Das Bihter, Kutsal Mucahit, Das Resul
Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey.
Chemometr Intell Lab Syst. 2022 Oct 15;229:104640. doi: 10.1016/j.chemolab.2022.104640. Epub 2022 Aug 24.
Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.
尽管新冠病毒疫情随着奥密克戎变种迅速蔓延，但在疫苗和免疫力的作用下，其致死率有所下降。住院率和强烈需求也有所降低。然而，关于这种疾病何时会结束或不同变种可能有多危险，尚无确切信息。此外，自然界中在动物间持续传播的变种所带来的风险也无法消除。在此阶段之后，应该研究药物与病毒的相互作用，以便能够针对可能出现的新型病毒和变种做好准备，并快速生产针对可能出现的病毒的药物或疫苗。尽管实验方法昂贵、费力且耗时，但几何深度学习（GDL）是一种可用于使这一过程更快、更便宜的替代方法。在本研究中，我们提出了一种基于几何深度学习的新模型，用于预测针对新冠病毒的药物与病毒相互作用。首先，我们使用SMILES分子结构表示中的抗病毒药物数据来生成大量特征，并更好地描述化学物质的结构。然后将数据转换为分子表示，再转换为GDL模型能够理解的图形结构。通过消息传递神经网络（MPNN）将节点特征向量转移到不同空间，以便进行训练过程。我们开发了一种几何神经网络架构，其中图嵌入值通过全连接层传递并实现预测。结果表明，所提出的方法在预测药物与病毒相互作用方面以97%的准确率优于现有方法。
