DDI-AttendNet: Cross Attention with Structured Graph Learning for Inter-Drug Connectivity Analysis

Jing Wang^*Huili Du

• Xinxiang Central Hospital, Xinxiang, China

In 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. To 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. The 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. These 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.