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HNEDTI: Prediction of drug-target interaction based on heterogeneous network embedding

机译：HNEDTI：基于异构网络嵌入的药物-靶标相互作用预测

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Identifying drug-target interactions (DTIs) is an important task in drug discovery. Various computational models have been proposed to predict potential association between drugs and targets. However, it is still a great challenge to accurately predict the potential drug-target interactions with rare known drug-target interactions. In this work, we propose a heterogeneous network embedding model to predict drug-target interactions, called HNEDTI. Based on the assumption that similar drugs share similar patterns of relationships with target proteins, we integrate the drug-drug similarity network, target-target similarity network and known drug-target interactions into a heterogeneous network. HNEDTI can learn more accurate feature representation of drugs and targets by extract both local and global information of the heterogeneous network from different lengths of meta-paths. The low dimensional feature representation vectors of drugs and targets are applied to random forest model to predict whether the given drug-target pair has an interaction. The evaluation on four benchmark datasets (Enzyme, Ion Channel, GPCR and Nuclear Receptor) shows that our method HNEDTI outperforms the previous methods.
机译：鉴定药物目标相互作用（DTIS）是药物发现中的重要任务。已经提出了各种计算模型来预测药物和目标之间的潜在关联。然而，准确预测与稀有已知的药物 - 靶靶相互作用的潜在药物靶相互作用仍然是一个巨大挑战。在这项工作中，我们提出了一种异质的网络嵌入模型，以预测药物 - 目标相互作用，称为HNedti。基于类似药物与靶蛋白的关系模式的假设，我们将药物 - 药物相似性网络，目标目标相似性网络和已知的药物 - 目标相互作用集成到异构网络中。 HNedti通过从不同长度的元路径中提取异构网络的本地和全局信息，了解更准确的药物和目标的特征表示。药物和靶标的低尺寸特征表示载体应用于随机林模型，以预测给定的药物 - 目标对是否具有相互作用。对四个基准数据集（酶，离子通道，GPCR和核受体）的评价表明，我们的HNEDTI优于先前的方法。

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