Medicinal Chemistry Applications
CCG has over a decade of experience in
creating and deploying solutions for medicinal chemists in lead generation and optimization. The Molecular
Operating Environment (MOE) has been adopted by many of the top pharmaceutical
research companies for large-scale medicinal chemistry deployment. A
large number of small to medium sized pharmaceutical companies have also
deployed MOE as their primary medicinal chemistry modeling platform to
accelerate development efforts of novel therapeutics.
- Harmonized platform for both medicinal chemists and computational scientists
- Seamless communication between multiple diverse discovery project groups
- Ease of integration with in-house databases, servers and pipeline workflow systems
CCG has developed (in collaboration with
large pharmaceutical companies) a streamlined interface for active site
visualization and ligand optimization. A button bar
provides applications for structure preparation, active site analysis,
molecular property/binding affinity calculations, potential R-group
directions (for substitution opportunities) and ligand optimization in
an active site. Modify ligands using the 3D builder or using 2D
sketchers. Measure distances, angles and dihedral profiles.
Visualize and modify aligned complexes, toggle proteins on/off, browse
docking poses, pharmacophore hits and adjust rendering with the System Manager.
MOEsaic is a browser-based application for analyzing series of small molecule chemical structures and
related property data from drug discovery projects. Align molecules to facilitate pairwise comparison.
Conduct substructure and similarity searches. Perform Matched Molecular Pair (MMP) analyses.
Profile R-groups with defined scaffolds using a built-in chemical sketcher. Detect activity cliffs and bioisosteres.
Visualize the data through property Plots and applied Filters. Design virtual structures and Document findings with text and images.
Visualize and analyze ligand-receptor
interactions such as hydrogen bonds including CH..O interactions,
halogen bonds, sulfur-oxygen interactions, proton- and cation-π
interactions using Extended Hückel Theory (EHT). EHT more
accurately calculates interaction strengths and takes into account electron
withdrawal and resonance effects.
Automatically generate 2D diagrams [Clark 2007] of the active site
residues interacting with a ligand or series of ligands. Visualize
key interactions such as hydrogen bonds, salt bridges, hydrophobic
interactions, cation-π, sulfur-LP and halogen bonds in 2D. Identify potential
locations for ligand substitution using a depicted steric contour.
Visualize solvent exposed ligand atoms and residues with strong
hydrophobic interactions. Browse through a chemical series or receptor
family series to identify conserved or non-conserved interactions for selectivity
Build Molecular Surfaces colored by
properties to define and characterize active site topology and identify
ligand substitution opportunities. Predict knowledge-based non-bonded
Contact Preferences or calculate Electrostatic Maps using the
non-linear Poisson-Boltzmann equation to identify high value hydrophobic
regions and polar hot spots. Calculate water density and binding
desolvation penalty maps using 3D-RISM, a first principles theory of
solvation based on the Density Functional Theory of liquids. Detect non-obvious
hydrophobic regions of binding sites created by correlation and cavitation
effects to prioritize ligand modifications.
Explore ligand conformation space to gain
insights regarding bioactive conformations and intra-molecular
interactions. Use LowModeMD [Labute 2010] to generate
conformations of macrocycles and multi-component systems (e.g.,
explicit water or counter-ions) by performing a fast implicit vibrational
analysis and short molecular dynamics simulation.
Perform 3D alignment (or
superposition) of known and putative ligands to determine structural
requirements for biological activity - particularly useful in ligand-based
drug design protocols since aligned groups are likely to be important for
determining the bioactive conformation . Use the all-atom flexible
alignment procedure [Labute 2001] that combines a forcefield and a 3D
similarity function based on Gaussian descriptions of shape and pharmacophore
features to produce an ensemble of possible alignments of a collection of small
ligands, link fragments and replace scaffolds
[Grimshaw 2010] for fast follow-on compounds incorporating innovative
linear, cyclic or fused scaffold arrangements. Refine novel structures in
a (flexible) active site while maintaining important pharmacophore
interactions and calculate predicted binding affinities. Use Medicinal
Chemistry Transforms to explore local SAR by making small isosteric
changes to ligands. CCG provides a database of 170+ functional
group, homologation and hetercycle transformations using rules
extracted from the chemical literature. New transformations can be added
with standard 2D sketchers.
MOE contains the industry-leading
suite of pharmacophore discovery applications used for fragment-, ligand- and
structure-based design projects. Pharmacophore modeling is a powerful
means to generate and use 3D information to search for novel active
compounds, particularly when no receptor geometry is available. Pharmacophore
methods use a generalized molecular recognition representation and
geometric constraints to bypass the structural or chemical class bias of 2D
Use an interactive editor to construct a 3D query
from a molecular alignment or receptor structure. Perform a virtual
screen of a conformational database to determine candidate
active compounds. Customize pharmacophore features with SMARTS
chemical patterns (for particular groups) and/or expressions.
Restrict shape (receptor or ligand) by using union-of-spheres for
included, excluded and exterior volumes. Refine the query with directional
vector constraints on atoms or partial matches on features.
Calculate over four hundred 2D and 3D molecular descriptors including topological indices, structural keys,
E-state indices, physical properties, topological polar surface area (TPSA) and CCG's VSA descriptors [Labute 2003]
with wide applicability to both biological activity and ADME property prediction. Apply Extended Hückel-based descriptors,
such as LogP, LogD, and molar refractivity, for computing molecular properties. Calculate pKa and pKb
of small molecules and determine the populations of ligand protonation states at a given pH. Use descriptors for classification,
clustering, filtering and predictive model construction. Add custom descriptors using MOE's built-in Scientific Vector Language.
compound libraries through the reaction-based Combinatorial Library
Builder. Use commercial or customized in-house reagents as
input to a reaction engine. Conduct simple esterification reactions or
multi-component Ugi type or Groebke-Blackburn-Bienyame reactions. Use
standard 2D sketchers to specify reactions or multiple simultaneous
reaction steps. Automatically screen reaction products for
chemical similarity to a target or with a pharmacophore model.
Filter the results with chemical descriptors or Lipinski's rule-of-five
for drug-likeness. Calculate focused libraries by applying QSAR or
[Clark 2006] Clark,
A., Labute, P., Santavy, M.; 2D Structure Depiction; J. Chem. Inf.
Model. 46 (2006) 1107-C1123
[Clark 2007] Clark,
A. M., Labute, P.; 2D Depiction of Protein-Ligand Complexes; J. Chem.
Inf. Model. 47 (2007) 1933-C1944.
[Clark 2008] Clark,
A.M., Labute, P.; Detection and Assignment of Common Scaffolds in Project
Databases of Lead Molecules; J. Med. Chem. 52 (2008)
Grimshaw, S.; Scaffold Replacement in MOE; JCCG (2010)
[Labute 2001] Labute, P., Williams,
C., Feher, M., Sourial, E., Schmidt, J. M.; Flexible Alignment of Small
Molecules; J. Med. Chem. 44 (2001) 1483-C1490.
Labute, P.; The Derivation and Applications of
Molecular Descriptors Based Upon (Approximate) Surface Area; Chemoinformatics:
Concepts, Methods, and Tools for Drug Discovery; J. Bajorath ed. (2003)
[Labute 2010] Labute, P.; LowModeMD -
Implicit Low Mode Velocity Filtering Applied to Conformational Search of
Macrocycles and Protein Loops; J. Chem. Inf. Model. 50 (2010)