He Haohuai, Chen Guanxing, Yu-Chian Chen Calvin
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China.
Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac134.
Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificial intelligence methods predict and mine potential DDI, they ignore the 3D structure information of drug molecules and do not fully consider the contribution of molecular substructure in DDI.
We proposed a new deep learning architecture, 3DGT-DDI, a model composed of a 3D graph neural network and pre-trained text attention mechanism. We used 3D molecular graph structure and position information to enhance the prediction ability of the model for DDI, which enabled us to deeply explore the effect of drug substructure on DDI relationship. The results showed that 3DGT-DDI outperforms other state-of-the-art baselines. It achieved an 84.48% macro F1 score in the DDIExtraction 2013 shared task dataset. Also, our 3D graph model proves its performance and explainability through weight visualization on the DrugBank dataset. 3DGT-DDI can help us better understand and identify potential DDI, thereby helping to avoid the side effects of drug mixing.
The source code and data are available at https://github.com/hehh77/3DGT-DDI.
药物相互作用（DDIs）发生在药物联合使用过程中。识别潜在的药物相互作用有助于我们研究联合用药背后的机制或不良反应，从而避免副作用。尽管许多人工智能方法可以预测和挖掘潜在的药物相互作用，但它们忽略了药物分子的三维结构信息，并且没有充分考虑分子亚结构在药物相互作用中的作用。
我们提出了一种新的深度学习架构3DGT-DDI，这是一个由三维图神经网络和预训练文本注意力机制组成的模型。我们使用三维分子图结构和位置信息来增强模型对药物相互作用的预测能力，这使我们能够深入探究药物亚结构对药物相互作用关系的影响。结果表明，3DGT-DDI优于其他现有的基准模型。在2013年DDIExtraction共享任务数据集中，它的宏观F1分数达到了84.48%。此外，我们的三维图模型通过在DrugBank数据集上的权重可视化证明了其性能和可解释性。3DGT-DDI可以帮助我们更好地理解和识别潜在的药物相互作用，从而有助于避免药物混合带来的副作用。
