用新型神经网络预测药物相互作用，更准且能区分方向性影响
MGKAN: Predicting Asymmetric Drug-Drug Interactions via a Multimodal Graph Kolmogorov-Arnold Network
- 引入可学习基函数的非线性图网络，突破传统模型对称假设
- 在两个数据集上超越7个主流模型，准确率显著提升
- 适合药物研发人员和精准医疗研究者参考
预测药物-药物相互作用（DDI）对安全用药至关重要。现有图神经网络模型多依赖线性聚合和对称假设，难以捕捉非线性和异质性模式。本文提出MGKAN，一种基于图柯尔莫哥洛夫-阿诺德网络的不对称DDI预测方法。该模型将可学习基函数引入药物关系建模，取代传统MLP，实现更强的非线性表达能力。为捕获药理依赖性，MGKAN融合三个网络视图——不对称DDI网络、共作用网络和生化相似性网络，并采用角色特定嵌入保持方向语义。通过线性注意力与非线性变换的融合模块增强表征能力。在两个基准数据集上，MGKAN优于七个先进基线模型。消融实验与案例分析验证其预测精度与建模方向效应的有效性。
原文摘要 · Abstract (English)
Predicting drug-drug interactions (DDIs) is essential for safe pharmacological treatments. Previous graph neural network (GNN) models leverage molecular structures and interaction networks but mostly rely on linear aggregation and symmetric assumptions, limiting their ability to capture nonlinear and heterogeneous patterns. We propose MGKAN, a Graph Kolmogorov-Arnold Network that introduces learnable basis functions into asymmetric DDI prediction. MGKAN replaces conventional MLP transformations with KAN-driven basis functions, enabling more expressive and nonlinear modeling of drug relationships. To capture pharmacological dependencies, MGKAN integrates three network views-an asymmetric DDI network, a co-interaction network, and a biochemical similarity network-with role-specific embeddings to preserve directional semantics. A fusion module combines linear attention and nonlinear transformation to enhance representational capacity. On two benchmark datasets, MGKAN outperforms seven state-of-the-art baselines. Ablation studies and case studies confirm its predictive accuracy and effectiveness in modeling directional drug effects.
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