Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.

中文翻译：

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HF-DDI：基于多模态混合融合预测药物相互作用事件。

药物相互作用（DDI）会对患者的安全和健康产生重大影响。在给患者用药之前预测潜在的 DDI 是药物开发的关键一步，有助于预防药物不良事件。在这项研究中，我们提出了一种称为 HF-DDI 的新方法，用于根据各种药物特征（包括分子结构、靶标和酶信息）预测 DDI 事件。具体来说，我们设计了具有早期融合和晚期融合策略的模型，并利用分数计算模块来预测药物之间相互作用的可能性。我们的模型在已知 DDI 的大型数据集上进行了训练和测试，总体准确率达到 0.948。结果表明，整合多种药物特征可以提高 DDI 事件预测的准确性，并可能有助于改善药物安全性和患者治疗结果。