用多尺度图神经网络预测药物相互作用，还能评估结果可信度。
A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction
- 通过迭代消息传递学习分子从局部到全局的多尺度结构特征。
- 跨药物注意力机制融合多尺度表示，提升交互对预测精度。
- 内置不确定性估计，适合临床用药安全与精准医疗场景。
准确预测药物-药物相互作用（DDI）对用药安全和药物研发至关重要。现有方法常难以捕捉从局部功能基团到全局分子拓扑的多尺度结构信息，且缺乏置信度量化机制。为此，我们提出MPNP-DDI，一种新型多尺度图神经过程框架。其核心是迭代式消息传递机制，可逐层学习多尺度图表示；关键在于跨药物共注意力机制动态融合这些表示，生成上下文感知的药物对嵌入；同时集成神经过程模块实现合理的不确定性估计。大量实验表明，MPNP-DDI在基准数据集上显著优于现有最优方法。该模型基于多尺度结构特征，提供准确、可泛化且具备置信度评估的预测，是药物流行病学监测、多重用药风险评估与精准医疗的强大计算工具。
原文摘要 · Abstract (English)
Accurate prediction of drug-drug interactions (DDI) is crucial for medication safety and effective drug development. However, existing methods often struggle to capture structural information across different scales, from local functional groups to global molecular topology, and typically lack mechanisms to quantify prediction confidence. To address these limitations, we propose MPNP-DDI, a novel Multi-scale Graph Neural Process framework. The core of MPNP-DDI is a unique message-passing scheme that, by being iteratively applied, learns a hierarchy of graph representations at multiple scales. Crucially, a cross-drug co-attention mechanism then dynamically fuses these multi-scale representations to generate context-aware embeddings for interacting drug pairs, while an integrated neural process module provides principled uncertainty estimation. Extensive experiments demonstrate that MPNP-DDI significantly outperforms state-of-the-art baselines on benchmark datasets. By providing accurate, generalizable, and uncertainty-aware predictions built upon multi-scale structural features, MPNP-DDI represents a powerful computational tool for pharmacovigilance, polypharmacy risk assessment, and precision medicine.
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