Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction
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.
Code (1)
Tasks
Neural Architecture SearchSimilar Papers 제목 키워드 기반
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding inter…
Gene Interaction PredictionRepresentation LearningAccurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning
Background: Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. Howeve…
Graph Neural NetworkKnowledge GraphsCADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potent…
DecoderDrug–drug Interaction ExtractionGraph LearningBuilding explainable graph neural network by sparse learning for the drug-protein binding prediction
Explainable Graph Neural Networks (GNNs) have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. Howeve…
Graph Neural NetworkSparse LearningvalidTri-graph Information Propagation for Polypharmacy Side Effect Prediction
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolution…
Link PredictionPose PredictionPrediction