A novel memory interaction neural network for multi-label drug-drug interaction prediction with neighbor importance sampling.

Abstract

Co-administration of multiple drugs can frequently cause drug-drug interactions (DDIs), including adverse drug reactions (ADRs) that may increase the likelihood of morbidity and mortality. Identifying potential DDIs presents a significant challenge, due to the complexity of pharmacology. Recent advances in knowledge graphs have contributed to DDI prediction by providing a robust framework for representing various relationships between drugs and other entities, such as proteins, diseases, and drug attributes. However, current network-based models often fail to uncover interaction information among DDI triplets, as they typically encode triplets independently. Additionally, uniform sampling methods may overlook differences in neighboring node properties. In this work, we propose a novel memory interaction neural network for DDI prediction, which integrates drug molecular sequences with semantic information from the drug knowledge graph. Specifically, we introduce a neighbor importance sampling strategy that selectively samples highly connected neighbors, improving computational efficiency and reducing noise. We also design a memory interaction module that utilizes multi-head attention mechanisms and deep neural networks to capture interactions among DDI triplets. Experimental evaluation on KEGG and OGB-biokg datasets demonstrates the superiority of our model compared to classical and state-of-the-art methods in predicting DDIs. Datasets and code for this proposed DDIs prediction model are freely accessible at https://github.com/wj1108114106/Multi-label-DDIs.