MGDDI: A multi-scale graph neural networks for drug–drug interaction prediction

Guannan Geng, Lizhuang Wang, Yanwei Xu, Tianshuo Wang, Wei Ma, Hongliang Duan, Jiahui Zhang, Anqiong Mao

Research output: Contribution to journalArticlepeer-review


Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

Original languageEnglish
Pages (from-to)22-29
Number of pages8
Publication statusPublished - Aug 2024


Dive into the research topics of 'MGDDI: A multi-scale graph neural networks for drug–drug interaction prediction'. Together they form a unique fingerprint.

Cite this