Most of the functionality available in OEDocking is also available in toolkit form via the OEDocking TK.
POSIT - Ligand guided pose prediction
POSIT uses the information from bound ligands to improve pose prediction. Using a combination of OpenEye approaches, including structure generation, shape alignment and flexible fitting, a ligand of interest is compared to bound ligands and its similarity to such both guides the nature of the applied algorithm and produces an accuracy estimate. Both 2D and 3D similarity measures are used in this reliability index, which is an industry first.
In addition, when provided with a selection of ligand-receptor complexes from a crystallographic series, POSIT will automatically determine which is best suited to guide the new ligand docking. The performance of this "best guess" structure is very close to that found by using each structure in turn and retrospectively choosing the best result! However, since only one structure is actually utilized, there is a considerable saving in computational effort for a significant gain in performance.
FRED - Fast exhaustive docking
Within a given, but practical, resolution FRED performs a systematic and non-stochastic examination of all possible protein-ligand poses, filters for shape complementarity and chemical feature alignment before selecting and optimizing poses using the Chemgauss4 scoring function [1,2,3,4]. It comes with a powerful GUI for preparing the active site and adding custom restraints. It also provides a detailed scoring analysis that uses our Grapheme toolkit (see right). In a recent publication, Brus et al used FRED to discover a validated 2.7nM inhibitor of BChE, an Alzheimer's target . The authors describe FRED as "by far the fastest docking tool and thus particularly suitable for ultrahigh-throughput docking (>1 million compounds)".
HYBRID - Ligand guided docking
HYBRID uses bound ligand information to improve virtual screening performance, e.g. as POSIT improves poses HYBRID improves enrichment. Like FRED, HYBRID performs a systematic, exhaustive, non-stochastic examination of poses within the protein active site; however, HYBRID reduces this search space based on shape and chemical complementarity to known bound ligands. This ligand-guided docking provides equivalent or better enrichment compared to most docking procedures [1,2].
- "FRED Pose Prediction and Virtual Screening Accuracy", M. McGann, J. Chem. Inf. Model., 2011, 51 (3), 578-596
- "FRED and HYBRID Docking Performance on Standardized Datasets", M. McGann J. Comp.-Aid. Mol. Design, 2012, 26 (8), 897-906
- "Comparison of Topological, Shape, and Docking Methods in Virtual Screening", G.B. McGaughey, R.P. Sheridan, C.I. Bayly, J.C. Culberson, C. Kreatsolas, S. Lindsley, V. Maiorov, J.-F. Truchon and W.D. Cornell, J. Chem. Inf. Model., 2007, 47 (4), 1504-1519
- "Gaussian docking functions", M.R. McGann, H.R. Almond, A. Nicholls, J.A. Grant and F.K. Brown, Biopolymers, 2003, 68 (1), 76-90
- Discovery, Biological Evaluation, and Crystal Structure of a Novel Nanomolar Selective Butyrylcholinesterase Inhibitor, B. Brus, U. Kosak, S, Turk, A. Pislar, N. Coquelle, J. Kos, J. Stojan, J Colletier, S. Gobec, J. Med. Chem, 2014