Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
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DOI：
10.1093/bioinformatics/bty535
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发表时间：
2019-01-15
期刊：
影响因子：
5.8
通讯作者：
Sese, Jun
中科院分区：
文献类型：
--
作者：
Tsubaki, Masashi;Tomii, Kentaro;Sese, Jun
动机：在生物信息学中，基于机器学习的化合物-蛋白质相互作用（CPI）预测方法在药物发现的虚拟筛选中发挥着重要作用。最近，使用深度神经网络对离散符号数据（例如自然语言处理中的单词）进行端到端表示学习，在各种困难问题上表现出出色的性能。对于CPI问题，数据被提供为离散符号数据，即化合物被表示为顶点是原子，边是化学键的图，蛋白质是字符是氨基酸的序列。在这项研究中，我们研究了化合物和蛋白质的端到端表示学习的使用，整合了表示，并通过结合化合物的图神经网络（GNN）和蛋白质的卷积神经网络（CNN）开发了一种新的CPI预测方法。我们使用三个CPI数据集进行的实验表明，所提出的端到端与现有的各种CPI预测方法相比，末端方法实现了具有竞争力或更高的性能。此外，所提出的方法显着优于现有的方法在一个不平衡的数据集。这表明，通过端到端GNN和CNN获得的化合物和蛋白质的数据驱动表示比从数据库获得的传统化学和生物特征更强大。尽管由于深度学习模型的黑箱性质，分析它们是困难的，但我们使用神经注意力机制来解决这个问题，这使我们能够在预测药物化合物的相互作用时考虑蛋白质中的哪些干扰对药物化合物更重要。神经注意力机制还提供了有效的可视化，这使得即使使用实值表示而不是离散特征来执行建模时也更容易分析模型。
Motivation: In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.Results: Our experiments using three CPI datasets demonstrated that the proposed end-to-end approach achieves competitive or higher performance as compared to various existing CPI prediction methods. In addition, the proposed approach significantly outperformed existing methods on an unbalanced dataset. This suggests that data-driven representations of compounds and proteins obtained by end-to-end GNNs and CNNs are more robust than traditional chemical and biological features obtained from databases. Although analyzing deep learning models is difficult due to their black-box nature, we address this issue using a neural attention mechanism, which allows us to consider which subsequences in a protein are more important for a drug compound when predicting its interaction. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model even when modeling is performed using real-valued representations instead of discrete features.