1. a Virtual Compound Space
Screening
a Virtual Compound Space
Szabolcs Csepregi
Ferenc Csizmadia
Szilárd Dóránt
Nóra Máté
György Pirok
Zsuzsanna Szabó
Jenő Varga
Miklós Vargyas
ChemAxon Ltd.
Máramaros köz 3/a
1037 Budapest
Hungary

2. Drug research Finding or making a needle in the hay stack?
virtual screening
JChem Screen
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

3. Drug research Finding or making a needle in the hay stack?
virtual screening
JChem Screen
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

4. Virtual Screening Find something similar to a fistful of needles
corporate database
structures found
known actives

5. Molecular similarity How to tackle it?
Quantitative assessment of similarity/dissimilarity of structures
need a numerically tractable form
molecular descriptors, fingerprints, structural keys
Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics.

6. Virtual screening using fingerprints
Multiple query structures
hits
queries
hypothesis fingerprint
metric
targets
target fingerprints

7. Optimized virtual screening
Parameterized metrics
asymmetry factor
scaling factor
asymmetry factor
weights

8. How good is optimized virtual screening?
β2-adrenoceptor antagonist

9. Is virtual screening a discovery tool?
Scaffold hopping

10. Drug research Finding or making a needle in the hay stack?
virtual screening
JChem Screen
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

11. JChem AnalogMaker
Workflow
Lead
Candidates

12. Fragmentation Examples Fragmentation rules Original molecule
Generated fragments
Fragment 1
amide 2
Amide
Fragment 2
amide 1
ester 1
Ester
Fragment 3
ester 2

13. Fragmentation RECAP rules 1 = amide 2 =ester 3 = amine 4 = urea
5 = ether
6 = olefin
7 = quaternary nirogen
8 = aromatic N carbon
9 = lactam N carbon
10 = aromatic carbon – aromatic carbon
11 = sulphonamide
Xiao Qing Lewell, Duncan B. Judd, Stephen P. Watson, Michael M. Hann; RECAP – retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J. Chem. Inf. Comput. Sci. 1998, 38, 511–522

14. JChem AnalogMaker General algorithm start
create building block library
generate pharmacophore hypothesis of active compounds
create several starting compounds by random combination of some building blocks
select parent structure
convergence or end of optimization
generate  variants of parent
stop

15. Variant generation Example: TOPAS modifier
G. Schneider et al, J. Comput.-Aided Mol. Design, 14(2000):
G. Schneider et al, Angew. Chem. Int. Ed., 39(2000):

16. Drug research Finding or making a needle in the hay stack?
virtual screening
JChem Screen
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

17. Drug research Finding or making a needle in the hay stack?
virtual screening
JChem Screen ?
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

18. random virtual synthesis
Drug research
Screening a virtual compound space
virtual screening
JChem Screen
random virtual synthesis
JChem Synthesizer
de novo design
JChem AnalogMaker
advantages
disadvantages
fast
hits are readily available for in vitro screening
limited number of available compounds
advantages
disadvantages
fast
virtual molecules are likely to be synthetically available
practically infinite virtual compound space
structural novelty
advantages
disadvantages
practically unlimited virtual compound space
structural novelty
synthetic accessibility of virtual hits is a problem

19. Screening a virtual compound space
Smart reactions
Generic (simple)
the equation describes the transformation only
few hundred generic reactions can form the basic armory of a preparative chemist
Specific (complex)
chemo-, recognizes reactive and inactive functional groups
regio-, "knows" directing rules
stereo-, inversion/retention
Customizable
to improve reaction model quality

20. Smart reactions Chemoselectivity
REACTIVITY: !match(ratom(3), "[#6][N,O,S:1][N,O,S]", 1)

21. Smart reactions Chemoselectivity
REACTIVITY: !match(ratom(3), "[#6][N,O,S:1][N,O,S]", 1) && !match(ratom(3), "[N,O,S:1][C,P,S]=[N,O,S]", 1)

22. Smart reactions Regioselectivity SELECTIVITY: -charge(ratom(1))
TOLERANCE:

23. Smart reactions Regioselectivity SELECTIVITY: -charge(ratom(1))
TOLERANCE:

24. Smart reaction library
Example
Baeyer-Villiger ketone oxidation
SELECTIVITY: charge(ratom(2), "sigma")

25. Smart reaction library
Baeyer-Villiger ketone oxidation
Generic reaction

26. Smart reaction library
Example
Baeyer-Villiger ketone oxidation

27. Virtual compound space
JChem Synthesizer
Screen
Hits
Active
set1
Workflow
Virtual compound space
Available
chemicals
Synthesizer
Screen
Hits
Active
setn
Smart
reaction
library

28. JChem Synthesizer example
Dopamine D2 actives
Active sets were kindly provided by Aureus Pharma within a research collaboration between Aureus and ChemAxon.

29. JChem Synthesizer example
Virtual hits
similarity: 2D pharmacophore fingerprint, weighted Euclidean metric optimized for 20 random d2 actives

30. JChem Synthesizer example
Best virtual hits
9.88
9.82
9.53
9.73

31. JChem Synthesizer example
Synthesis path
step 1
Knoevenagel-Doebner condensation

32. JChem Synthesizer example
step 2
Baylis-Hillman vinyl alkylation

33. JChem Synthesizer example
step 3
Lawesson thiacarbonylation

34. JChem Synthesizer example
step 4
Dess-Martin alcohol oxidization

35. JChem Synthesizer example
Software and performance data
virtual reactions: reactions/s
random synthesis: structures/s
pharmacophore fingerprint generation: 100 structure/s (includes pharmacophore point perception)
metric optimization: 57 sec (13 parameterized metrics, 20 structures in training set, 50 spikes)
virtual screening: 7500 structure/s
pure Java
client: P4 1.6GHz, RH Linux, java 1.4.2
database server: P4 2.4GHz, Windows XP, MySQL

36. Acknowledgements ChemAxon Jean-Michael Drancourt François Petitet
Modest von Korff, Matthias Steger
(Axovan is now
part of Actelion.)
Alex Allardyce
ChemAxon

37. Contact Miklós Vargyas mvargyas@chemaxon.hu office: +36 1 453 2661
mobile: