## 5 Conclusions and Discussion  
We introduce an innovative approach, called DDI-SSL, geared toward detecting drug-to-drug interactions (DDIs) through mastering substructural blueprints, potent in aiding the forecast of medication adverse reactions. First, we analyze that previous methods relying on global structures may lead to inaccurate results in handling drug information. Leveraging a self-focus mechanism, we derive potency ratings for subgraphs, and every substructure is annotated with pertinent drug attributes scaled by adaptable weightings, enabling the output of variably sized and non-uniformly contoured outcomes. Due to the variable receptive field of message passing, our method has high scalability. Moreover, we accurately extract information from local drug subgraphs to identify the corresponding modules of the composed individuals. Subgraph formulas allow the projection of local structural details of attribute maps onto subgraph signature sets, which reduces noise by anchoring relevant information, thereby achieving modular representation of complex topological structures. Finally, we model the mutual influence between drugs by introducing a cooperative attention mechanism, which effectively avoids noise in molecular structures and properties. Thorough investigations on DDI data collections validate the enhancement brought by the DDI-SSL model and pinpoint the significance of each advancement aspect. Future research will leverage message passing and other methods to capture the topological relationships and semantic information of larger-scale protein atoms and bonds by representing nodes and edges in graph networks. In addition to drug-drug interactions, research in the field of graph neural networks has also made progress in learning protein structure and predicting temporal dynamics. However, challenges remain in protein dynamics prediction tasks, such as the need to dynamically capture complex structural spatiotemporal changes and use long-range correlations at different time scales. To better understand the physiological mechanisms behind protein dynamics, a new interaction modeling graph neural network framework that captures long-range dynamic spatiotemporal correlations is urgently needed to provide prediction and interpretation for protein dynamics research in target interactions. It is important to acknowledge that while the DDI-SSL method shows promise in enhancing drug-drug interaction predictions, there are several limitations to consider. First, the performance of DDI-SSL may be influenced by the quality and size of the available data, and the method's effectiveness could vary depending on the specific dataset used. Additionally, DDI-SSL's success may be constrained by the completeness and accuracy of the underlying drug interaction data, which can vary across different sources. **Real-World Impact:** Accurate predictions of drug-drug interactions hold immense potential for improving various aspects of healthcare. In particular, such advancements can significantly enhance patient safety, streamline drug development processes, and reduce healthcare costs. **Patient Safety:** Accurate predictions enable healthcare providers to proactively identify potential interactions between prescribed medications, thereby minimizing the risk of adverse effects and harmful drug interactions. This has a direct impact on patient safety, ensuring that treatments are both effective and safe. **Drug Development Efficiency:** Drug development is a costly and time-consuming process. Accurate predictions of drug interactions allow pharmaceutical companies to identify potential issues early in the development pipeline, saving time and resources. This can expedite the release of safe and effective medications to the market. **Healthcare Cost Reduction:** Avoiding drug-drug interactions can lead to a reduction in hospitalizations, emergency room visits, and other healthcare expenses associated with adverse drug events. This, in turn, can contribute to overall healthcare cost reduction.