How to design potential hit molecules using the structure of the target and the Protein Data Bank ?

The FBDD application gives results which are similar to docking as it returns poses in the binding site for molecules from a 2D molecular file. It has advantages as a reasonable number of molecules are usually found in the order of a few hundreds. The poses are relying on structural data and not on scoring functions. You don't have to worry about which scoring function to use and how well it performs. With MED-SuMo, it is as easy as using available structural information on ligand binding from the PDB.

The method doesn't rely on scoring functions, it is exploiting exhaustively the structural similarities between protein structures. The assumption is that two very similar protein environment are likely to bind the same fragment with the same relative pose.

The following videos will help you to see how using MED-SuMo and MED-Ligand to design relevant Hit candidate for a given target, here it is the protein kinase EGFR. Results and Interfaces are available for a free trial in the download section.

The computational Fragment-Based Drug Design is introduced in the next video which is based on a talk given at the EuroQSAR.

More online videos are available in the learning center.

The fragments originating from the PDB ligands (hundreds of thousands in the PDB) are selected according to their likelyhood of binding to the query target:

More online videos are available in the learning center.

They are combined in 3D to design hit like molecules as explained in the next video:

More online videos are available in the learning center.

Those Hit like molecules are searched into chemical libraries, so you end up with compounds likely to be experimentally tested. A validated scoring function (PLP) is used to rank/prioritize the hit like molecules:

More online videos are available in the learning center.

This work is based on two publications:

Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site. Oguievetskaia K, Martin-Chanas L, Vorotyntsev A, Doppelt-Azeroual O, Brotel X, Adcock SA, de Brevern AG, Delfaud F, Moriaud F. J Comput Aided Mol Des. 2009 Jun 17. 
PMID: 19533373

Computational fragment-based approach at PDB scale by protein local similarity. Moriaud F, Doppelt-Azeroual O, Martin L, Oguievetskaia K, Koch K, Vorotyntsev A, Adcock SA, Delfaud F. J Chem Inf Model. 2009 Feb;49(2):280-94. 
PMID: 19434830