Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

中文翻译：

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基于特征表示学习和深度神经网络的基于学习的药物-药物相互作用预测方法。

药物-靶标相互作用的预测对于缩小候选药物的范围具有重要意义，因此是药物发现中至关重要的一步。由于生化实验的特殊性，新药的开发不仅成本高昂，而且费时。因此，药物靶标相互作用的计算预测已成为药物发现过程中必不可少的方法，旨在大大降低实验成本和时间。我们提出了一种基于学习的方法，该方法基于特征表示学习和名为DTI-CNN的深度神经网络来预测药物-靶标相互作用。我们首先使用Jaccard相似系数从异构网络中提取药物和蛋白质的相关特征，然后重新启动随机游动模型。然后，我们采用降噪自动编码器模型来缩小尺寸并确定基本特征。第三，基于从最后一步获得的特征，我们构建了卷积神经网络模型来预测药物和蛋白质之间的相互作用。评估结果表明，DTI-CNN的平均AUROC得分和AUPR得分分别为0.9416和0.9499，比其他三种现有的最新方法获得更好的性能。所有的实验结果表明，DTI-CNN的性能优于现有的三种方法，并且对该方法进行了适当的设计。评估结果表明，DTI-CNN的平均AUROC评分和AUPR评分分别为0.9416和0.9499，比其他三种现有的最新方法获得更好的性能。所有的实验结果表明，DTI-CNN的性能优于现有的三种方法，并且对该方法进行了适当的设计。评估结果表明，DTI-CNN的平均AUROC评分和AUPR评分分别为0.9416和0.9499，比其他三种现有的最新方法获得更好的性能。所有的实验结果表明，DTI-CNN的性能优于现有的三种方法，并且对该方法进行了适当的设计。