Skip to Content


OEDockingOEDocking is a suite of well-validated molecular docking tools and their associated workflows. Each tool is specifically designed to address its own unique application to the docking problem.

OEDocking 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.

Pose Prediction


POSIT - Ligand guided pose prediction

POSIT is designed to use bound ligand information to improve pose prediction. Using a combination of OpenEye approaches, including structure generation, shape alignment and flexible fitting, it produces a predicted pose whose accuracy depends on similarity measures to known ligand poses. As such, it produces a reliability estimate for each predicted pose - an industry first.

The estimate is computed using both the 2D and the 3D similarity of the ligand being fit to the known bound ligand. In addition, if provided with a selection of receptors from a crystallographic series, POSIT will automatically determine which receptor is best suited for pose prediction.

Virtual Screening

Docking Results

FRED - Fast exhaustive docking

FRED is a fast and effective docking application whose performance is significantly more reliable, i.e. lower variance, than most other programs [1,2].

FRED performs a systematic, exhaustive, nonstochastic examination of all possible poses within the protein active site, filters for shape complementarity [3] and pharmacophoric features before selecting and optimizing poses using the Chemgauss4 scoring function.

HYBRID - Ligand guided docking

HYBRID is a docking program that can utilize bound ligand information in a seamless manner. Like FRED, HYBRID performs a systematic, exhaustive, nonstochastic examination of all possible poses within the protein active site; however, instead of filtering the poses based on their shape complementarity to the active site, they are filtered on their shape and chemical complementarity to a known bound ligand. This ligand-guided docking provides statistically improved enrichment compared to many docking tools [2].


  1. "FRED Pose Prediction and Virtual Screening Accuracy", M. McGann, J. Chem. Inf. Model., 2011, 51 (3), 578-596
  2. "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
  3. "Gaussian docking functions", M.R. McGann, H.R. Almond, A. Nicholls, J.A. Grant and F.K. Brown, Biopolymers, 2003, 68 (1), 76-90