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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
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Presentation at the:  Spring 2008 ACS National Meeting & Exposition

to be held in New Orleans, LA, Apr 6-10, 2008

Presentation in COMP 57, SESSION: Drug Discovery,
Monday, April 7, 2008 from 9:15 AM to 9:40 AM

eHiTS Lightning Redefines the State-of-the-art for Structure-Based Virtual Screening

Zsolt Zsoldos (1), A. Peter Johnson (2)
(1) SimBioSys Inc., 135 Queen's Plate Dr, Unit 520, Toronto, ON M9W 6V1, Canada
(2) School of Chemistry, University of Leeds, Leeds, LS2 9JT, United Kingdom


The primary goal of most virtual screening experiments is to find new lead compounds as a starting point in the drug discovery pipeline. There are two typical approaches that are sometimes combined in a screening workflow funnel: ligand-based screening (2D similarity, 3D pharmacophore, fingerprint, surface or other QSAR descriptors) and structure-based flexible ligand docking and scoring. The latter is often considered too slow for large scale screening, especially when considering databases of millions of structures, while the former does not provide 3D coordinates or estimated binding energies. The combination of flexible docking and ligand based filtering in a single software platform has proven to be among the most accurate pose prediction tools and provides one of the highest enrichment factors based on comparative evaluation studies. The software platform is called eHITS and utilizes a surface-based descriptor know as LASSO, Ligand Activity in Surface Similarity Order. Accurate binding energy estimation and activity rank ordering has historically been a very challenging task for all tools and methods in the past. Detailed scientific analysis has been carried out on a wide variety of protein targets to find the root of the elusive scoring problem. The results of this study will be presented with compelling evidence pointing towards insufficient pose prediction accuracy.

Full Presentation

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