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

Publication date: May 10, 2023

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.

Concepts Keywords
Drugs COVID-19
Genomes D3Similarity
Hydroxychloroquine DNA Sequencing
Pandemic Drug Repurposing
Siamese Graph Neural Network
Multi-view Learning
Next Gene Sequencing
Siamese Network


Type Source Name
disease MESH Covid-19
disease VO time
drug DRUGBANK Flunarizine
disease VO gene
drug DRUGBANK D-Tryptophan
drug DRUGBANK Hydroxychloroquine
disease VO effective
drug DRUGBANK Dexamethasone

Original Article

(Visited 1 times, 1 visits today)

Leave a Comment

Your email address will not be published. Required fields are marked *