mlsanityData to 14 Oct 2026

Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction

arXiv 2411.01535 ↗First posted 3 Nov 2024cs.LG cs.AI q-bio.BMcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

44 of 71 references.

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Predictioncited by 6
  2. 2025Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interactioncited by 2
  3. 2025Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agendacited by 0
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