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OEDocking is a suite of well-validated molecular docking tools and workflows, each specifically designed to address its own unique aspect of protein-ligand interaction. Specifically, it features POSIT for informed pose prediction as well as FRED and HYBRID as complementary tools for virtual screening.

Most of the functionality available in OEDocking is also available in toolkit form via the OEDocking TK.

We identified the ureido methylpiperidine carboxylate derivative [using OEDocking], compound 7, as a reversible, selective, and potent inhibitor of cathepsin V. – Mitrovic et al. (CSBJ, 2022)
2IKO receptor.


  • FRED - super fast exhaustive docking (virtual screening)
  • HYBRID - very fast ligand guided docking (virtual screening)
  • POSIT - fast knowledge guided pose prediction (optimization)
  • Can utilize multiple crystallographic protein structures
  • Can use the crystallographic structure of a ligand to guide docking
  • 5-100 times faster than competing software

Pose Prediction

POSIT - Knowledge 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 can 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! When generating only a single pose, POSIT thus only utilizes one structure providing considerable saving in computational effort for a significant gain in performance. For generating multiple poses, POSIT provides the flexibility to work with just the "best guess" structure or all of the structures for even better quality pose generation.

POSIT results graph.
Gloved hands using pipette and tube, scientific illustrations in the foreground.
Dock report.

Virtual Screening

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. In a recent publication, Brus et al used FRED to discover a validated 2.7nM inhibitor of BChE, an Alzheimer's target [5]. The authors describe FRED as "by far the fastest docking tool and thus particularly suitable for ultrahigh-throughput docking (>1 million compounds)".


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].

For more detailed information on OEDocking, check out the link below:



  1. "FRED Pose Prediction and Virtual Screening Accuracy", M. McGann, J. Chem. Inf. Model., 2011, 51 (3), 578-596
  2. "FRED and HYBRID Docking Performance on Standardized Datasets", M. McGann J. Comp.-Aid. Mol. Design, 2012, 26 (8), 897-906
  3. "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
  4. "Gaussian docking functions", M.R. McGann, H.R. Almond, A. Nicholls, J.A. Grant and F.K. Brown, Biopolymers, 2003, 68 (1), 76-90
  5. 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

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