Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction

Haotong Du, Quanming Yao, Juzheng Zhang, Yang Liu, Zhen Wang

Keywords: Drug-drug Interaction Prediction, Neural Architecture Search, Graph Neural Networks

TL;DR: We propose to efficiently and robustly search for data-specific subgraph-based pipeline components for drug-drug interaction prediction.

Abstract:

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.

Primary Area: Machine learning for healthcare

Submission Number: 18289