This document discusses the design of fragment screening libraries for fragment-based drug discovery. It describes how fragment hits have high ligand efficiency due to their low molecular weight. The document outlines criteria for selecting fragments, including molecular complexity, solubility, and availability of chemical neighbors. It presents details on the design of the GFSL05 20,000 compound screening library from AstraZeneca, including controlling molecular properties like size, lipophilicity, and structural diversity. Literature on fragment-based screening and library design is also cited.

1. Design of Fragment Screening Libraries
Peter W. Kenny
AstraZeneca, Alderley Park
IQPC Compound Libraries 2008

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

3. 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)

4. 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 M
hydrophobic m-subst
130 M
AZ11548766
3 M
PTP1B: Fragment elaboration
P
O
O
O
F F
P
O
O
O
F F
15M
Inactive at 200M
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)

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

6. Ligand Efficiency (Bang For Buck)
Does molecule punch its weight?
• Scale pIC50 or DGº by molecular weight or number of heavy
atoms as surrogate for molecular surface area
– Rationale: Molecules interact by presenting molecular
surfaces to each other. How effectively does a molecule
make use of its molecular surface?
• Fragment hits tend to have high ligand efficiency…
– But then they need to!
• Is high ligand efficiency indicative of hot spot on protein
surface?
Hopkins, Groom & Alex, Ligand efficiency: A useful metric for lead selection
Drug Discov. Today 2004, 430-431

7. 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

8. 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

9. Scheme for fragment based lead optimisation

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

11. 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

12. Measures of Diversity & Coverage
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
2-Dimensional representation of chemical space is used here to illustrate concepts of
diversity and converage. Stars indicate compounds selected to sample this region of
chemical space. In this representation, similar compounds are close together

13. Coverage & Diversity
Poor coverage of available
chemical space by small set of
mutually similar compounds
Reasonable coverage of
available chemical space given
small, diverse set of
compounds
Good coverage of available
chemical space by appropriate
number of compounds
• •
• •
•
•• •
• •
• •
•

14. Neighborhoods and library design

15. 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

16. NH
NN
H H
H H
O O
O
Me
NH
N
N H
H
H
H
O
O
O
Me
O
O
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)

17. 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

18. 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

19. Solubility in DMSO: Salts
Precipitate
observed
Precipitate
not 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

20. 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).

21. The 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

22. GFSL05: Design
• 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

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

24. 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)

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

26. 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

27. 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?

28. GFSL05 Acknowlegements
Jeff Albert
Sam Blackburn
Niklas Blomberg
Roger Butlin
Alex Breeze
Gill Burgess
Jeremy Burrows
Lindsey Cook
Dave Cosgrove
Al Dossetter
Phil Edwards
Kevin Embrey
Thomas Fex
Rutger Folmer
Richard Gallagher
Andrew Grant
Ed Griffen
James Haigh
Neil Hales
Richard Kilburn
Jin Li
Sorel Muresan
Paul Owen
Steve St-Gallay
Adam Shapiro
Ellen Simkiss
Kin Tam
Daniel Taylor
Dave Timms
Tony Wilkinson

29. 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
• 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.