[Product Releases]

• eHiTS v9.1
• Score v9.1
• LASSO v9.1
• CheVi
• SPROUT
• ARChem
• CAESA
• CLiDE v5

[Blog]

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[News]

Can we trust docking results?
Sept 2010

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

EPA's ToxCast^TM project will use SimBioSys' eHiTS as docking engine
Nov, 2007

[Events]

243rd ACS
Mar 25-29, 2012
San Diego, CA
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Utilizing training techniques in eHiTS improve score-RMSD and score-IC50 correlations in in silico high throughput screening.

Danni L. Harris, Orr Ravitz, Antony Williams, Zsolt Zsoldos

SimBioSys, Inc., 135 Queen's Plate Dr., Unit 520, Toronto, ON, M9W 6V1, Canada

Abstract:

Virtual screening of compounds has become a prevalent step in a variety of biologically related applications including drug discovery, predictive metabolism and toxicity prediction. The docking paradigm consists of two interrelated steps, namely pose prediction, and scoring. While most docking approaches are capable of predicting poses consistent with known structural solutions, they do not generally score these as the top ranking poses. Furthermore, correlations between docking scores and low RMSD values or bioactivity given in terms of lnKd or IC50 values are often limited (J. Med. Chem, 49:5912-5931). Such correlations are crucial if docking is to play a reliable role in either prioritizing prospective ligands for synthesis or in ranking protein-target interactions in metabolic and toxicity studies. We describe in detail the mixed physical and informatics approach of the scoring function of eHiTS (Electronic High Throughput Screening), and demonstrate by comparing to quantum mechanical results how it is well equipped to capture subtle interactions such as Pi-cation, non-conventional hydrogen bonding, and Pi-stacking. Good score-based low RMSD discrimination of biochemically and pharmacologically relevant poses as well as score-IC50 correlations are shown with illustrations from several systems: nicotinic acetylcholine receptors and their surrogate binding proteins (AChBP), kinases, and cytochrome P450s. We demonstrate how these features may be further enhanced using eHiTS' unique training utility, and discuss this as a method to improve discrimination of ligand-target recognition.

See full poster presentation

For more information on this topic see blog posting, or about the product see web pages: http://www.simbiosys.com/ehits/