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