Slimmed down fragment screening library talk presented at University of Adelaide (Dec 2011) and Pharmaxis (Feb 2012). Includes dingo and Maria Sharapova (losing finalist at 2012 Australian Open). The photo for the title slide is of a range finder from the Admiral Graf Spee and was taken in Montevideo.

1. Design of Compound Libraries for Fragment Screening
Peter W. Kenny
pwk.pub.2008@gmail.com | http://fbdd-lit.blogspot.com

2. Achtung!
Spitfire!
A Brief History of Screening: In the Beginning…
Stuka on wikipedia

3. “Why can’t we pray for something good, like a tighter
bombing pattern, for example? Couldn’t we pray for a
tighter bombing pattern?” , Heller, Catch 22, 1961
… and then there was HTS
B52 on wikipedia

4. So, Maria, why do you
think it is that the
Russians are so much
better than the
Germans at tennis
these days?
Actually we started
to beat them at their
national sport almost
70 years ago and...
.... as Uncle Joe
was so fond of
saying, quantity
has a quality all of
its own.
Even the stars of tennis have heard of HTS....

5. The HTS was YOUR idea
so don’t try blaming me!
Unfortunately HTS is not a panacea

6. FBDD Essentials
Screen fragments
Synthetic
Elaboration
Target
Target & fragment hit
Target & lead

7. Linking
Fragment Elaboration Tactics
Merging
Growing

8. Why fragments?
• Access to larger chemical space
• Counter the advantage of competitors’ large
compound collections
• Ligands are assembled from proven molecular
recognition elements
• A smart way to do Structure-Based Design
• Control resolution at which chemical space is
sampled

9. PTP1B (Diabetes/Obesity): Fragment elaboration
Elaboration by Hybridisation: Literature SAR was mapped onto intial fragment hit
(green). Note overlay of aromatic rings of elaborated fragment (blue) and
difluorophosphonate (red). See Black et al BMCL 2005, 15, 2503-2507 |
http://dx.doi.org/doi:10.1016/j.bmcl.2005.03.068
Inactive at 200mM
15 mM
3000 mM 3 mM
150 mM
(Conformational lock)
130 mM
(3-Phenyl substituent)

10. Fragment-based lead discovery: Generalised workflow
Target-based
compound selection
Analogues of known
binders
Generic screening
library
Measure
Kd or IC50
Screen
Fragments
Synthetic
elaboration
of hits
SAR
Protein
Structures
Milestone achieved!
Proceed to next
project

11. A model for molecular complexity
This model is equally relevant to conventional and fragment-based screening. See Hann, Leach
& Harper J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | http://dx.doi.org/10.1021/ci000403i
Success landscape

12. Degree of substitution as measure of molecular complexity
The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be
observed, will deliver a hit against both targets See Blomberg et al JCAMD 2009, 23, 513-525 |
http://dx.doi.org/10.1007/s10822-009-9264-5 | This way of thinking about molecular complexity is
similar to the ‘needle’ concept introduced by Roche researchers. See Boehm et al J. Med. Chem.
2000, 43, 2664-2774 | http://dx.doi.org/10.1021/jm000017s

13. Ligand Efficiency
LE= DGº/NonHyd
Hopkins, Groom & Alex, DDT 2004, 9, 430-431
Binding Efficiency Index
BEI= pIC50/(MW/kDa)
Abad-Zapaftero & Metz, DDT 2005, 10, 430-431
Ligand Lipophilicity Efficiency
LLE = pIC50 - ClogP
Leeson & Springthorpe , NRDD 2007, 6, 881-890.
Measured binding is scaled Measured binding is offset
Binding Efficiency
Measures

14. Fragment screening requirements
• Assay capable of reliably quantifying weak (~mM)
binding
• Library of compounds with low molecular complexity
and good (~mM) aqueous solubility
•

15. Screening Library Design Requirements
• Precise specification of substructure
– Count substructural elements (e.g. chlorine atoms; rotatable bonds;
terminal atoms; reactive centres…)
– Define generic atom types (e.g. anionic centers; hydrogen bond
donors)
– SMARTS notation is particularly useful
• Meaningful measure of molecular similarity
– Structural neighbours likely to show similar response in assay

16. Measures of Diversity & Coverage
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•
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•
•
•
•
•
•
•
•
2-Dimensional representation of chemical space is used here to illustrate concepts of diversity and
coverage. Stars indicate compounds selected to sample this region of chemical space. In this
representation, similar compounds are close together. The title slide for this talk shows the optical range
finder that was salvaged from the pocket battleship Admiral Graf Spee and can be seen in Montevideo.

17. Coverage, Diversity & Library Design
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•
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18. Neighborhoods and library design

19. Hits, non-hits & lipophilicity: Survival of the fattest*
Mean Std Err Std Dev
Hits 2.05 0.08 1.10
Non-Hits 1.35 0.03 1.24
*Analysis of historic screening data & quote: Niklas Blomberg, AZ Molndal
Comparison of ClogP for hits and non-hits from
fragment screens run at AstraZeneca

20. Sample
Availability
Molecular
Connectivity
Physical
Properties
screening samples Close analogs Ease of synthetic
elaboration
Molecular
complexity
Ionisation Lipophilicity
Solubility
Molecular
recognition
elementsMolecular shape
3D Pharmacophore
Privileged
substructures
Undesirable
substructures
Molecular
size
3D Molecular
Structure
Fragment selection criteria
Why I don’t use the rule of 3: http://fbdd-lit.blogspot.com/2011/01/rule-of-three-considered-harmful.html

21. Acceptable diversity
And coverage?
Assemble library in
soluble form
Add layer to core
Incorporate layer
Yes
No
Select core
Core and layer library design
Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers
typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library
design can be applied with Flush or BigPicker programs (Dave Cosgrove, AstraZeneca, Alderley Park)
using molecular similarity measures calculated from molecular fingerprints. See Blomberg et al
JCAMD 2009, 23, 513-525 | http://dx.doi.org/10.1007/s10822-009-9264-5

22. ClogP: Charged library compounds
ClogP: Neutral library compoundsNon-hydrogen atoms
GFSL05: Size and lipophilicity profiles
Rotatable bonds

23. 61
17
13
4 4
1
0
Breakdown of GFSL05 by charge type
Neutral
Anion Cation
Ionisation states are identified using AZ ionisation and tautomer model. Multiple forms are generated for
acids and bases where pKa is thought to be close to physiological pH. See Kenny & Sadowski Methods
and Principles in Medicinal Chemistry 2005, 23, 271-285 | http://dx.doi.org/10.1002/3527603743.ch11

24. GFSL05: Numbers of neighbours within library as function of
similarity (Tanimoto coefficient; foyfi fingerprints)
0.90 0.85 0.80

25. FBDD Blogs
These are ‘crosslinked’ and both will direct you to LinkedIn & facebook groups
Practical Fragments: http://practicalfragments.blogspot.com
FBDD Literature: http://fbdd-lit.blogspot.com

26. Spare slides

27. A (small) selection of literature
General
• Erlanson, McDowell & O’Brien, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463-
3482 | http://dx.doi.org/10.1021/jm040031v
• Congreve et al. Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem., 2008 51,
3661–3680 | http://dx.doi.org/10.1021/jm8000373
• Albert et al, An integrated approach to fragment-based lead generation: philosophy, strategy and
case studies from AstraZeneca's drug discovery programmes. Curr. Top. Med. Chem. 2007, 7,
1600-1629 | http://www.ingentaconnect.com/content/ben/ctmc/2007/00000007/00000016/art00006
• Hann, Leach & Harper, Molecular Complexity and Its Impact on the Probability of Finding Leads for
Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | | http://dx.doi.org/10.1021/ci000403i
Screening Libraries
• Blomberg et al, Design of compound libraries for fragment screening, JCAMD 2009, 23, 513-525 |
http://dx.doi.org/10.1007/s10822-009-9264-5
• Schuffenhauer et al, Library Design for Fragment Based Screening, Curr. Top. Med. Chem. 2005, 5,
751-762 | http://www.ingentaconnect.com/content/ben/ctmc/2005/00000005/00000008/art00003
• Baurin et al, Design and Characterization of Libraries of Molecular Fragments for Use in NMR
Screening against Protein Targets, J. Chem. Inf. Comput. Sci., 2004, 44, 2157-2166 |
http://dx.doi.org/10.1021/ci049806z
• Colclough et al, High throughput solubility determination with application to selection of compounds
for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616 |
http://dx.doi.org/doi:10.1016/j.bmc.2008.05.021
• Kenny & Sadowski, Structure modification in chemical databases. Methods and Principles in
Medicinal Chemistry 2005, 23, 271-285 | http://dx.doi.org/10.1002/3527603743.ch11

28. Scheme for fragment based lead optimisation

29. 20%
10%
30%
40%
50%
log(S/M)
Aqueous solubility:
Percentiles for measured log(S/M) as function of ClogP
Data set is partitioned by ClogP into bins and the percentiles and mean ClogP is calculated for each. This way of plotting results is
particularly appropriate when dynamic range for the measurement is low. Beware of similar plots where only the mean or median
value is shown for the because this masks variation and makes weak relationships appear stronger than they actually are. See
Colclough et al Bioorg. Med. Chem. 2008, 16, 6611-6616 | http://dx.doi.org/doi:10.1016/j.bmc.2008.05.021
Measure solubility for
neutral (at pH 7.4)
fragments for which
ClogP > 2.2

30. GFSL05 project
• Strategic requirement:
– Readily accessible source of compounds for a range of fragment
screening applications (NMR, Biochemical Assay, HTS at 10 x
normal concentration)
• Tactical objective:
– Assemble 20k structurally diverse compounds with properties that
are appropriate for fragment screening as 100mM DMSO stocks

31. GFSL05: Overview
• Molecular recognition considerations
– Requirement for at least one charged center or acceptably strong
hydrogen bond donor or acceptor
• Substructural requirements defined as SMARTS
– Progressively more permissive filters to apply core and layer design
– Restrict numbers of non-hydrogen atoms (size) and extent of
substitution (complexity)
– Filters to remove undesirable functional groups (acyl chloride) and
to restrict numbers of others (nitro, chloro)
– ‘Prototypical reaction products’ for easy follow up
• Control of lipophilicity (ClogP) dependent on ionisation state
– Solubility measurement for more lipophilic neutrals
• Tanimoto coefficient calculated using foyfi fingerprints (Dave
Cosgrove) as primary similarity measure
– Requirement for neighbour availability in core and layer design

32. APGNMR07: Overview
• General
– Designed for NMR screening (especially 2D protein detect)
– 1200 Compounds
– Derived in part from existing AZ NMR libraries and GFSL05
– Molecules smaller on average than those in GFSL05
– Stock solutions: 200mM in d6-DMSO
• Partitioning of library for cocktailing
– Groups (200) of 6 compounds defined
– Allows screening in mixtures of 6 or 12
– Acid:Base:Neutral = 1:1:4

33. GFSL05
APGNMR07
Lipophilicity profiles for GFSL05 and APGNMR07

34. GFSL05: Numbers of available neighbours as function of similarity
(Tanimoto coefficient; foyfi fingerprints) and sample weight
>10mg
>20mg
0.90 0.85 0.80
0.90 0.85 0.80