用生成模型合成罕见药物相互作用数据，提升预测准确性
GFlowNets for Learning Better Drug-Drug Interaction Representations
- 结合生成流网络与变分图自编码器生成稀有交互样本
- 显著改善罕见药物相互作用的预测性能
- 适合药物安全预警和新药研发领域使用
药物-药物相互作用在临床药理学中构成重大挑战，其交互类型存在严重类别不平衡问题，导致预测模型效果受限。常见交互类型在数据集中占主导地位，而罕见但关键的交互类型则严重缺失，造成模型对低频案例表现不佳。现有方法通常将DDI预测视为二分类问题，忽略类别特异性特征，进一步加剧对高频交互的偏倚。为此，我们提出一种融合生成流网络（GFlowNet）与变分图自编码器（VGAE）的框架，用于生成稀有类别的合成样本，提升模型平衡性，并生成有效且新颖的药物相互作用对。该方法在各类交互类型上均提升了预测性能，增强临床应用可靠性。
原文摘要 · Abstract (English)
Drug-drug interactions pose a significant challenge in clinical pharmacology, with severe class imbalance among interaction types limiting the effectiveness of predictive models. Common interactions dominate datasets, while rare but critical interactions remain underrepresented, leading to poor model performance on infrequent cases. Existing methods often treat DDI prediction as a binary problem, ignoring class-specific nuances and exacerbating bias toward frequent interactions. To address this, we propose a framework combining Generative Flow Networks (GFlowNet) with Variational Graph Autoencoders (VGAE) to generate synthetic samples for rare classes, improving model balance and generate effective and novel DDI pairs. Our approach enhances predictive performance across interaction types, ensuring better clinical reliability.
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