Virtual Screening Similarity

1
Virtual Screening - Similarity

• Chemoinformatics and Medicinal Chemistry Group
• Departamento de Química UFMG
• http//www.nequim.qui.ufmg.br
• jlopes_at_netuno.lcc.ufmg.br

Julio C. D. Lopes
UFMG, Set 2007 Belo Horizonte, Brazil
2
Evolution of Molecular Models
3
Similarity and Diversity
4
Volumes and Surface Potentials
5
Hydrophobic and Polar Regions
6
Hydrogen Bond Acceptor Potentials
7
Hydrogen Bond Donor Potentials
8
Molecular Electrostatic Potentials (MEP)
9
Similarity and Diversity
Volumes and Surface Potentials
Hydrophobic and Polar Regions
Hydrogen Bond Donor Potentials
Hydrogen Bond Acceptor Potentials
Molecular Electrostatic Potentials (MEP)
10
Modeling Drug Discovery
11
Strategies in Drug Design
12
Virtual Screening

• Virtual screening involves scanning databases of
  compounds to find molecules that may exhibit some
  bioactivity of interest, so as to prioritise a
  screening programme.
• Wide range of methods available
• Similarity searching, pharmacophore searching,
• substructural analysis, ligand docking and
  scoring.
• Varying amounts of structural and biological
  information, and varying computational
  requirements.

13
Virtual Screening Tools
14
Virtual Screening Alternatives
15
Virtual Screening Methods
2D Filtering o Topology, Molecular Graph o (Red.
Graphs, FTrees, )
3D Filtering o 3-point pharmacophores o
Distance hashing
3D Fitting o Flexible o From pre-computed conforme
rs
1D Filtering o Property Ranges o Fingerprints
e.g. MW 200-500 Lipinsky
16
3D Similarity Pharmacophores
17
Shape Signatures
18
Virtual Screening Workflow
N
Known ligands alignment
Receptor?
S
N
Homology
PDB?
S
N
Active Site Identification
Ligand?
S
Modelo farmacofórico
Active site mapping
Molecular Databases
Filter
Docking
Docking Sequential growth Linking
Filter
Hits
Scoring
LEAD
Fragments Database
19
Binary Fingerprints
20
Chemaxon Pharmacophore fingerprints
All pharmacophore feature pairs of captopril, and
the shortest paths between its pharmacophore
points.
21
Chemaxon Pharmacophore fingerprints
The pharmacophore feature pair histograms of
captopril, and the pharmacophore fingerprint of
captopril.
22
Similarity and Dissimilarity
23
Similarity indices
24
Similarity indices
25
Molecular Similarity
D.R.Flower J.Chem.Inf.Comput.Sci. 38 (1998) 379.
26
Similarity vs. Activity
Black square Salmonella mutagenicity of aromatic
amines Debnath et al. 1992 (log TA98) Red
circle Glende et al. 2001 set
alkyl-substituted (ortho to the amino function)
derivatives not included in original Debnath data
set
27
Do structurally similar molecules have similar
biological activity ?

• Set of 1645 chemicals with IC50s for monoamine
  oxidase inhibition
• Daylight fingerprints 1024 bits long ( 0-7
  bonds)
• When using Tanimoto coefficient with a cut off
  value of 0.85 only 30 of actives were detected

Cutoff values of actives detected False
positives
Y. Martin J. Med. Chem. 45, 4350-4358 (2002)
28
Pharmacophore Definition
A '''pharmacophore''' is a three-dimensional
substructure of a molecule that carries
(''phoros'') the essential features responsible
for a drug's (''pharmacon'') biological activity.
Alternatively described as an ensemble of
interactive functional groups with a defined
geometry. Basically, one tries to talk the
protein language by finding the "structural and
chemical complementaries" (pharmacophore
hypothesis) to target receptors.
http//en.wikipedia.org/wiki/Pharmacophore
29
Alignment by pharmacophore points
30
Pharmacophore Searching
14251druglike compounds
843search hits
31
Alignment of Hits
32
Quality of the Pharmacophore

• Adrenergic agonists

Hitsactive / Hitstotal
49 / 843
EF
7.0

DBactive / DBtotal
119 / 14251

• Dopaminic Agonists

Hitsactive / Hitstotal
41 / 843
EF
4.3

DBactive / DBtotal
162 / 14251
33
Pharmacophore Definition
34
Problems in Pharmacophore Definition

• Ionisation and Dissociation
• Quacpac (OpenEye)
• Marvin (ChemAxon)
• Tautomeric and protomeric forms
• Quacpac (OpenEye)
• Acceptor properties of oxygen and sulfur
  atoms (esters, aromatic ethers, oxazoles,
  isoxazoles, thiazoles, etc.)

35
Donor and Acceptor Properties of O and N
36
The Relevance of Different Conformations
a) in vacuo b) in water c)
in the crystal
37
Binding Mode of Citric Acid in Citrate Synthase
38
(No Transcript)
39
Clique-Detection

• Nodes comprise of matches between protein and
  ligand
• Edges connect distance compatible pairs of nodes
• In a clique all pair of nodes are connected

40
Pharmacophores
Combining Pharmacophore Search, Automated
Docking, and Molecular Dynamics Simulations as a
Novel Strategy for Flexible Docking. Proof of
Concept Docking of Arginine-Glycine-Aspartic
Acid-like Compounds into the avb3 Binding Site.
J. Med. Chem. 2004, 47, 4178-4187
41
LigBuilder A Multi-Purpose Program for
Structure-Based Drug Design
J. Mol. Model. 2000, 6, 498 516
42
Ph4Dock Pharmacophore-Based Protein-Ligand
Docking
J. Med. Chem. 2004, 47, 6804-6811
43
Software for Similarity comparisons
THINK (www.treweren.com) JCHEM (www.chemaxon.com)
Joelib (www.sourgeforce.org) Surflex Sim
(www.biopharmics.com) NEQUIM 3D Pharm
(www.nequim.qui.ufmg.br)
(available soon )
44
Conclusions
As understanding of the chemistry and biology of
drug action improves and a greater ability to
model the underlying mechanisms appears, the need
for similarity approaches will
diminish. Bender, A. Glen, R. C.
(2004) Molecular similarity a key technique in
molecular informatics. Org. Biomol. Chem.,
2(22), 3204-3218