Identifying Side Effects of Adverse Drug Reactions via Drug-Drug Interactions with Advanced Neural Network Techniques

Abstract

Adverse Drug Reactions (ADRs) due to drug-drug interactions pose a significant public health issue, impacting mortality, morbidity, and healthcare costs. The increasing complexity of therapeutics and aging populations intensify these challenges. Currently, no standard method exists to detect such ADRs before drugs reach the market, as rare interactions often emerge only after patient reports. Clinical trials struggle to capture these rare effects. Thus, a reliable technique to predict ADRs prior to drug release is urgently needed. We propose an effective framework that models drug-drug interactions using Graph Neural Networks (GNNs) and self-supervised learning. By representing drugs as molecular graphs, our approach leverages their spatial and physical properties to enhance predictive capabilities, offering a promising solution to mitigate ADR risks early in the drug development process.

Introduction

Adverse Drug Reactions (ADRs) are a major global health issue often caused by Drug-Drug Interactions (DDIs), which are complex and difficult to predict using traditional methods like clinical trials and pharmacovigilance. To improve ADR prediction, this study proposes a novel system that leverages Graph Neural Networks (GNNs) combined with Self-Supervised Learning (SSL).

The system models drugs as nodes and their interactions as edges in a graph, enabling it to capture intricate relationships between drugs. SSL helps the model learn from large amounts of unlabeled data, addressing the scarcity of labeled ADR data. The approach includes extracting molecular features (atom and bond level), generating graph embeddings, and classifying interactions to predict potential ADRs.

Experimental results show that the proposed GNN+SSL model significantly outperforms traditional machine learning techniques in accuracy, precision, recall, and overall predictive power, especially when pretrained weights are used. The system efficiently predicts ADR risks, which can assist healthcare providers in safer clinical decision-making. Challenges remain in predicting rare or novel drug interactions, but overall, the model offers a scalable and effective solution to improve drug safety.

Conclusion

In summary, research into the prediction of ADRs and DDIs has evolved significantly over the years, with machine learning and deep learning techniques showing considerable promise. Graph-based models, particularly Graph Neural Networks, have proven effective in representing and predicting drug interactions, while Self-Supervised Learning techniques have the potential to enhance model performance, especially when labeled data is limited. The integration of these approaches could lead to more accurate, scalable, and reliable ADR prediction systems, offering important insights into drug safety and benefiting both patients and healthcare providers.

References

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Copyright

Copyright © 2025 Karra Maheswari, G. Indiravathi, Mabbu Mythri, Bantupalli Lazar, J G Rajesh, Utukuri Reddy Kalyan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.