FReDD: Supporting Mining Strategies through a Flexible-Receptor Docking Database

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Abstract

Among different alternatives to consider the receptor flexibility in molecular docking experiments we opt to execute a series of docking using receptor snapshots generated by molecular dynamics simulations. Our target is the InhA enzyme from Mycobacterium tuberculosis bound to NADH, TCL, PIF and ETH ligands. After testing some mining strategies on these data, we conclude that, to obtain better outcomes, the development of an organized repository is especially useful. Thus, we built a comprehensive and robust database called FReDD to store the InhA-ligand docking results. Using this database we concentrate efforts on data mining to explore the docking results in order to accelerate the identification of promising ligands against the InhA target.