A Multi-Modality Framework for Drug-Drug Interaction Prediction by Harnessing Multi-source Data
A Multi-Modality Framework for Drug-Drug Interaction Prediction by Harnessing Multi-source Data
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DOI：
10.1145/3583780.3614765
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Qianlong Wen;Jiazheng Li;Chuxu Zhang;Yanfang Ye
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文献类型：
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作者：
Qianlong Wen;Jiazheng Li;Chuxu Zhang;Yanfang Ye
药物-药物相互作用（DDI），作为药物联合治疗的可能结果，可能导致不良的生理反应和增加患者的死亡率。因此，潜在DDI的预测一直是医疗卫生应用中的一个重要而具有挑战性的问题。由于广泛的药理学研究，我们可以获得各种与药物相关的特征来预测DDI；然而，现有的DDI预测工作大多没有纳入全面的特征来分析DDI模式。尽管已有的研究成果已经取得了很高的性能，但有限来源产生的不完整和有噪声的信息通常会导致性能不优，对未知DDI对的泛化能力较差。在这项工作中，我们提出了一个整体框架，即药物-药物相互作用预测的多模态特征最优融合（MOF-DDI），该框架结合了来自多个数据源的特征来解决DDI预测。具体而言，该模型综合考虑ddi文献描述、生物医学知识图谱和药物分子结构进行预测。为了克服以不同方式直接聚合特征所带来的问题，我们在组合之前将从不同来源学习到的表示映射到统一的隐藏空间，从而带来了新的见解。实证结果表明，与多个最先进的基线相比，MOF-DDI在不同的DDI数据集上获得了较大的性能增益，特别是在归纳设置下。
Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.