Graph Representation Learning for Drug Discovery: Integrating Graph Neural Networks with Molecular Property Prediction and Explainable AI
Keywords:
graph representation learning; graph neural networks; molecular property prediction; explainable artificial intelligence; drug discovery; AI governance; socio-technical systems; robustness; fairness; sustainabilityAbstract
Graph representation learning has become a central computational paradigm in drug discovery because molecular entities are naturally structured as graphs of atoms, bonds, and stereochemical relationships. This paper examines the integration of graph neural networks, molecular property prediction, and explainable artificial intelligence from a system-level perspective. Rather than treating predictive performance as the sole criterion, the analysis foregrounds architectural trade-offs, data infrastructure, deployment constraints, robustness, fairness, sustainability, and governance. Graph neural networks provide inductive biases suited to molecular topology, but their expressiveness, scalability, and interpretability depend on choices about message passing, pooling, pretraining, and uncertainty estimation. Explainability methods can support medicinal chemistry by identifying substructures and interaction patterns, yet explanations must be validated against domain knowledge and must remain stable under distribution shift. The paper argues that drug discovery platforms require hybrid socio-technical architectures that couple model development with assay data governance, provenance tracking, human oversight, and regulatory documentation. Cross-domain comparisons with materials discovery, protein modeling, and clinical decision support illustrate recurring tensions between accuracy, transparency, and operational efficiency. The discussion also addresses environmental costs of large-scale training, equitable access to computational resources, and policy mechanisms for accountable artificial intelligence in pharmaceutical research. The conclusion proposes that future progress depends less on isolated algorithmic novelty than on interoperable infrastructures, reproducible benchmarks, and institutional arrangements that align graph representation learning with public health goals.
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