Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
复制标题
DOI：
10.18653/v1/p18-2108
复制
发表时间：
2018-05
期刊：
影响因子：
--
通讯作者：
Masaki Asada;Makoto Miwa;Yutaka Sasaki
中科院分区：
文献类型：
--
作者：
Masaki Asada;Makoto Miwa;Yutaka Sasaki
本文提出了一种利用外部药物分子结构信息从文本中提取药物-药物相互作用（ddi）的神经网络方法。我们用卷积神经网络编码文本药物对，用图卷积网络编码分子对，然后我们将这两个网络的输出连接起来。在实验中，我们发现GCNs可以高精度地从药物的分子结构中预测DDI，并且分子信息可以使基于文本的DDI提取在DDIExtraction 2013共享任务数据集上的f得分提高2.39%。
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.