DDI-AttendNet: cross attention with structured graph learning for inter-drug connectivity analysis - Wang, Jing; Du, Huili; Li, Yuanlei (Frontiers in Pharmacology, 2026)
IntroductionIn the context of interdisciplinary computational science and its increasingly vital role in advancing applied computer-aided drug discovery, the accurate characterization of inter‐drug connectivity is essential for identifying synergistic therapeutic effects, mitigating adverse reactions, and optimizing polypharmacy strategies. Traditional computational approaches—such as similarity-based screening, molecular docking simulations, or conventional graph convolutional networks—often struggle with a range of limitations, including incomplete relational structures, lack of scalability to complex molecular systems, restricted model interpretability, and an inability to capture the multi-level hierarchical nature of chemical interactions and pharmacological effects. These constraints hinder the full potential of data‐driven strategies in complex biomedical environments. MethodsTo address these pressing challenges, we introduce DDI-AttendNet, a novel cross-attention architecture integrated with structured graph learning mechanisms. Our model explicitly encodes both molecular topologies and inter-drug relational dependencies by leveraging dual graph encoders, one dedicated to learning intra‐drug atomic interactions and the other to capturing the broader inter-drug relational graph. The model’s centerpiece is a cross-attention module, which dynamically aligns and contextualizes functionally relevant substructures across interacting drug pairs, allowing for more nuanced predictions. Built upon the foundation described in our methodology section, DDI‐AttendNet is evaluated on multiple large-scale DDI benchmark datasets.ResultsThe results demonstrate that our model consistently and significantly outperforms state‐of‐the‐art baselines, with observed improvements exceeding 5%–10% in AUC and precision‐recall metrics. Attention weight visualization contributes to improved interpretability, allowing researchers to trace predictive outcomes back to chemically meaningful features. DiscussionThese advancements affirm DDI-AttendNet’s capability to model complex drug interaction structures and highlight its potential to accelerate safer and more efficient data-driven drug discovery pipelines.