标题： MUDI：理解药效药物-药物相互作用的多模态生物医学数据集 显示英文标题

标题： MUDI: A Multimodal Biomedical Dataset for Understanding Pharmacodynamic Drug-Drug Interactions

摘要： 理解不同药物之间的相互作用（药物-药物相互作用或DDI）对于确保患者安全和优化治疗效果至关重要。现有的DDI数据集主要关注文本信息，忽视了反映复杂药物机制的多模态数据。本文（1）介绍了MUDI，这是一个大规模的多模态生物医学数据集，用于理解药效学药物-药物相互作用，并（2）评估学习方法来研究它。简而言之，MUDI通过结合310,532个标注的药物对（标记为协同作用、拮抗作用或新效应）的药理学文本、化学公式、分子结构图和图像，提供了药物的全面多模态表示。关键的是，为了有效评估基于机器学习的泛化能力，MUDI测试集中包含未见过的药物对。我们使用晚期融合投票和中间融合策略评估基准模型。所有数据、注释、评估脚本和基线都以开放研究许可的形式发布。 显示英文摘要

摘要： Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.