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. 2022 Oct 15:229:104640.
doi: 10.1016/j.chemolab.2022.104640. Epub 2022 Aug 24.

A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2

Bihter Das  1 , Mucahit Kutsal  1 , Resul Das  1
Affiliations

A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2

Bihter Das et al. Chemometr Intell Lab Syst. .

Abstract

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.

Keywords: Antiviral drugs; COVID-19; Drug-target combination; Geometric deep learning; Graph neural networks; Message passing neural network.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The flow diagram of the proposed MPNN model.
Fig. 2
Fig. 2
Transition of Oxaloacetate Ion drug from SMILES format to graph representation.
Fig. 3
Fig. 3
The used architecture in the fully connected network part.
Fig. 4
Fig. 4
The quantitative estimates of drug-virus interaction.
Fig. 5
Fig. 5
The result of the MPNN model for the ROC curve.
Fig. 6
Fig. 6
The accuracy performance of the MPNN model.
Fig. 7
Fig. 7
The F1-score performance of the MPNN model.
See this image and copyright information in PMC

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