Combinatorial Designing of Novel Lead Molecules Towards the Putative Drug Targets of Extreme Drug-Resistant Mycobacterium tuberculosis: A Future Insight for Molecular Medicine

Publication: Essentials of Bioinformatics
Software: ADMET Predictor®


Mycobacterium tuberculosis (Mtb) is one of the notorious pathogens which has led to high mortality rates and demonstrated extreme drug resistance (XDR) to most of the conventional drugs and become a potential threat to public health worldwide. Hence, there is high demand and need to screen novel drug targets and alternate lead molecules that can be used as starting point of developing potential therapies against this pathogen. The proposed chapter illustrates the application of computer-aided virtual screening for screening novel and probable drug targets of Mycobacterium tuberculosis and identification of novel lead molecules as therapeutic remedies by computational biology tools and approaches. The chapter initially focuses on the recent perspectives on XDR-Mtb, major metabolic pathways responsible for the pathogenesis, conventional therapies and associated drug resistance and challenges and scope of computational drug screening. This chapter further illustrates potential drug targets, various approaches for the prediction of these targets, molecular modelling works, screening of novel lead molecules by computational virtual screening with ideal drug likeliness and ADMET (absorption, distribution, metabolism, excretion and toxicity) features, application of docking studies and simulation. Thus, the present chapter provides latest developments in molecular medicine and computational drug discovery to combat tuberculosis (TB) and thereby open new paradigm for the development of novel leads against potential drug targets for XDR-Mtb.