A Multi-Source drug combination and Omnidirectional feature fusion approach for predicting Drug-Drug interaction events

Background: In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.

Methods: This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction. We utilized the real-world DrugBank dataset, which contains rich drug information. Our task was to predict multiple interaction events between drug pairs and analyze the underlying mechanisms of these interactions. The MMDDI model achieves precise predictions through four key stages: feature extraction, drug pairing strategy, fusion network, and multi-source feature integration. We employed advanced data fusion techniques and machine learning algorithms for multidimensional analysis of drug features and interaction events.

Results: The MMDDI model was comprehensively evaluated on three representative prediction tasks. Experimental results demonstrated that the MMDDI model outperforms existing technologies in terms of predictive accuracy, generalization ability, and interpretability. Specifically, the MMDDI model achieved an accuracy of 93% on the test set, and the area under the AUC-ROC curve reached 0.9505, showing excellent predictive performance. Furthermore, the model's interpretability analysis revealed the complex relationships between drug features and interaction mechanisms, providing new insights for clinical medication decisions.

Conclusion: The MMDDI model not only improves the accuracy of DDI prediction but also provides significant scientific support for clinical medication safety and drug development by deeply analyzing the mechanisms of drug interactions. These findings have the potential to improve patient medication outcomes and contribute to the development of personalized medicine.