[Product Releases]

Most recent post


Can we trust docking results?
Sept 2010

IBM Systems and Technology Group releases a white paper with eHiTS and Cell
Oct 2008

EPA's ToxCastTM project will use SimBioSys' eHiTS as docking engine
Nov, 2007


243rd ACS
Mar 25-29, 2012
San Diego, CA
see >> more


Ligand Activity in Surface Similarity Order

Spot all the actives quickly and easily with eHiTS LASSO !

The activity surface point types (shown as triangles) of a Faxtor Xa inhibitor  ZK-807834 (CI-1031).

What is LASSO? A short overview

Download eHiTS LASSO datasheet with the latest results.

A new 3D ligand activity surfaced-based similarity tool from SImBioSys gives users the power to quickly screen large datasets for structurally diverse active molecules. This proven scaffold-hopping capability is possible because eHiTS LASSO uses chemical features, not just 2D or 3D structural similarity, of active ligands to rapidly identify molecules with potentially similar activity.

  • eHiTS LASSO provides a very easy-to-use training utility that will capture the chemical features of your active molecules and uses this information to look for molecule with similar features. In addition eHiTS LASSO ships with a large set of pre-trained knowledge files that can have you screening databases within minutes of installing the software.

  • Trails have shown that eHiTS LASSO is able to retrieve high percentages of actives in a seeded dataset when trained on a very small number of actives. When tested on very diverse ligands, eHiTS LASSO has shown a very strong ability to retrieve structurally diverse actives, showing its ability to scaffold hop and identify actives of varying chemotypes. 

  • The eHiTS LASSO has been seamlessly integrated with the eHiTS docking program, giving you the ability to screen very large databases quickly while still generating docking poses of the most promising molecules.

Evaluating eHiTS LASSO Performance

Following the ligand diversity classification of Hert et. al J. Chem. Inf Model, 46, 462-470, 2006, three groups of ligands with low, medium and high Mean Pairwise Similarity (MPS) were extracted from the MDDR database. For each of these 3 groups 5 families, i.e. 15 sets in total were chosen. Please see detailed results in a table here or a quick view on the chart.

To test eHiTS LASSO, 2% of the actives from each dataset were used to train the neural net used

by the LASSO.  The eHiTS LASSO was then run on a cleaned version of MDDR database and the ability of the LASSO utility to rank actives was captured. The enrichment chart at the top of the page is showing the % of actives recovered in the top 2%, 5% and 10% of the screened and ranked database. It can be seen that the eHiTS LASSO, trained on only 2% of the actives is capable of retrieving over 50% of the actives in the top 10% of the screened database in 11 out of the 15 cases tested. Even for the most structurally diverse data set eHiTS LASSO was able to place at least 20% of the actives in the top 2% of the screened results.

These results show that the eHiTS LASSO is capable of identifying actives from large datasets using a very small number of actives for training. Training with just 2% of a know set of actives gives very strong enrichment results over a very diverse range of activity classes. It is clear, however, that if the actives have low diversity (i.e. are structurally very similar) then this ligand based similarity search is far more effective than if the actives are highly diverse. In the test shown, eHiTS LASSO retrieved over 90% of the actives in the top 10% of the screened database for all 5 test sets. However it is clear that there are situations where the eHiTS LASSO does not perform as well. This is likely due to the small number of actives used to train the LASSO and the large diversity of actives being tested. Therefore it is important to use as many actives as you have available to train the eHiTS LASSO for optimal results in real life screening situations.

For more detailed supporting data and information on data preparation, please click here.

[LASSO Links]

Copyright © 2011 SimBioSys Inc., All rights reserved.