MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
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DOI：
10.1093/bib/bbab421
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发表时间：
2022-01-17
影响因子：
9.5
通讯作者：
Wei, Dong-Qing
中科院分区：
文献类型：
--
作者：
Lin, Shenggeng;Wang, Yanjing;Wei, Dong-Qing
联合使用多种药物的主要问题之一是，它可能会导致不良的药物相互作用和损害身体的副作用。因此，预测潜在的药物相互作用非常重要。然而，大多数现有的预测方法只能预测两种药物是否相互作用，而很少有方法可以预测两种药物之间的相互作用事件。准确预测两种药物的相互作用事件对于研究两种药物相互作用的机制具有重要意义。在本研究中，我们提出了一种新的方法MDF-SA-DDI，该方法基于多源药物融合、多源特征融合和Transformer自注意机制来预测药物相互作用（DDI）事件。MDF-SA-DDI主要由两部分组成：多源药物融合和多源特征融合。首先，我们将两种药物以四种不同的方式进行联合收割机组合，并将组合后的药物特征表示输入到四种不同的药物融合网络（连体网络、卷积神经网络和两个自编码器）中，得到药物对的潜在特征向量，其中两个自编码器具有相同的结构，它们的主要区别是两个自编码器输入层的神经元数目不同。然后，我们使用包含自注意机制的Transformer块来执行潜在特征融合。我们用两个数据集对三个不同的任务进行了实验。在小数据集上，我们的方法在任务1上的精确召回曲线下面积（AUPR）和F1得分分别达到0.9737和0.8878，这比最先进的方法更好。在大型数据集上，我们的方法在任务1上的AUPR和F1得分分别达到0.9773和0.9117。在两个数据集的任务2和任务3中，我们的方法也取得了与最先进的方法相同或更好的性能。更重要的是，对五个DDI事件进行了案例研究，并取得了令人满意的效果。源代码和数据可在https://github.com/ShenggengLin/MDF-SA-DDI上获得。
One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.