Identifying drug-target interactions (DTIs) is an important step in the process of new drug discovery and drug repositioning. Accurate predictions for DTIs can improve the efficiency in the drug discovery and development. Although rapid advances in deep learning technologies have generated various computational methods, it is still appealing to further investigate how to design efficient networks for predicting DTIs. In this study, we propose an end-to-end deep learning method (called MHSADTI) to predict DTIs based on the graph attention network and multi-head self-attention mechanism. First, the characteristics of drugs and proteins are extracted by the graph attention network and multi-head self-attention mechanism, respectively. Then, the attention scores are used to consider which amino acid subsequence in a protein is more important for the drug to predict its interactions. Finally, we predict DTIs by a fully connected layer after obtaining the feature vectors of drugs and proteins. MHSADTI takes advantage of self-attention mechanism for obtaining long-dependent contextual relationship in amino acid sequences and predicting DTI interpretability. More effective molecular characteristics are also obtained by the attention mechanism in graph attention networks. Multiple cross validation experiments are adopted to assess the performance of our MHSADTI. The experiments on four datasets, human, C.elegans, DUD-E and DrugBank show our method outperforms the state-of-the-art methods in terms of AUC, Precision, Recall, AUPR and F1-score. In addition, the case studies further demonstrate that our method can provide effective visualizations to interpret the prediction results from biological insights.

中文翻译：

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使用多头自注意力和图注意力网络的药物-目标相互作用预测

识别药物-靶点相互作用 (DTI) 是新药发现和药物重新定位过程中的重要一步。对 DTI 的准确预测可以提高药物发现和开发的效率。尽管深度学习技术的快速发展已经产生了各种计算方法，但进一步研究如何设计有效的网络来预测 DTI 仍然很有吸引力。在这项研究中，我们提出了一种基于图注意力网络和多头自注意力机制的端到端深度学习方法（称为 MHSADTI）来预测 DTI。首先，分别通过图注意力网络和多头自注意力机制提取药物和蛋白质的特征。然后，注意分数用于考虑蛋白质中的哪个氨基酸子序列对于药物预测其相互作用更重要。最后，我们在获得药物和蛋白质的特征向量后，通过一个全连接层来预测 DTI。MHSADTI 利用自我注意机制获得氨基酸序列中的长期依赖上下文关系并预测 DTI 可解释性。更有效的分子特征也通过图注意力网络中的注意力机制获得。采用多个交叉验证实验来评估我们的 MHSADTI 的性能。四个数据集的实验，人类，MHSADTI 利用自我注意机制获得氨基酸序列中的长期依赖上下文关系并预测 DTI 可解释性。更有效的分子特征也通过图注意力网络中的注意力机制获得。采用多个交叉验证实验来评估我们的 MHSADTI 的性能。四个数据集的实验，人类，MHSADTI 利用自我注意机制获得氨基酸序列中的长期依赖上下文关系并预测 DTI 可解释性。更有效的分子特征也通过图注意力网络中的注意力机制获得。采用多个交叉验证实验来评估我们的 MHSADTI 的性能。四个数据集的实验，人类，C.elegans、DUD-E 和 DrugBank 表明我们的方法在 AUC、Precision、Recall、AUPR 和 F1 分数方面优于最先进的方法。此外，案例研究进一步表明，我们的方法可以提供有效的可视化来解释生物学见解的预测结果。