Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

Abstract

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.

arXiv commentIn Proceedings of the Web Conference 2021 (WWW '21)

References in corpus (23)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A Comprehensive Survey on Graph Neural Networks
• DeepWalk: Online Learning of Social Representations
• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Neural Message Passing for Quantum Chemistry
• Molecular Graph Convolutions: Moving Beyond Fingerprints
• Learning deep representations by mutual information estimation and maximization
• Modeling polypharmacy side effects with graph convolutional networks
• Fast Graph Representation Learning with PyTorch Geometric
• struc2vec: Learning Node Representations from Structural Identity
• Representation Learning on Graphs with Jumping Knowledge Networks
• Variational Graph Auto-Encoders
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• Interaction Networks for Learning about Objects, Relations and Physics
• Highway Networks
• Deep Graph Contrastive Representation Learning
• Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
• Link Prediction Based on Graph Neural Networks
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
• Drug-Drug Adverse Effect Prediction with Graph Co-Attention
• Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

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• Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
• AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange