Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents.

A novel data mining procedure to look for new antitubercular agents and targets as well as to find a minimum common bioactive substructure (MCBS), has been reported here. The methodology extracts MCBS, both across the diverse chemical classes and within the particular chemical class, known to be present in the various marketed drugs alongside antimycobacterial compounds with known MICs. For this purpose a small in-house database of compounds has been created, for which MICs against Mycobacterium are known. The compounds have been collected from literature available on the synthetic compounds, having known MICs against Mycobacterium tuberculosis. An elaborate HQSAR (Hologram QSAR) study has been attempted to extract active fragment from a diverse class of compounds, in combination with the clustering technique to select a homogeneous group of compounds having good a profile toward the activity. The 2D pharmacophore (the 2D fragments extracted from HQSAR) has been validated searching the database. It has been found further that this validated 2D pharmacophore could be used for searching the orphan target in Mycobacterium effectively.