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[Blog]
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[News]
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IBM's Systems & Technology Group releases a white paper with
eHiTS & Cell
Oct 2008 Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database
Sept 2010
EPA's ToxCastTM project will use SimBioSys' eHiTS as docking engine
Nov, 2007
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[Events]
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| 240th ACS
Aug 22-26, 2010
Boston, MA, USA
booth #945
see >> more
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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
Abstract:
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.
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