College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte 28660, Madrid, Spain.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab545.
The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.
该药物的性质可能会因组合而改变，这可能会导致意想不到的药物-药物相互作用（DDI）。DDI 的预测为药物的组合策略提供了系统和有效的治疗方法。在大多数基于深度学习的 DDI 预测方法中，药物的编码信息在某种程度上是不充分的，这限制了 DDI 预测的性能。在这项工作中，我们提出了一种用于 DDI 预测的新的基于注意力机制的多维特征编码器，即基于注意力的多维特征编码器（AMDE）。具体来说，在 AMDE 中，我们从多个维度编码药物特征，包括来自简化分子输入行系统序列和药物原子图的信息。数据实验是在从 Drugbank 中选择的 DDI 数据集上进行的，总共涉及 34282 个 DDI 关系，其中有 17141 个阳性 DDI 样本和 17141 个阴性样本。实验结果表明，我们的 AMDE 比一些最先进的基线方法表现更好，包括随机森林、一维卷积神经网络、DeepDrug、长短时记忆、Seq2seq、Deepconv、DeepDDI、图注意力网络和知识图神经网络。在实践中，我们选择了一组从未在训练、验证和测试集中出现过的 150 种药物，共 3723 个 DDI。AMDE 在 DDI 预测任务中表现良好，AUROC 和 AUPRC 分别为 0.981 和 0.975。此外，我们以托拉塞米（DB00214）为例，预测最有可能与它相互作用的药物。前 15 名的得分都在文献中有明确的相互作用报道。
