Introducing StarDrop 6.6

pKa Model

StarDrop’s new pKa model, included in the ADME QSAR module, combines quantum mechanics and machine learning to accurately predict acid dissociation constants for your compounds.

This model accurately predicts pKa values for mono- and di-protic compounds. Rigorously validated, our model performs excellently on external benchmarks, such as the SAMPL6 test set that was specifically created to benchmark pKa prediction methods.

To learn more about the new model, there is a short video that guides you through the new functionality.

StarDrop - pKa model


StarDrop 6.6 now includes an extended suite of SeeSAR modules that enhance StarDrop’s capabilities in structure-based design. These include;


Visualise your ligands in their protein environment to identify the key interactions driving binding affinity in 3D. These structures can be derived from X-ray crystallography or predicted with any docking software.


Using the award-winning HYDE scoring method, analyse your ligand’s binding affinity, related to free energies with visual atomic contributions, and view torsion angle heat maps to assess docked poses.


Generate compound poses for virtual screening and interactive 3D design using FlexX docking. Fast template docking enables you to edit compounds with real-time feedback, while maintaining an established binding mode. Combined with the SeeSAR Affinity module, you can assess the impact of new optimisations on binding energetics.

StarDrop - SeeSAR modules

The SeeSAR View and Affinity modules can be applied to 3D poses derived from crystal structures or any docking software.

For an overview of the new modules watch quick introductory videos here.

If you don’t yet have access to StarDrop’s SeeSAR modules and would like to try them on your compounds, please contact us at and we’ll be happy to provide a free trial license.

Further Enhancements

There have been many more additions in StarDrop 6.6:

  • A new column finder tool to help you search, particularly when working with large data sets
  • The export to PowerPoint® tool has been enhanced and we have also added support for Macs and Keynote®
  • Copy and paste support is now offered for mol format for ChemDraw
  • And many more...
StarDrop - PowerPoint slide

If you have any questions or would like any additional information about StarDrop 6.6, please contact us .

Optibrium Community

StarDrop’s capabilities can be extended with a range of free add-ons that we are continually extending and updating. You can download these by logging in to the Optibrium Community website. Please note that with the release of StarDrop 6.6 you may need to update your add-ons for them to continue to work.

You may be interested in some of the most recently added extensions:

REAL Space Search Tool

This tool enables you to search the Enamine REAL space for compounds that are similar to a query structure of interest. The Enamine REAL space currently contains over 11 billion virtual compounds. This tool is analogous to BioSolveIT’s REAL Space Navigator and uses their FTrees technology to quickly search the enormous REAL space.

  • Investigate the chemical space local to your compound
  • Find structurally novel leads with improved properties or avoid patent-protected space


This script enables you to access ICSYNTH light to evaluate possible synthetic pathways for new molecules designed in StarDrop’s interactive designer or Nova module. ICSYNTH light uses a transform-based approach to generate synthesis pathways for target molecules, even if these are novel and/or unpublished. This script will display the possible synthetic pathways in a web browser.

ChEMBL Search Tool

ChEMBL is probably the foremost manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. This recently updated script enables you to search ChEMBL directly from StarDrop, returning the data pre-formatted and ready to use for tasks such as:

  • Identifying potential off-target activities for your compounds
  • Understanding SAR around your compounds or targets
  • Building QSAR models to make predictions for new compounds

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