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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network
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LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network

机译：农历：基于代表学习图卷积网络的新型冠状病毒药物筛查

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An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.
机译：2019年底开始的Covid-19爆发是由新的冠状病毒（SARS-COV-2）造成的。它已成为一个全球大流行。截至2020年6月9日，它感染了近700万人并造成40多万，但没有特定的药物。因此，迫切需要查找或发展更多的药物来抑制病毒。在这里，我们提出了一个名为Lunar的新的非线性端到端模型。它使用图表卷积神经网络自动学习复杂异构关系网络的邻域信息，并结合了注意机制来反映不同类型的邻域信息和获得每个节点的表示特征的重要性。最后，通过拓扑重建过程，强制提取药物和目标的特征表示以尽可能地匹配观察到的网络。通过这种重建过程，我们获得不同节点之间的关系的强度，并预测基于Covid-19的已知目标可能影响Covid-19的治疗的药物候选。这些选定的候选药物可作为实验科学家的参考，加速药物发育的速度。 Lunar可以在异构网络中整合各种拓扑结构信息，巧妙地结合注意力机制来反映不同类型节点的邻里信息的重要性，提高了模型的解释性。模型的曲线（AUC）下的区域为0.949，使用10倍交叉验证，准确的召回曲线（AUPR）为0.866。这两个性能指标表明该模型具有卓越的预测性能。此外，我们模型中筛选的一些药物出现在一些临床研究中，以进一步说明模型的有效性。

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