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MAGNETIC: Multilayer Attention and Graph Neural Network Diffusion for Effective Drug-Target Interaction Prediction.

D. Papadopoulos Bin Liu Fragkiskos D. Malliaros Grigorios Tsoumakas
Aug 2026 · IEEE journal of biomedical and health informatics · Vol PP · 0 citations
Medicine

TL;DR

Results reveal that MAGNETIC consistently out performs baselines on both the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), indicating improved identification and ranking of true interactions under class imbalance.

Abstract

Drug target interaction (DTI) prediction is a critical task in drug discovery, as it has the potential to accelerate the identification of promising drug-target interactions, thus leading to more efficient and focused wet lab validations. Our central premise is that modeling DTI as a multilayer attributed heterogeneous graph and learning layer-specific representations with diffusion-based graph neural networks (GNNs) improves predictive performance over conventional single-view graph learning and matrix factorization approaches. To test this premise, we pro pose Multilayer Attention and Graph Neural nETwork dIffusion for effeCtive Drug-Target Interaction prediction (MAG NETIC). MAGNETIC is a graph auto-encoder framework that represents drugs and targets in a multiplex network. It further incorporates meta-path-derived drug-drug and target-target similarity relations. MAGNETIC trains a separate Adaptive Graph Diffusion Network (AGDN) per layer to preserve layer-specific topology and attributes, then combines embeddings through learnable layer weights to produce a unified representation for DTI inference. The model is optimized with a reconstruction objective tailored to the sparse and imbalanced DTI setting. We evaluate MAGNETIC against strong graph neural network and matrix factorization baselines across multiple experimental scenarios. Results reveal that MAGNETIC consistently out performs baselines on both the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The largest gains are observed in AUPRC, indicating improved identification and ranking of true interactions under class imbalance. Finally, a real-world validation study on previously undiscovered DTIs provides additional evidence of the model's practical utility for novel interaction discovery.

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