Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
This addresses a critical problem in medical health machine learning for predicting drug interactions, but it appears incremental as it builds on existing graph-based and contrastive learning techniques.
The paper tackles drug-drug interaction prediction by introducing MIRACLE, a multi-view graph contrastive learning method that captures molecule structures and interactions, and it outperforms state-of-the-art models on multiple real datasets.
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community. This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. MIRACLE treats a DDI network as a multi-view graph where each node in the interaction graph itself is a drug molecular graph instance. We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage, respectively. Also, we propose a novel unsupervised contrastive learning component to balance and integrate the multi-view information. Comprehensive experiments on multiple real datasets show that MIRACLE outperforms the state-of-the-art DDI prediction models consistently.