Indeed, M. abscessus is uniformly resistant to first-line antituberculosis drugs, macrolide-based for searching promising drug targets has been increasing significantly in the field of drug research. The availability of complete genome sequences of several disease causing organisms and human in public databases is greatly facilitating the search of novel targets. Current computational target discovery approaches include identification of pathogen-specific essential genes, host-pathogen interaction factors, proteins involved in persistence, chokepoint enzymes, resistance genes/resistance-associated proteins; characterization of pathogen-specific metabolic pathways; prediction of gene expression levels etc.. The approaches have been efficiently utilized to identify novel drug target candidates in several lifethreatening pathogens, including Mycobacterium tuberculosis, Mycobacterium leprae, Mycobacterium ulcerans, Helicobacter pylori, Streptococcus pneumoniae, Yersinia pestis, and Pseudomonas aeruginosa. Most of these target discovery methods consider selectivity/specificity and essentiality as the principal selection criteria for prioritizing therapeutic candidates. An ideal drug target must be specific to the pathogen for avoiding unwanted host-drug interactions and should be a crucial protein for growth and survival of the pathogen as inhibition of such proteins would lead to the death of the pathogen. The present manuscript introduces a novel hierarchical in silico approach, which integrates various computational methods, with the objective of identification and qualitative characterization of therapeutic candidates in M. abscessus. The current approach enables us to identify 40 promising drug target candidates, based on the criteria of essentiality and selectivity. The characterization of the candidate targets predicts their location in bacterial cell, capability to act as a broad spectrum target, functional association with metabolically interacting proteins, cellular function, and druggable property. The current target identification and characterization approach comprises three phases of analyses. Phase I involves the mining of protein datasets GDC-0879 through chokepoint, plasmid protein, pathway, virulence factor, and resistance genes and protein network analysis. Phase II passes the mined protein datasets through the subtractive channel of analysis. This comprises four sequence level analyses for filtering proteins that 
are nonhomologous to human, essential for the survival of the pathogen, non-homologous to anti-targets, and non-homologous to gut microflora. st of the bacteria must undergo de novo biosynthesis of folate. Proteins involved in folate biosynthesis are efficient drug targets as its derivatives are involved in nucleotide and amino acid biosynthesis. Using two component signal Doxorubicin 25316-40-9 transduction system the organism senses the changes in the environment, interprets the signals, and modifies the expression of some genes as response, which facilitates its survival. Moreover, this system is also found to be associated with the pathogenicity of the organisms and its absence in human makes them attractive drug targets. Hence, inhibition of proteins involved in two component system could reduce virulence of the organism and hinder its adaptability to the changing environmental conditions.