No abstract

In this chapter, we will describe the various deep learning strategies used in detecting COVID-19pneumonia with the help of different models.

SARS-CoV-2 caused a global pandemic in March 2020. The disease has been characterised by various symptoms including loss of taste and smell, dizziness, encephalitis, encephalopathy, and cerebrovascular diseases. Cytopathic effects are shown to be induced post-infection which ultimately leads to organ failure. To date in addition to vaccine administration, various therapeutic strategies like convalescent plasma infusion, administration of the antiviral drug Remdesivir and using the immunomodulatory agents like chloroquine and hydroxychloroquine have been implemented. The concept of drug-repurposing has been extensively used wherein the efficacy of already known potent drugs is analyzed. Here, by using an online server “CoV-DrugX Pipeline” the use of Indomethacin which is a non-steroidal anti-inflammatory drug (NSAID) has been analyzed for repurposing against COVID19. The multi-modal system contains a total of 13 modules built using different drug repurposing approaches like target-based approaches, docking-based systems, symptom-based approaches, and pathways/circuit-based approaches. Cov-DrugX pipeline provides important parameters which indicate the suitability of Indomethacin as potential candidate drug for COVID 19 treatment.