Computational methods for multi-target drug designing against mycobacterium tuberculosis

Abstract

Despite the availability of several drugs, Mycobacterium tuberculosis is still a big concern for public health. Such situation exists because of continuous emergence of TB-resistant strains. Possible reasons of developing resistance include long therapy and combination therapy. Therefore new potential leads are needed to be identified, and at the same time, the number of drugs in the combination therapy should also be reduced that will make administration of drug doses easier. In the present scenario, developing drug having the ability to interact with multiple targets, simultaneously, is a promising approach to treat the complicated diseases. These multi-target drug therapies have advantage of improved safety profile and high drug efficacy with easier administration over the single-target drug therapies. Many of in silico methods have been applied to reach different polypharmacologically directed drug designing, mainly for multi-target drug designing. In this chapter, we have discussed about the available strategies for computational multi-target drug designing with their advantages and disadvantages. We have also discussed an easy, fast, and equally accurate method for multi-target drug designing against the Mycobacterium tuberculosis.