针对罕见但高危药物相互作用，提出多模态融合预测框架
Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction
- 融合药物图结构、分子结构、靶点与酶信息进行多模态建模
- 在4个长尾数据集上超越当前最佳方法，尤其提升罕见类型准确率
- 设计新损失函数缓解极端长尾数据下的梯度消失问题，适合药理研究者
药物-药物相互作用（DDI）识别是药物研发的关键环节。现有DDI类型达数百种，分布极不均衡，部分罕见类型风险极高，一旦遗漏可能危及生命，构成典型的长尾分布难题。现有模型难以应对此类分布偏斜，且忽视药物的多维特性。本文提出一种基于深度学习的多模态框架TFDM，融合药物的图结构、分子结构、靶点与酶等多维度特征，实现更精准的DDI分类。为解决类别分布偏斜问题，引入新型损失函数Tailed Focal Loss，有效缓解焦损函数在极端长尾数据中的梯度消失现象。在4个具有挑战性的长尾数据集上的大量实验表明，该方法在罕见类型预测上显著优于当前最先进模型。代码已开源：https://github.com/IcurasLW/TFMD_Longtailed_DDI.git
原文摘要 · Abstract (English)
Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graph-based, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git
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