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Toolkit Development Platform

OEDocking TK

OEDocking TK

Programming Libraries for Creating Custom Applications, Scripts, and Web Services

There are a wide variety of docking programs available to the community at large; however, until now there have been no resources in existence for individual methods developers to provide a common and well-supported foundation for the development of new docking and scoring applications.

The OEDocking TK from OpenEye is a programming library that provides this core docking and scoring functionality. Coupled with the highly acclaimed cheminformatics functionality in OEChem, the OEDocking TK is a comprehensive solution for anyone seeking to develop new docking tools. The toolkit supports docking, scoring, and optimization with the Chemscore, Chemgauss3, PLP, and Shapegauss scoring functions. The ligand-aware Hybrid Docking functionality unique to OpenEye's FRED docking program as well as the POSIT shape fitting algorithm for pose prediction are also provided in the toolkit. C++, C#, Python, and Java are supported.

Features

Docking

  • Exhaustive search followed by pose optimization
  • Hybrid docking (uses the structure of a known bound active to guide docking)
  • Docking constraints

Scoring

  • Score optimization (systematic solid body optimization)
  • Breakdown of score by atom and/or scoring function component
  • Score annotation (scores are stored on molecule for visualization in VIDA

Scoring Functions

    • Chemgauss4
    • Chemgauss3
    • Chemscore
    • PLP
    • Shapegauss
For more detailed information on OEDocking TK, check out the links below:


 Documentation   >   Evaluate
Image Map

The OpenEye Toolkit Ecosystem

Cheminformatics

The Cheminformatics suite of toolkits provides the core foundation upon which all of the OpenEye applications and remaining toolkits are built. The Cheminformatics suite is a collection of seven individual yet interdependent toolkits that are described in the table below.

  Toolkit Major Functionality
  FastROCS TK Real-time shape similarity for virtual screening, lead hopping & shape clustering
  OEChem TK Core chemistry handling and representation as well as molecule file I/O
  OEDepict TK 2D Molecule rendering and depiction
  Grapheme™ TK Advanced molecule rendering and report generation
  GraphSim TK 2D molecular similarity (e.g. fingerprints) 
  Lexichem TK  name-to-structure, structure-to-name, foreign language translation 
  MolProp TK Molecular property calculation and filtering 
  Quacpac TK Tautomer enumeration and charge assignment
  MedChem TK Matched molecular pair analysis, fragmentation utilities, and molecular complexity metrics

Modeling

The Modeling suite of toolkits provides the core functionality underlying OpenEye's defining principle that shape & electrostatics are the two fundamental descriptors determining intermolecular interactions. Many of the toolkits in the Modeling suite are directly associated with specific OpenEye applications and can therefore be used to create new or extend existing functionality associated with those applications.

  Toolkit Major Functionality
  OEChem TK Core chemistry handling and representation as well as molecule file I/O
  OEDocking TK Molecular docking and scoring
  Omega TK Conformer generation
  Shape TK 3D shape description, optimization, and overlap
  SiteHopper TK Rapid Comparison of Protein Binding Sites
  Spicoli TK Surface generation, manipulation, and interrogation
  Spruce TK Protein preparation and modeling
  Szybki TK General purpose optimization with MMFF94
  Szmap TK Understanding water interactions in a binding site
  Zap TK Calculate Poisson-Boltzmann electrostatic potentials

References

  1. Gaussian Docking Functions Mark McGann, Harold R Almond, Anthony Nicholls, J. Andrew Grant and Frank K. Brown, BioPolymers, 2003, 68, 76-90.
  2. Deciphering common failures in molecular docking of ligand-protein complexes Gennady M. Verkivker, Djamal Bouzida, Daniel K. Gehlaar, Paul A. Rejto, Sandra Arthurs, Anthony B. Colson, Stephan T. Freer, Veda Larson, Brock A. Luty, Tami Marrone and Peter W. Rose, J. Comput. Aided Mol. Des., 2000, 14, 731-751.
  3. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes Matthew D. Eldridge, Christopher W. Murray, Timothy R. Auton, Gaia V. Paolini and Roger P. Mee., J. Comput. Aided Mol. Des., 1997, 11, 425-445.
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