Target based virtual screening has surpassed ligand based virtual screening methods in the recent past mainly
as it provides more clues regarding intermolecular interactions and takes into consideration the flexible receptor as well.
The current methodology describes a computational strategy of predicting Mycobacterium tuberculosis (M. tuberculosis)
binders for five well studied targets representing M. tuberculosis proteome encompassing most of the known mechanisms
of action. The diversity of the targets was affirmed by their active site analysis and structural studies. The current
approach employed pharmacophore searching, docking and clustering techniques in tandem and was validated by
enrichment studies using the available Schrödinger data set consisting of 1000 decoys. The application of this
methodology was demonstrated by predicting potential molecular targets for fifty newly synthesized compounds. Cross
docking studies on the targets were carried out with 4512 known inhibitors utilizing a high performance computing
platform to reveal underlying affinity and promiscuity patterns. Optimum binding energy range for all targets as
determined by high throughput docking was found to be -3 to -13 kcal/mol.
Keywords: Binding energy, docking, Mycobacterium tuberculosis, open source drug discovery (OSDD), pharmacophore,
structure based drug design (SBDD).
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