Analysis of the interaction network relationship between drugs using a graph neural network

Introduction The ever-increasing complexity of biochemical systems, alongside the rapid growth of pharmaceutical and biomedical data, underscores the urgent need for intelligent, scalable, and interpretable computational models. These models must be capable of supporting next-generation decision-support systems and driving knowledge discovery in the realm of computational science. Traditional approaches to relational biomedical modeling, however, often struggle to accurately capture intricate multi-relational dependencies and typically lack robustness in sparse or incomplete interaction domains. To address these pressing limitations, we present a novel, biologically grounded graph-based learning framework designed to overcome such challenges. Methods Our approach comprises a two-tiered system: PHARMNet, a multi-relational graph neural network (GNN) equipped with memory-augmented attention mechanisms, and INTERACT-SCOPE, an advanced, context-aware optimization strategy that leverages structured biomedical ontologies and domain knowledge. PHARMNet employs relation-specific graph convolutions and semantic embedding alignment to effectively model latent relational dependencies in biochemical and pharmacological datasets. In parallel, INTERACT-SCOPE improves predictive generalization and stability by incorporating ontology-guided constraints, estimating epistemic uncertainty, and applying adaptive graph regularization techniques tailored to biomedical structures. Results and Discussion Through rigorous experimental evaluations across a variety of pharmacological interaction categories, our framework consistently achieves state-of-the-art (SOTA) predictive performance, enhanced model interpretability, and notable robustness—especially in low-data or high-noise scenarios. These outcomes strongly align with the journal’s mission to promote innovative and knowledge-driven advances in software engineering, artificial intelligence, and biomedical informatics. Ultimately, our article illustrates the synergistic integration of computational intelligence, domain-informed graph representation learning, and scalable modeling, contributing a powerful and interpretable solution to real-world challenges in healthcare informatics and biomedical discovery. Experimental results demonstrate that MGTNSyn outperforms existing methods, achieving an AUC of 0.873 and an F1-score of 0.831 on drug–drug interaction (DDI) benchmark datasets.