Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reaction

Abstract

Adverse Drug Reactions (ADRs) resulting from drug combinations can endanger patients' health and life. In this paper, we propose ADGCL, a novel Adaptive Dual Graph Contrastive Learning based on heterogeneous signed networks for predicting ADRs. Specifically, ADGCL first explicitly models positive and negative drug-drug interactions (DDIs) using a heterogeneous signed network to learn semantic-rich drug feature representations. Additionally, ADGCL employs a dual graph contrastive learning strategy including self-supervised contrastive learning and micro-supervised learning to learn high-level features, which significantly improves prediction performance. Furthermore, ADGCL incorporates an adaptive negative sampling method to generate negative samples. Lastly, an encoder based on the implicit graph neural network is designed to capture long-range dependencies in underlying networks, thereby improving the quality of drug feature representation. Comprehensive experimental results on real-world datasets demonstrate that ADGCL is a promising ADR prediction model.