Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
This work addresses the challenge of high manual adjustment costs in subgraph-based methods for drug-drug interaction prediction, which is crucial for medical practice and drug development, representing an incremental improvement.
The study tackled the problem of customizing subgraph selection and encoding for drug-drug interaction prediction by proposing a neural architecture search-based method to efficiently explore optimal configurations, demonstrating effectiveness and superiority in experiments.
Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs), which are essential for medical practice and drug development. Subgraph selection and encoding are critical stages in these methods, yet customizing these components remains underexplored due to the high cost of manual adjustments. In this study, inspired by the success of neural architecture search (NAS), we propose a method to search for data-specific components within subgraph-based frameworks. Specifically, we introduce extensive subgraph selection and encoding spaces that account for the diverse contexts of drug interactions in DDI prediction. To address the challenge of large search spaces and high sampling costs, we design a relaxation mechanism that uses an approximation strategy to efficiently explore optimal subgraph configurations. This approach allows for robust exploration of the search space. Extensive experiments demonstrate the effectiveness and superiority of the proposed method, with the discovered subgraphs and encoding functions highlighting the model's adaptability.