Comparison of Automated Docking Programs as Virtual Screening Tools

Abstract

The performance of several commercially available docking programs is compared in the context of virtual screening. Five different protein targets are used, each with several known ligands. The simulated screening deck comprised 1000 molecules from a cleansed version of the MDL drug data report and 49 known ligands. For many of the known ligands, crystal structures of the relevant protein-ligand complexes were available. We attempted to run experiments with each docking method that were as similar as possible. For a given docking method, hit rates were improved versus what would be expected for random selection for most protein targets. However, the ability to prioritize known ligands on the basis of docking poses that resemble known crystal structures is both method- and target-dependent.

Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search

“…Cummings et al [24] reported comparative performance of DockVision, Dock, GOLD, and Glide with respect to their ability to identify known ligands of HIV Protease (HVR), the human homolog of the mouse double minute 2 oncoprotein (HDM2), protein tyrosine phosphatase 1b (PTP1b), THR, and urokinase plasminogen activator (uPA). Of these, the ligands used for the first four were available from the authors.…”

“…A recent and valuable trend within the field has been the use of standard benchmarks on multiple methods [16][17][18][19][20][21][22]. For the work reported here, benchmarks were selected either for which previous versions of Surflex-Dock had been tested [16,17,19] in order to show the effects of new features, or where protein and ligand structures were publicly available along with performance of widely used methods [23,24].…”

“…3. Screening utility, 4 proteins: Cummings et al reported on the performance of four docking systems against four targets of pharmaceutical interest: HIV protease, thrombin (THR), protein-tyrosine-phosphatase 1b, and HDM2 (also called MDM2, a protein that binds to and inactivates the tumor suppressor p53) [24]. This set of four proteins, known actives, and a decoy set derived from the MDL Drug Data Report (MDDR) will be called the J&J set.…”

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

“…A recent case study by Muthas et al indicated that post-filtering with pharmacophores was shown to increase enrichment rates in their investigated targets compared with docking alone [6] . Several studies have been performed to assess various DBVS methods and compare which docking programs are the most successful in identifying active hits [7][8][9][10] . The conclusion is that no docking program may outperform other docking programs for all the tested targets, and the performance of each tested docking program is highly dependent on the nature of the target binding site [5] .…”

Virtual Screening and Its Integration with Modern Drug Design Technologies

“…There are, fundamentally, two approaches to VS studies: i. structure-based virtual screening (SBVS), which requires knowledge of the 3D structures of target proteins to prioritize compounds by their complementarity to the binding site; and, ii. ligandbased virtual screening (LBVS), where no information on the protein is needed, instead, compounds known to bind to the protein are used as queries to search databases for new molecules possessing biological activity [21][22][23][24][25]. Fig.…”