1. Faculté de Chimie, ULP, Strasbourg, FRANCE
Master Chemoinfo
Criblage virtuel
Alexandre Varnek
Faculté de Chimie, ULP, Strasbourg, FRANCE

2. Small Library of selected hits
experimental
computational
Virtual Screening
Filtering,
QSAR,
Docking
Small Library of selected hits
High Throughout Screening
Hit
Target Protein
Large libraries
of molecules

3. Virtual screening must be fast and reliable
Chemical universe:
10200 molecules
1060 druglike molecules
Virtual screening must be fast and reliable
Molecules are considered as vectors in multidimentional chemical space defined by the descriptors 3

4. Candidat au développement
Criblage à haut débit
Cible
HTS
Criblage à haut débit
High-throughput
screening
Hits
Lead
Génomique
Analyse de données
Optimisation
Candidat au développement

5. Drug Discovery and ADME/Tox studies should be performed in parallel
idea target combichem/HTS hit lead candidate drug
ADME/Tox studies

6. Methodologies of a virtual screening
from A.R. Leach, V.J. Gillet “An Introduction to Chemoinformatics”, Kluwer Academic Publisher, 2003

7. Platform for Ligand Based Virtual Screening
~106 – 109
molecules
Filters
Similarity search
~103 - – 104
molecules
QSAR models
Candidates for docking or experimental tests 7

8. Criblage à haut débit (HTS)
Mots clés:
- Chimie combinatoire
Criblage à haut débit
(High Throughput Screening (HTS))
- Screening virtuel
- Aspect Drug-like
- Training sets jusqu’à composés

9. Virtual Screening Molecules available for screening (1) Real molecules
1 - 2 millions in in-house archives of large pharma and agrochemical companies
3 - 4 millions of samples available commercially
(2) Hypothetical molecules
Virtual combinatorial libraries (up to 1060 molecules)

10. Methods of virtual High-Throughput Screening
Filters
Similarity search
Classification and regression structure – property models
Docking

11. Filters to estimate “drug-likeness”

12. Lipinski rules for intestinal absorption (« Rules of 5 »)
H-bond donors < 5
(the sum of OH and NH groups);
MWT < 500;
LogP < 5
H-bond acceptors < (the sum of N and O atoms without H attached).

13. Lipinski rules for drug-like molecules (« Rules of 5 »)

14. Lipinski rules for drug-like molecules (« Rules of 5 »)

15. Example of different filters:
Rules for Absorbable compounds
It is quite interesting to compare our permeability model to the Lipinski’s and Veber’s rules. All three models are described by similar parameters. The following table shows the maximum cut-off values for absorbable compounds in our data set. In bold we show cut-off values of Lipinski’s and Veber’s rules. By comparing these three columns we can see that in most cases the cut-off values of Lipinski’s and Veber’s rules have been exceeded by 100 percent. This observation has a dual explanation. First, all three models dealt with quite different biological phenomena. Lipinski analyzed compounds that reached the second phase of clinical trials. Veber analyzed oral bioavailability in rats (which is affected by metabolism to a much greater extend than HIA). Whereas we analyzed HIA. The second explanation is that all models have been derived using quite different analytical tools. We used C-SAR analysis that automatically considers a large variety of possible causes that determine poor permeability. Lipinski and Veber used conventional data mining techniques.

16. Remove compounds containing too many rings

17. Remove compounds with toxic groups

18. Remove compounds with reactive groups

19. Remove False-Positive Hits

20. Remove poorly soluble compounds

21. Filter on inorganic and heteroatom compounds

22. Remove compounds with multiple chiral centers

23. Paclitaxel (Taxol): violation of 2 rules
MW = 837
logP=4.49
HD = 3
HA = 15

24. logD vs logP
95% of all drugs are ionizable :
75% are bases and 20% acids
Utilizing pH dependent log D as a descriptor for lipophilicity in place of log P significantly increases the number of compounds correctly identified as drug-like using the drug-likeness filter: log D5.5 < 5
The Rule of Five Revisited: Applying Log D in Place of Log P in Drug-Likeness Filters
S. K. Bhal, K. Kassam, I. G. Peirson, and G. M. Pearl , MOLECULAR PHARMACEUTICS, v.4, , (2007)

25. Synthetic Accessibility
is proportional to fragment’s occurrence in the PubChem database
Ertl and Schuffenhauer Journal of Cheminformatics :8

26. Synthetic Accessibility
Frequency distribution of fragments
Altogether 605,864 different fragment types have been obtained by fragmenting the PubChem structures. Most of them (51%), however are singletons (present only once in the whole set). Only a relatively small number of fragments, namely 3759 (0.62%), are frequent (i.e. present more than 1000-times in the database).
Ertl and Schuffenhauer Journal of Cheminformatics :8

27. Synthetic Accessibility
The most common fragments present in the million PubChem molecules. The "A" represents any non-hydrogen atom, "dashed" double bond indicates an aromatic bond and the yellow circle marks the central atom of the fragment.
Ertl and Schuffenhauer Journal of Cheminformatics :8

28. Synthetic Accessibility
Distribution of (- Sascore) for natural products, bioactive molecules and molecules from catalogues.
Correlation of calculated (-SAscore ) and average chemist estimation for 40 molecules (r2 = 0.890)
Ertl and Schuffenhauer Journal of Cheminformatics :8

29. Similarity Search:
unsupervised and supervised approaches 29

30. 2d (unsupervised) Similarity Search
Tanimoto coef
Recherche par similarité; comparaison des clés structurales;
molecular fingerprints 30

32. Contineous and Discontineous SAR

33. Structural Spectrum of Thrombin Inhibitors
structural similarity “fading away” …
reference compounds
0.56
0.72
0.53
0.84
0.67
0.52
0.82
0.64
0.39

34. R. Guha et al. J.Chem.Inf.Mod., 2008, 48, 646
discontinuous SARs
continuous SARs
gradual changes in structure result in moderate changes in activity
“rolling hills” (G. Maggiora)
small changes in structure have dramatic effects on activity
“cliffs” in activity landscapes
Structure-Activity Landscape Index: SALIij = DAij / DSij
DAij (DSij ) is the difference between activities (similarities) of molecules i and j
R. Guha et al. J.Chem.Inf.Mod., 2008, 48, 646

35. discontinuous SARs VEGFR-2 tyrosine kinase inhibitors
MACCSTc: 1.00
Analog
6 nM
2390 nM
small changes in structure have dramatic effects on activity
“cliffs” in activity landscapes
lead optimization, QSAR
bad news for molecular similarity analysis...

36. Example of a “Classical” Discontinuous SAR
Any similarity method must recognize these
compounds as being “similar“ ...
(MACCS Tanimoto similarity)
Adenosine deaminase inhibitors

37. Supervised Molecular Similarity Analysis

38. Dynamic Mapping of Consensus Positions
Prototypic “mapping algorithm” for simplified binary-transformed* descriptor spaces
Uses known active compounds to create activity-dependent consensus positions in chemical space
Operates in descriptor spaces of step-wise increasing dimensionality (“dimension extension”)
Selects preferred descriptors from large pools
* median-based, i.e. assign “1” to a descriptor if its value is greater than (or equal to) its screening database median; assign “0” if it is smaller
Godden et al. & Bajorath. J Chem Inf Comput Sci 44, 21 (2004)

39. DMC Algorithm Calculate and binary transform descriptors
Descriptor bit strings for reference molecules
DMC Algorithm
Calculate and binary
transform descriptors
Compare descriptor bit strings of reference molecules and determine consensus bits
Select DB compounds matching consensus bits
Re-generate bit strings
permitting bit variability
Select DB compounds
matching extended bit strings
Repeat until a small selection set is obtained …
Calculate consensus bit string:
= 1.0 or = 0.0
no variability
1. Dimension extension:
³ 0.9 or £ 0.1
10% variability
2. Dimension extension:
³ 0.8 or £ 0.2
20% variability
(white “0”, black “1” gray, variably set bits) 1 2
e.g. 0%, 10%, 20% permitted bit variability:
longer bit strings – fewer matching DB compounds

40. QSAR/QSPR models 40

41. Screening and hits selection
Database
Virtual
Sreening
QSPR model
Experimental
Tests
Hits
Useless
compounds 41

42. Libraries profiling: indexing a database by simultaneous assessment of various activities
Example:
PASS software
(Prediction of Activity Spectra for Substances)

43. For each fragment i

44. PASS Naïve Bayes estimator Calculations of « P(act) » and « P(inact) »
Molecule is considered as active if
 P(act) > P(inact) or/and   P(act) > 0.7

45. Quantitative Structure-Property Relationships
(QSPR)
Y = f (Structure)
= f (descriptors)
QSPR restricts reliable predictions for compounds which are similar to those used for the obtaining the models.
Similarity / pharmacophore search approaches are still inevitable as complementary tools

46. Combinatorial Library Design

47. ... when target structure is unknown
Virtual Screening
... when target structure is unknown
Virtual library
Screening library
Diverse
Subset
Parallel synthesis or
synthesis of single
compounds
Design of
focussed library
Screening
HTS
Hits

48. Generation of Virtual Combinatorial Libraries
Fragment Marking approach
Markush structure if
R1, R2, R3 =
and
then

49. The types of variation in Markush structures:
Substituent variation (R1)
Position variation (R2)
Frequency variation
Homology variation (R3) (only for patent search)
n = 1 – 3
R2 =NH2
R3 = alkyl or
heterocycle
R1 = Me, Et, Pr

50. Generation of Virtual Combinatorial Libraries
Reaction transform approach
from A.R. Leach, V.J. Gillet “An Introduction to Chemoinformatics”, Kluwer Academic Publisher, 2003

51. Issues and Concepts in Combinatorial Library Design
Size of the library
Coverage of properties („chemical space“)
Diversity, Similarity, Redundancy
Descriptor validation
Subset selection from virtual libraries

52. Hot topics in chemoinformatics
Predictions vs interpretation
New approaches in structure-property modeling
descriptors,
applicability domain
machine-learning methods (inductive learning transfer, semi-supervised learning, ....)
New techniques to mine chemical reactions
Schematiquement,
QSAR of complex systems
multi-component synergistic mixtures, new materials, metabolic pathways, ...
Public availability of chemoinformatics tools

53. Predictions vs interpretation
Nathan BROWN “Chemoinformatics—An Introduction for Computer Scientists”
ACM Computing Surveys, Vol. 41, No. 2, Article 8, February 2009

54. Predictions vs interpretation
Problems :
Ensemble modeling
Non-linear machine-learning methods (SVM, NN, …)
Descriptors correlations
What do end users expect from QSAR models ?
Reliable estimation (prediction) of the given property.

55. Public accessibility of models:
WEB based platform for virtual screening
Schematiquement,

56. Some Screen Shots: Welcome Page…

57. ISIDA property prediction WEB server
infochim.u-strasbg.fr/webserv/VSEngine.html

58. ISIDA ScreenDB tools only INTERNET browser is required
only INTERNET browser is required
Different descriptors
(ISIDA fragments, FPT, ChemAxon)
Similarity search with different metrics (Tanimoto, Dice, …)
ensemble modeling approach
(simulteneous application of several models)
models applicability domain
(automatic detection of useless models)

59. The most fundamental and lasting objective of synthesis is not production of new compounds but production of properties
George S. Hammond
Norris Award Lecture, 1968 59