用交叉注意力GNN提升药物相互作用机制分类，效果显著优于传统方法。
From Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation
- 采用交叉注意力机制建模药物间原子级交互，捕捉机制特异性信息。
- 在86类相互作用中F1-macro提升45%，远超传统方法，验证机制识别能力。
- 适合药物研发中需精准预测相互作用类型的场景，如新药安全评估。
预测两种药物是否发生相互作用（二分类）与预测其相互作用机制类型（多分类）是本质不同的任务。本研究在包含38,337个正样本对、涵盖86种机制类型的公开基准数据集上，系统比较了三种图神经网络架构在相同训练条件（共61,339对）下的表现：双分支消息传递神经网络（MPNN）拼接结构（Concat）、四头交叉注意力双分支结构（CrossAtt），以及引入交互图的三分支结构（Ternary）。结果表明，CrossAtt在多分类F1-macro上比Concat绝对提升0.186（+45%），而二分类AUC仅提升0.012（+1.3%），证实原子级分子间通信对机制分类至关重要。尽管训练数据量相当，但Ternary性能更差，其失败现象与训练不稳定性假设一致。对十对阿司匹林（ASA）药物对的独立验证显示，CrossAtt全部预测正确（10/10），而Ternary全错（0/10）。所有模型均存在两个持续性失败案例，与前期毒性研究中揭示的结构限制相关。
原文摘要 · Abstract (English)
Predicting whether two drugs interact (binary detection) is a substantially dif- ferent task from predicting the mechanism type of that interaction (multi-class classification). This study presents a systematic ablation study of three Graph Neural Network (GNN) architectures for drug-drug interaction (DDI) prediction on a publicly available benchmark dataset comprising 38,337 positive pairs across 86 interaction types. Three architectures are compared under identical training conditions (n = 61,339 pairs): a siamese dual Message Passing Neural Network (MPNN) with concatenation (Concat), a dual MPNN with four-head cross-attention (CrossAtt), and a ternary MPNN incorporating an interaction graph (Ternary). CrossAtt improves multi-class F1-macro by +0.186 absolute (+45%) over Concat, while improving binary AUC by only +0.012 (+1.3%) - confirming that atom-level inter-molecular communication specifically enables mechanism-type classification. The ternary architecture underperforms despite equivalent training data, with its failure consistent with a training instability hypothesis. Validation on ten acetylsali- cylic acid (ASA) drug pairs, held out prior to training, demonstrates 10/10 correct DDI-type predictions for CrossAtt versus 0/10 for Ternary. Two consistent failure cases are identified across all architectures, linking to structural limits established in a companion toxicity study.
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