Graph Neural Network-Based Prediction of Synergistic Drug Interactions from Multilayer Biological Networks
Keywords:
graph neural networks; drug synergy; multilayer networks; network medicine; biomedical artificial intelligence; systems governanceAbstract
Synergistic drug interactions are foundational to combination therapy, yet their prediction remains a persistent challenge because therapeutic outcomes emerge from multilayered biological interactions across molecular targets, pathways, protein interaction networks, disease modules, and patient-specific contexts. This paper presents a systems-oriented examination of graph neural network architectures for predicting synergistic drug interactions from multilayer biological networks. It integrates perspectives from network medicine, representation learning, computational infrastructure, and science governance. The discussion emphasizes structural trade-offs among encoder depth, attention mechanisms, message passing, scalability, and interpretability. Attention is given to the integration of pharmacogenomic, pathway, protein interaction, and molecular signature resources, as well as to the governance mechanisms required to sustain data quality and provenance. The paper further considers deployment constraints, fairness implications, robustness under distribution shift, and regulatory expectations in biomedical machine learning systems. Rather than treating predictive accuracy as the sole objective, the paper argues that trustworthy synergy prediction requires a broader institutional design that coordinates model architecture, data standards, audit procedures, and clinical translation pathways. A balanced framework is proposed in which model performance is pursued alongside transparency, equity, reproducibility, and operational resilience. The synthesis contributes a scholarly systems perspective for researchers, infrastructure designers, and policy stakeholders developing machine learning systems for polypharmacy, precision therapeutics, and complex biomedical discovery.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
