I have lectured on design of fragment screening libraries a number of times and, to be honest, my material is getting a bit dated. This presentation is from Feb 2010 when I was visiting CSIRO and the photo in the title slide was taken in Tierra del Fuego.

1. Design of Fragment Screening Libraries
Peter W. Kenny (pwk.pub.2008@gmail.com)

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

3. Why fragments?
• Ligands are assembled from proven molecular
recognition elements
• Access to larger chemical space
• Ability to control resolution at which chemical
space is sampled
L

4. 2D Protein-observe NMR: PTP1B
15N
ppm
1H ppm
V49 F30
W125
Y46/T154
Ligand Conc
(mM)
o 0
o 0.5
o 1.0
o 2.0
o 4.0
N
S
O
N
O
O
O
Me
L83
G277
G283
T263
A278
D48
Observation of protein resonances allows
determination of Kd and can provides binding site
information. These techniques require isotopically
labelled protein and there are limits on the size of
protein that can be studied. (Kevin Embrey)

5. 1D Ligand-observe NMR
Ligand in buffer
Ligand and target protein
After saturation with potent inhibitor
Isotopically labelled protein is not required when
observing ligand resonances and there are no
restrictions on protein molecular weight. However
competition experiments are necessary to quantify
binding (Rutger Folmer).

6. -6 -5 -4 -3 -2
-10
0
10
20
30
40
50
60
70
80
90
log [compound]/M
%inhibition
IC50 = 371 mM
Biochemical assay run at high concentration
Inhibition of target enzyme by ~200 Da fragment. When using a biochemical assay at high
concentration it is necessary to check for non-specific binding and other potential artifacts. It is
also possible to assess solubility under assay conditions. Compounds identified by biochemical
assays are inhibitory which may not always be the case when using affinity methods. (Shapiro,
Walkup & Keating J. Biomol. Screen. 2009, 4, 1008 -1016)

7. Measurement of fragment binding by SPR
[Inhibitor] uM
0
0
0.2
0.4
0.6
0.8
1
0.001 0.01 0.1 1 10 100 1000
In these experiments, protein is first allowed to bind to ligand (target definition compound) that has
been tethered to sensor chip (Biacore). Test compounds binding competitvely with respect to TDC
effectively draw protein off sensor and strength of binding can be quantified (Wendy VanScyoc).
Fragment (~200 Da) binding with similar affinities (102 mM &145 mM) to different
forms of target protein

8. P
O
O
O
F F
P
O
O
O
F F
15mM
Inactive at 200mM
N
S
N
O
O
O
N
S
N
O
O
O
OMe
N
S
N
O
O
O
N
S
N
O
O
O
OMe
AZ10336676
3 mM
conformational lock
150 mM
hydrophobic m-subst
130 mM
AZ11548766
3 mM
PTP1B: Fragment elaboration
Elaboration by Hybridisation: Literature SAR was mapped
onto the fragment AZ10336676 (green). Note overlay of
aromatic rings of elaborated fragment AZ11548766 (blue)
and difluorophosphonate (red). See Bioorg Med Chem Lett,
15, 2503-2507 (2005)

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

10. The Hann molecular complexity model
Hann et al [2001]: Molecular Complexity and Its Impact on the Probability of Finding Leads
for Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864
Success landscape

11. Overview of fragment based lead discovery
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

12. Scheme for fragment based lead optimisation

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

14. Achtung!
Spitfire!
Hitting the target: The old way…
Stuka on wikipedia

15. “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 the new
B52 on wikipedia

16. Measures of Diversity & Coverage
•
• •
•
•
•
•
•
•
•
•
•
•
•
•
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

17. Coverage, Diversity & Library Design
••
• •
•
•• •
• •
• •
•

18. Neighborhoods and library design

19. 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)
• Meaningful measure of molecular similarity
– Structural neighbours likely to show similar response in assay

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

21. 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 Curr. Top. Med.
Chem. 2007, 7, 1600-1629)

22. 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

23. 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 Bioorg. Med. Chem. 2008, 16, 6611-6616).
Measure solubility for
neutral (at pH 7.4)
fragments for which
ClogP > 2.2

24. Solubility in DMSO: Salts
Precipitate
not observed
Precipitate
observed
All samples
Adduct 525 29 554
Not Adduct 4440 89 4529
All samples 4965 118 5083
Analysis of 5k solubilised samples showed that 5% of samples
registered as ‘adduct’ (mainly salts) showed evidence of precipitation
compared to 2% of the other samples

25. 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 Curr. Top. Med. Chem. 2007, 7, 1600-1629).

26. GFSL05 project
• Rationale
– Strategic requirement: Readily accessible source of compounds
for a range of fragment screening applications
– Tactical objective: Assemble 20k structurally diverse
compounds with properties that are appropriate for fragment
screening as 100mM DMSO stocks
• Design overview
– Core and layer design applied with successively more permissive
filters (substructural, neighborhood, properties)
– Bias compound selection to cover unsampled chemical space

27. At least they didn’t
make you
coordinate the
generic fragment
screening library
project
We should never
have listened to HR
OK it’s Dien Bien Phu or
you can coordinate the
generic fragment
screening library
project.
A personal view of coordinating GFSL05…

28. GFSL05: Overview
• Molecular recognition considerations
– Requirement for at least one charged center or acceptably
strong hydrogen bonding 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 terminal
atoms (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

29. APGNMR07: Overview
• General
– 1200 Compounds
– Derived in part form existing AZ NMR libraries and
GFSL05
– Molecules smaller than those in GFSL05
– 200mM in d6-DMSO
• Partitioning of library
– Groups (200) of 6 compounds defined
– Allows screening of mixtures of 6 or 12
– Acid:Base:Neutral = 1:1:4

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

31. GFSL05
APGNMR07
Lipophilicity profiles for fragment libraries

32. 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)

33. Library Property Ionized N Mean SD SE
GFSL05
Non-H atoms
No 12284 15.64 3.42 0.03
Yes 8058 16.04 3.58 0.04
ClogP
No 12284 1.248 1.056 0.010
Yes 8058 1.658 1.337 0.015
APGNMR07
Non-H atoms
No 800 13.66 2.18 0.08
Yes 400 13.91 2.14 0.11
ClogP
No 800 1.528 0.978 0.035
Yes 400 1.718 1.006 0.050
Summary statistics for fragment libraries

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

35. 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

36. A couple of questions to finish with…
• Is it helpful to think of leadlikeness in terms of the point at
which screening stops and synthesis begins?
• Does a screening technology that allows millimolar binding of
a compound to be characterized reliably make that compound
more leadlike?

37. Literature
General
• Erlanson et al, Fragment-Based Drug Discovery, J. Med. Chem., 2004, 47, 3463-3482.
• Congreve et al. Recent Developments in Fragment-Based Drug Discovery, J. Med. Chem., 2008
51, 3661–3680.
• 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
• Hann et al Molecular Complexity and Its Impact on the Probability of Finding Leads for
Drug Discovery, J. Chem. Inf. Comput. Sci., 2001, 41, 856-864
• Shuker et al, Discovering High Afinity Ligands for Proteins: SAR by NMR, Science,
1996, 274 1531-1534).
Screening Libraries
• Blomberg et al, Design of compound libraries for fragment screening, JCAMD 2009,
23, 513-525.
• Schuffenhauer et al, Library Design for Fragment Based Screening, Curr. Top. Med.
Chem. 2005, 5, 751-762.
• 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
• Colclough et al, High throughput solubility determination with application to selection
of compounds for fragment screening. Bioorg, Med. Chem. 2008, 16, 6611-6616.
• Kenny & Sadowski, Structure modification in chemical databases. Methods and
Principles in Medicinal Chemistry 2005, 23, 271-285.

38. FBDD Blogs
Practical Fragments: http://practicalfragments.blogspot.com
FBDD Literature: http://fbdd-lit.blogspot.com
(these will both lead you to LinkedIn & facebook groups)