Modeling Drug-Drug Interactions Using Graph Attention Networks and Latent Alignment for Unsupervised Severity Prediction

Background: Drug-drug interactions (DDIs) represent a critical challenge in pharmacoepidemiology. There are frequent instances of patients being prescribed multiple medications concurrently. Certain combinations of two or more drugs can be contraindicated owing to their potential to lead to adverse clinical outcomes. This leads to backfiring of the well-intentioned prescription. Moreover, prediction tasks associated with DDI outcomes continue to represent a field with a strong potential for improvements, largely because of the absence of efficient modernistic approaches as well as reliable, comprehensive datasets. Objective: This study aims to explore a forward-looking paradigm, based on artificial intelligence, for predicting the outcomes of DDIs. Towards this aim, we use cutting-edge advances in natural language processing and graph-based learning architectures to render a capable model. Conceptual Design: The proposed framework employs an unsupervised learning approach that integrates both cross-attention and self-attention mechanisms. The system first represents drug entities as embeddings, aggregates them using attention-based pooling, and models their interactions through graph attention networks. Cross-attention is then incorporated to refine pairwise representations before outcome classification. The architectural paradigm presents a welcome opportunity for validation after rigorous experimentation which simulates its efficacy for the intended task. Contribution: This paper presents a proof-of-concept study for unsupervised prediction of drug–drug interaction impacts. It integrates cross-attention with self-attention and suggests a novel direction for improving the classification of interaction severity in the absence of large-scale labeled datasets. Conclusion: The work introduces a methodological innovation that demonstrates potential for improving DDI outcome prediction. It highlights a promising avenue for future research and simulation while advancing the reliability of AI-driven systems in pharmacoepidemiology.