Jiang Zhenyu, Ding Pingjian, Shen Cong, Dai Xiaopeng
IEEE J Biomed Health Inform. 2024 Dec;28(12):7623-7632. doi: 10.1109/JBHI.2024.3453956. Epub 2024 Dec 5.
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs for systematic and effective treatment. Existing deep learning-based methods often rely on DDI functional networks, or use them as an important part of the model information source. However, it is difficult to discover the interactions of a new drug. To address the above limitations, we propose a geometric molecular graph representation learning model (Mol-DDI) for DDI prediction based on the basic assumption that structure determines function. Mol-DDI only considers the covalent and non-covalent bond information of molecules, then it uses the pre-training idea of large-scale models to learn drug molecular representations and predict drug interactions during the fine-tuning process. Experimental results show that the Mol-DDI model outperforms others on the three datasets and performs better in predicting new drug interaction experiments.
药物相互作用（DDI）会在患者身上引发多种不良反应，已成为医学和公共卫生领域的一大威胁。因此，预测潜在的药物相互作用非常重要，因为它可以为系统有效的治疗提供药物组合策略。现有的基于深度学习的方法通常依赖于DDI功能网络，或将其用作模型信息源的重要组成部分。然而，发现新药的相互作用却很困难。为了解决上述局限性，我们基于结构决定功能这一基本假设，提出了一种用于DDI预测的几何分子图表示学习模型（Mol-DDI）。Mol-DDI仅考虑分子的共价键和非共价键信息，然后利用大规模模型的预训练思想来学习药物分子表示，并在微调过程中预测药物相互作用。实验结果表明，Mol-DDI模型在三个数据集上均优于其他模型，并且在预测新药相互作用实验中表现更佳。
