ICM-VLS

NOTE: This product is an add-on to ICM-Pro. This product also includes all the chemistry functionalities included in ICM-Chemist-Pro.

Structure-Based Virtual Ligand Screening

ICM Virtual Ligand Screening (VLS) is a combination of the genuine internal coordinate docking methodology with a sophisticated global optimization scheme. Accurate and fast potentials and empirically adjusted scoring functions have led to an efficient virtual screening methodology in which ligands are fully and continuously flexible. The average yield of lead candidates is as high as 10% and in many cases only the top 1% of hits need to be tested experimentally to find a lead.

Features

Fast and accurate docking and scoring procedure.
Screen large databases from chemical vendors or directly from MolCart
Automatic flexibility of the ligand and methods for incorporating receptor induced fit.
Option to use the Graphical User Interface to set up all stages of the screening or run from command line in batch.
Index large .sdf files and get scripting access to large compound files
Automatically convert your 2D database compounds to 3D, add hydrogens and assign charges
From Smiles to 3D and from 3D to Smiles conversions
Template docking for pharmacophore based drug design
Dock on-the-fly Markush generated libraries and focused libraries
Apply additional selection criteria such as size, number of h-bond donors/acceptors/torsions
Scan substituted derivatives of your leads
Easy to use graphical user interface for post-docking analysis
One-click hitlist generation, quickly browse through ligand binding poses
Browse scan results interactively with diverse display features such as hbond and mesh contact views
Analyze scan performance with score and property histograms

Ligand-Based Virtual Ligand Screening

Ligand-based virtual screening can be performed using the Atomic Property Field (APF) method developed by MolSoft ( Totrov 2008). APF is a 3D pharmacophoric potential implemented on a continuously distributed grid which can be used for ligand docking and scoring. APF can be generated from one or more high affinity scaffolds and seven properties are assigned from empiric physico-chemical components. These properties include: hydrogen bond donors, acceptors, Sp2 hybridization, lipophilicity, size, electropositive/negative and charge (Figure 1). A single ligand atom can contribute to multiple fields; multiple similar ligand atoms in a spatially consistent location result in a strong pharmacophore signal for their features in this location. APF has also been extended to multiple flexible ligand alignments using an iterative procedure. APF uses Monte Carlo minimization in the atomic property fields potentials in conjunction with standard force-field energies.

Selected ICM-VLS Success Stories

Three inhibitors discovered by the ICM virtual screening procedure. Activity was confirmed experimentally. The figure shows the active site of the protein in skin (molecular surface) representation, colored by binding properties: hydrophobic areas are in green, hydrogen bond donor areas are in blue and hydrogen bond acceptor areas are in red.

Drug Target	Notes	Reference
Pancreatic endoplasmic reticulum kinase	Specific inhibitors identified by ICM-VLS.	Wang et al 2010
P300 HAT	Screened 500K compounds – selected 194 for experimental testing resulting in 3 inhibitors which had specificity.	Bowers, E.M. et al. (2010)
GPCR – Adenosine A2A	Out of 56 compounds sent for experimental testing in functional assays. 23 compounds were identified with affinity <10 µM and 11 of those had had sub-µM affinities and 2 had affinities <60nM representing a diverse and novel set of antagonist scaffolds.	Katritch, V. et al. (2010)
TNF-Alpha	Structure-based discovery of natural-product-like inhibitors.	Chan, D.S. et al. (2010)
Ricin Toxin	Identification of new classes of ricin toxin inhibitors.	Bai Y. et al. (2010)
Tumor maker, AKR1B10	Discovery of several chomene-3carboxamide derivatives as potent competitive inhibitors.	Endo S. et al. (2010)
Dynamin I and II GTPase	Pthaladyns active compounds discovered using ICM-VLS to a homology model.	Odell LR et al. (2010)
H5N1 Neuraminidase	Used MolSoft’s ICMPocketFinder to identify a new pocket conformation. Used ICM-VLS to identify a ligand with different binding pose and interactions than oseltamivir and zanamivir.	An, J. et al. (2009)
Aryl Hydrocarbon Receptor	Discovery of a new class of inhibitors.	Bisson W.H. et al. (2009)
PTPN22	Sub and low micormolar inhibitors discovered using ICM-VLS scoring.	Wu S. et al. (2009)
Thermolysin	NCI compound library screened and 12 inibitors discovered.	Khan MTH et al. (2009)
GPCR- Melanin Concentrating Hormone	First demonstration that GPCR models can be used for antagonist discovery by virtual screening.	Cavasotto, C.N. et al. (2008)
Ubiquitin-like Poxvirus Proteinase	230,000 available ketone and aldehyde compounds were screened. Out of 456 predicted ligands, 97 inhibitors of I7L proteinase activity were confirmed in biochemical assays.	Katritch, V. et al. (2007)
SARS Protease	In silico predictionof SARS protease inhibitors by ICM-VLS.	Plewczynski D et al. (2007)
Alpha Antitrypsin	Virtual ligand screening was performed on 1.2 million small molecules and 6 antagonists were identified which were further optimized using ICM tools.	Mallya, M. et al. (2007)
Serotonin N-acetyltransferase	1.2 million compounds were screened and 241 compounds tested resulting in the discovery of a new class of inhibitors.	Szewczuk, L.M. et al. (2007)
Androgen Receptor	Screening to multiple receptor conformations of the androgen receptor led to the identification of an antagonist.	Bisson, W.H. et al. (2007)
Enoyl Reductase	ChemBridge database was screened. 169 compounds were tested experimentally and 16 compounds had activity.	Nicola, G. et al. (2007)
EGFR Tyrosine Kinase	300K compounds were screened > 7 micromolar hits identified.	Cavasotto, C.N. et al. (2006)
Thyroid Receptor	250K compounds were screened, 75 were tested experimentally and 14 antagonists were discovered.	Schapira, M. et al. (2003)
RAR	Example of successful virtual screen to a homology model.	Schapira, M. et al. (2003)
TAR RNA	High enrichment factor and 7 new inhibitors identified.	Filikov, A.V. et al. (2000)

"I had a tough case of tightly bound ligands in a crystal structure whose bound poses were difficult to reproduce with other programs. They could just not find the right pose - often, ICM fit it right into this tight site, with terrific overlap with much of the crystal structure pose. Dr. Terry R. Stouch, PhD, Head, Computational Chemistry, Lexicon Pharmaceuticals

NOTE: Installation instructions for a linux cluster can be found here.