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Criblage virtuel. Alexandre Varnek Faculté de Chimie, ULP, Strasbourg, FRANCE. computational. Filtering, QSAR, Docking. Small Library of selected hits. Virtual Screening. Hit. Target Protein. High Throughout Screening. Large libraries of molecules. experimental. - PowerPoint PPT Presentation
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Alexandre VarnekFaculté de Chimie, ULP, Strasbourg, FRANCE
• Criblage virtuel
Target Protein
Large librariesof molecules
High Throughout Screening
Hit
experimental
computational
Virtual Screening
Filtering, QSAR,Docking
Small Library of selected hits
Molecules are considered as vectors in multidimentional chemical space defined by the descriptors
Chemical universe:
• 10200 molecules
• 1060 druglike molecules
Virtual screening must be fast and reliable
CibleHTS
Criblage à haut débitHigh-throughput
screening Hits
Lead
Génomique
Analyse de données
Optimisation
Candidat au développement
Criblage à haut débit
Drug Discovery and ADME/Tox studies should be performed in parallel
idea target combichem/HTS hit lead candidate drug
ADME/Tox studies
Methodologies of a virtual screening
Platform for Ligand Based Virtual Screening
• Similarity search
~106 – 109
molecules
~103 - – 104
molecules
Candidates for docking or experimental tests
• Filters
• QSAR models
Virtual Screening
Molecules available for screening
(1) Real molecules
1 - 2 millions in in-house archives of large pharma and agrochemical companies3 - 4 millions of samples available commercially
(2) Hypothetical moleculesVirtual combinatorial libraries (up to 1060 molecules)
Methods of virtual High-Throughput Screening
• Filters• Similarity search • Classification and regression structure –
property models• Docking
Filters: Lipinski rules for drug-like molecules (« Rules of 5 »)
• H-bond donors < 5 • (the sum of OH and NH groups);
• MWT < 500;
• LogP < 5
• H-bond acceptors < 10 (the sum of N and O atoms without H attached).
Example of different filters:
Mol. W .
Log P
H-Don.
H-Acc.
H-D + H-A
Rot-Bonds
tPSA
Lipinski Veber AB/HIA
< 500< 5< 5
< 10------
---
< 1,000
< 10
< 6
< 19
< 22
< 19
< 291
< 770
< 9
---
---
< 12< 10
< 140
Rules for Absorbable compounds
Similarity Search:unsupervised and supervised approaches
2d (unsupervised) Similarity Search
0 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1
1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1
Tanimoto coef
0.80NNN
NTB&ABA
B&A
NO
N
S
N
O
OH
NO
N
S
N
O
OCl
H
molecular fingerprints
structural similarity “fading away” …
0.82
0.39
0.84
0.72
0.67
0.64
0.53
0.56
0.52
reference compounds
Structural Spectrum of Thrombin Inhibitors
small changes in structure have dramatic effects on activity
“cliffs” in activity landscapes
discontinuous SARscontinuous SARs
gradual changes in structure result in moderate changes in activity
“rolling hills” (G. Maggiora)
Structure-Activity Landscape Index: SALIij = Aij / Sij
Aij Sij ) is the difference between activities (similarities) of molecules i and j
R. Guha et al. J.Chem.Inf.Mod., 2008, 48, 646
Courtesy of Prof. J. Bajorath, University of Bonn
VEGFR-2 tyrosine kinase inhibitors
bad news for molecular similarity analysis...
MACCSTc: 1.00
Analog
6 nM
2390 nM
small changes in structure have dramatic effects on activity
“cliffs” in activity landscapes lead optimization, QSAR
discontinuous SARs
Courtesy of Prof. J. Bajorath, University of Bonn
Example of a “Classical” Discontinuous SAR
Adenosine deaminase inhibitors
(MACCS Tanimoto similarity)
Any similaritymethod mustrecognize thesecompounds asbeing “similar“ ...
Virtual Screening... when target structure is unknown
Virtual library Screening library
DiverseSubset
Parallel synthesisor synthesis of singlecompounds
Design of focussed library
Screening
HTS
Hits