Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

Pati, Soumen Kumar and Gupta, Manan Kumar and Banerjee, Ayan and Shai, Rinita and Shivakumara, Palaiahnakote (2024) Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network. Multimedia Tools and Applications, 83 (1). 61 – 95. ISSN 1380-7501, DOI https://doi.org/10.1007/s11042-023-15270-8.

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After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93, respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Item Type: Article
Uncontrolled Keywords: Convolutional neural networks; DNA; DNA sequences; Gene encoding; Graph neural networks; Optimization; Viruses; D3similarity; DNA Sequencing; Drug discovery; Drug repurposing; Gene sequencing; Graph neural networks; Multi-view learning; Next gene sequencing; Repurposing; Siamese network; COVID-19
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 12 Mar 2024 07:43
Last Modified: 12 Mar 2024 07:43
URI: http://eprints.um.edu.my/id/eprint/45053

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