BackgroundThe coronavirus (CoV) disease identified in Wuhan, China in 2019 (COVID-19) was chiefly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and caused by SARS CoV-2 that belongs to the family Coronaviridae. COVID-19 symptoms vary from a mild cold to more severe illnesses such as SARS, thrombosis, stroke, organ failure, and in some patients even cause mortality. Deciphering the underlying disease mechanisms is pivotal for the identification and development of COVID-19 specific drugs for effective treatment and prevent human-to-human transmission, disease complications, and mortality. Methodology: Here, the Next Generation RNA Sequencing (RNA Seq) data using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells, were obtained from the gene expression omnibus (GEO) (GSE147507) and the Quality Control (QC) were evaluated using the CLC Genomics Workbench 20.0 (Qiagen, USA) before the RNA Seq analysis. The DEGs were imported into BioJupies to analyze to decipher COVID-19 induced biological, molecular, and cellular processes, pathways, and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID-19-specific gene signatures. Besides, we have used the iPathwayGuide (Advaita Bioinformatics USA) to identify COVID-19 specific pathways, biological, molecular, and cellular processes, and “druggable” candidates for future therapy. Results: 141 DEGs were identified out of a total of 9665 DEGs obtained from BioJupies analysis of the RNASeq reads of the SARS CoV infected A549 cells and mock-treated A549 cells based on a p-value cut off (0.05) and a fold change cut off 1.5.Conclusion: In conclusion, the present study unravels a novel approach of using next-generation knowledge discovery platforms to discover specific drugs for the amelioration of COVID-19 related disease pathologies.