neural Network for Drug Target Discovery in Alzheimer's Disease

Overview

Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder characterized by extracellular Aß plaques, neurofibrillary tau tangles, and neuronal degeneration, involving multiple cell-types and pathways. Modeling protein function across these varied cellular and molecular contexts poses a significant algorithmic challenge. A context-aware model is needed to identify key drug targets in AD progression and enable targeted interventions. To address this, we developed ALZ-GNN, a context-aware graph neural network, using brain omics data spanning the aging-to-AD spectrum. ALZ-GNN was trained on 14,951 proteins and 206,850 protein interactions, and fine-tuned with AD risk genes. We constructed 48 cell-subtype specific protein networks using differentially expressed genes. ALZ-GNN uses neural message passing to update node embeddings across these graphs and leverages an attention bridge to align protein and cell-subtype representations, generating protein embeddings that capture the unique biological context of each cell-subtype. Investigating APOE, the largest genetic risk factor for AD, we found high embedding similarity in microglial, neuronal, and CD8 cells, suggesting similar roles across these cell-types. Additionally, pathway analysis of the vulnerable layer 2/3 excitatory neurons revealed potential molecular targets. ALZ-GNN outperformed state-of-the-art models with improvements of 70.83% in AP@5 and 72.92% in AUPRC, demonstrating its ability to rank AD risk genes and capture complex biological relationships. Our results demonstrate that ALZ-GNN provides a powerful computational framework for uncovering novel insights into AD neurobiology, with potential to revolutionize drug target discovery and accelerate the development of precision therapeutics.

Competition history

ISEF 2025 Computational Biology and Bioinformatics · Entry CBIO040

Resources

ISEF abstract page

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