Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.

ABSTRACT:

Background

The emergence of Multi-drug resistant tuberculosis in pandemic proportions throughout the world and the paucity of novel therapeutics for tuberculosis have re-iterated the need to accelerate the discovery of novel molecules with anti-tubercular activity. Though high-throughput screens for anti-tubercular activity are available, they are expensive, tedious and time-consuming to be performed on large scales. Thus, there remains an unmet need to prioritize the molecules that are taken up for biological screens to save on cost and time. Computational methods including Machine Learning have been widely employed to build classifiers for high-throughput virtual screens to prioritize molecules for further analysis. The availability of datasets based on high-throughput biological

SUBMITTER: Periwal V 

PROVIDER: S-EPMC3342097 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature