Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets.

ABSTRACT:

Background

Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whol

SUBMITTER: Periwal V 

PROVIDER: S-EPMC3228709 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature