REVIEWS
Twenty years on: the impact of
fragments on drug discovery
Daniel A. Erlanson1, Stephen W. Fesik2, Roderick E. Hubbard3,4, Wolfgang Jahnke5
and Harren Jhoti6
Abstract | After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has
become mainstream. More than 30 drug candidates derived from fragments have entered the
clinic, with two approved and several more in advanced trials. FBDD has been widely applied
in both academia and industry, as evidenced by the large number of papers from universities,
non-profit research institutions, biotechnology companies and pharmaceutical companies.
Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to
computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly
realized, now is an opportune time to draw lessons and point the way to the future. This Review
briefly discusses how to design fragment libraries, how to select screening techniques and how to
make the most of information gleaned from them. It also shows how concepts from FBDD have
permeated and enhanced drug discovery efforts.
Carmot Therapeutics, Inc.
409 Illinois Street,
San Francisco, California
94158, USA.
2
Department of Biochemistry,
Vanderbilt University School
of Medicine, 2215 Garland
Avenue, 607 Light Hall,
Nashville, Tennessee
37232–0146, USA.
3
York Structural Biology
Laboratory, Department of
Chemistry, University of York,
York YO10 5DD, UK.
4
Vernalis Research,
Granta Park, Abington,
Cambridge CB21 6GB, UK.
5
Novartis Institutes for
Biomedical Research,
Novartis Campus,
CH‑4002 Basel, Switzerland.
6
Astex Pharmaceuticals,
436 Cambridge Science Park,
Milton Road, Cambridge
CB4 0QA, UK.
1
Correspondence to D.A.E. [email protected]
doi:10.1038/nrd.2016.109
Published online 15 Jul 2016
Drug discovery is constantly changing. New technologies
appear, mature and challenge existing technologies. Some
technologies disappear quickly, others establish themselves as new standards and yet others merge with existing technologies. Two decades ago, high-throughput
screening (HTS) was fast becoming a dominant leaddiscovery approach1, and by the early 2000s, companies
were building multimillion compound libraries. This
led to many drug leads, particularly against established
classes of targets. However, when screened against newer
or more difficult targets, huge compound libraries sometimes yielded few hits2 or, in more problematic cases,
yielded hits that were false positives, including aggregators, a type of artefact not widely recognized until the
early 2000s3 and which remains a problem even today 4.
At the same time, there was a growing awareness of the
enormity of chemical space. An early estimate put the
number of possible small drug-like molecules at 1063
— more than the number of stars in the universe5. In
this context, a library of 10 million compounds seems
rather paltry.
Fragment-based drug discovery (FBDD) takes a
different approach. Rather than screening millions of
compounds to find drug-sized starting points, FBDD
begins with much smaller collections of smaller compounds. Fragments are usually defined as having less
than 20 non-hydrogen (or ‘heavy’) atoms. In contrast to
the astronomical numbers of possible drug-like molecules
with up to 30 heavy atoms, the number of possible fragments is much lower. In fact, the Reymond group6 has
been systematically and computationally enumerating all
possible molecules containing key elements such as carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine
and iodine (as well as hydrogen). The numbers are large
but much more manageable, with just over 166 billion
possibilities for molecules having up to 17 heavy atoms.
This is actually an undercount, as certain classes of molecules were omitted, but even if the real number is severalfold higher, the sampling of chemical space is much more
efficient with fragments than with larger molecules.
This discussion is best appreciated with actual examples. Most fragment libraries consist of a few thousand
molecules7, about three orders of magnitude smaller than
a typical HTS collection. In a recent analysis of nearly
150 fragment-to-lead campaigns, fragments had around
15 heavy atoms, whereas the optimized compounds had
around 28 heavy atoms8. According to Reymond’s analysis, each atom adds roughly an order of magnitude to the
number of possibilities, meaning that the chemical space
of the leads is at least 13 orders of magnitude larger than
the chemical space of the fragments. Even if this figure
includes molecules that medicinal chemists would not
reasonably pursue, there is no denying that the possibilities
are vast. For difficult targets for which no small-molecule
inhibitor has ever been synthesized, a very wide range of
molecules may need to be explored to find the rare ones
that actually bind.
Another reason why it makes sense to screen smaller
molecules is the concept of ‘molecular complexity’,
which was first formalized by Hann and colleagues in
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cLogP
The logarithm of partition
coefficient between n‑octanol
and water. cLogP is a measure
of lipophilicity.
Chemotype
A chemical structure motif
or primary substructure
that is common to a group
of compounds.
Michael acceptors
An activated carbon–carbon
double bond that is susceptible
to nucleophilic attack.
2001 (REFS 9,10). This is the notion that, as molecules
become more complex (larger and with more features),
they have more possible interactions with a protein
target. These interactions can be thought of as any type
of contact, such as a single hydrogen bond or a hydrophobic contact. Molecules can havee unfavourable as
well as favourable interactions, which means that a molecule that is perfectly complementary to a protein might
have its interaction ruined by an unfortunately placed
methyl group, a risk that is more likely the larger and
more complicated a molecule becomes. Thus, small fragments, which form fewer interactions, should be able to
bind to a greater number of sites on a greater number of
proteins, leading to higher hit rates. Of course, because
each fragment has so few interactions, the binding affinities are likely to be lower than those for larger molecules,
requiring greater assay sensitivity.
Assuming that specific binding can be detected,
fragment screening provides several practical advantages. First, it is much easier to assemble, maintain, and
screen a library of a few thousand fragments than millions of larger molecules, thus opening lead discovery to
smaller companies and academic laboratories. Second,
the higher hit rates should enable more difficult targets
— such as protein–protein interactions — to be tackled.
Third, because fragments are small and typically soluble,
they are likely to have better pharmaceutical properties
and thus have the potential to produce superior drugs11–13.
Although some of these ideas had been discussed as far
back as the early 1980s14, most of them were not formalized until this century. Without methods to differentiate appropriate fragments from false positives and
improve their affinities, the approach remained largely
theoretical.
A major breakthrough was reported 20 years ago,
when Fesik and co-workers15 at the pharmaceutical
company Abbott reported solving both the detection
and hit‑to‑lead problems with a technique called ‘SAR
by NMR’, which stands for structure–activity relationships (SAR) by nuclear magnetic resonance (NMR).
Fragments were identified using 2D protein-detected
NMR spectroscopy — binding of a fragment to a protein
causes changes in the NMR spectrum that correspond
to specific amino acid residues in the protein. The technique is very sensitive and thus can be used to find weak
binders. Importantly, information about where on the
proteins the fragments bind can also be gleaned and is
useful for advancing the fragments to more potent molecules. In this and another early paper from the same
group16, two fragments were linked together to generate a molecule much more potent than either fragment
alone. More recently, as discussed below, other strategies
for advancing fragments have become mainstream.
Today FBDD is practiced widely in both industry
and academia. Dozens of drugs derived from this strategy have entered the clinic (TABLE 1). The topic has been
extensively reviewed in dozens of papers and eight books,
the most recent of which was published just this year 17.
With this in mind, the intention of this Review is not to
be comprehensive, but rather to provide a snapshot of
where the field stands, and where it is going.
Choosing fragments for a library
A crucial aspect of FBDD is the design of a fragment
library, and many good reviews have been written on this
topic18–20. Most libraries consist of molecules that adhere
to the ‘rule of three’ (REF. 21). This means that compounds have a molecular weight below 300 Da, fewer
than three hydrogen-bond donors and acceptors,
fewer than three rotatable bonds and a cLogP of three or
below 21. Of these criteria, the most important is probably
molecular weight — this upper limit on size can also
be measured by the number of non-hydrogen atoms,
which is typically less than 20. In fact, in recent years
there has been a trend towards even smaller fragments,
with some high-profile groups setting an upper limit of
17 heavy atoms22,23. By limiting the number of atoms,
other properties such as lipophilicity are also likely to
be lower, thus improving properties such as solubility in
water. However, some researchers include compounds
that slightly deviate from these criteria, such as those
compounds with substructures that are known to frequently bind to proteins24, including carboxylic acidand biphenyl-containing compounds or compounds
containing unique heterocycles.
The size of the fragment library can vary widely from
500 to 20,000 molecules7,25. In practice, most fragment
libraries consist of 1,000–5,000 compounds26. This generally provides a reasonable number of hits without
overwhelming screening methods (see below). However,
given sufficient resources, larger libraries can be useful.
Although a follow‑up screen to explore the SAR of the
hits obtained in a primary screen can be performed
when starting with a small library, there are particular
advantages of using larger libraries that contain multiple examples of each chemotype. By screening larger
libraries, initial SAR are often identified and provide
further validation of the hits. Additionally, in some
cases27, hits have been identified in a fragment-based
screen that would not have been found by screening a
smaller library that did not contain compounds beyond
the basic, undecorated heterocycles.
Other criteria that are important in the design of a
fragment library are the purity, stability and solubility
of the compounds. The purity of each compound should
be checked and the solubility should be tested. Relatively
high concentrations (>500 μM) are used in most primary
fragment-based screens, and even higher concentrations
are often needed in screens for secondary sites. Thus, the
compounds must be very soluble in aqueous solution.
Generally, solubility is easily measured alongside purity
assessment using NMR spectroscopy.
Perhaps the most important aspect in constructing a
fragment library is to avoid compounds known as ‘bad
actors’. These include nonspecific binders, reactive covalent modifiers, chelators or compounds that aggregate.
These traits mean that compounds can register as hits in
an assay without having specific binding affinities that
can be further enhanced. Some of these compounds are
relatively easy to recognize, such as those that contain
Michael acceptors, alkyl halides or epoxides. However,
other compounds are more difficult to recognize as bad
actors28. This is a major problem, especially when the
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Table 1 | FBDD-derived drugs that have entered the clinic
Drug*
Key companies
Targets
Status
Vemurafenib
Plexxikon
BRAF‑V600E
Approved
Structure (initial fragments are coloured§)
H
N
N
F
Cl
O
O
F
HN
S
O
Venetoclax
AbbVie, Genentech
BCL‑2
Approved
N
HN
Cl
O
O
N
N
N
H
O
O
NH
S
O
NO2
PLX3397
Plexxikon
FMS, KIT and
FLT3–ITD
Phase III
H
N
N
H
N
Merck
BACE1
N
N
Cl
Verubecestat
CF3
Phase III
O
O
O S
N
N
H
NH
N
F
NH
AZD3293
Astex
Pharmaceuticals,
AstraZeneca, Lilly
BACE1
Phase II/III
NH2
N
N
N
O
AT7519
Astex
CDK1, CDK2, CDK4,
CDK5 and CDK9
Phase II
Cl
Astex
Aurora and JAK2
Phase II‡
NH
N
NH
O
Cl
AT9283
H
N
O
H
N
O
N
H
NH
N
HN
AZD5363
AstraZeneca, Astex
AKT
Phase II
N
O
N
H
N
OH
H
N
H2N H
N
N
N
N
O
Cl
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Table 1 (cont.) | FBDD-derived drugs that have entered the clinic
Drug*
Key companies
Targets
Status
Erdafitinib
Johnson & Johnson,
Astex
FGFR1– FGFR4
Phase II
Structure (initial fragments are coloured§)
N
N
N
N
N
N
H
O
O
Indeglitazar
Plexxikon
PPAR agonist
Phase II‡
O
O
S
O
N
O
LY2886721
Lilly
BACE1
CO2H
Phase II‡
H
S
O
N
N H2
F
O
N
N
H
F
LY517717
Lilly, Protherics
FXA
Phase II
N
N
O
N
O
Navitoclax
AbbVie
BCL‑2 and BCL‑XL
H
N
N
H
Phase II
Cl
N
F
F
F
N
O
O
S
O
S
H
N
O
O
HN
S
N
O
NVP‑AUY922
Novartis, Vernalis
HSP90
Phase II
O
N
O
HO
O N
N
H
OH
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Table 1 (cont.) | FBDD-derived drugs that have entered the clinic
Drug*
Key companies
Targets
Status
Structure (initial fragments are coloured§)
Onalespib
Astex
HSP90
Phase II
OH
HO
N
N
N
ABL001
Novartis
BCR–ABL
Phase I
ABT‑518
AbbVie
MMP2 and MMP9
Phase I
Structure not in public domain
‡
O
F
AbbVie
BCL‑2 and BCL‑XL
O
O
O
F
Phase I‡
H
N
HO
OH
S
O
F
ABT‑737
O
O
Cl
N
N
H
N
O
O
S
S
O
NO2
ASTX660
Astex
XIAP and cIAP1
Phase I
AT13148
Astex
AKT, S6K1 and ROCK
Phase I
N
H
N
Structure not in public domain
Cl
HN
N
AZD3839
AstraZeneca, Astex
BACE1
Phase I‡
NH2
HO
N
N
N
CF2H
N
NH2
F
AZD5099
AstraZeneca
Bacterial
topoisomerase II
Phase I‡
H
N
O
O
HN
Cl
N
H
S
CO2H
N
Cl
O
BCL201
Vernalis, Servier,
Novartis
BCL‑2
Phase I
DG051
deCODE Genetics
LTA4H
Phase I‡
O
N
Structure not in public domain
O
Cl
O
N
O
OH
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Table 1 (cont.) | FBDD-derived drugs that have entered the clinic
Drug*
Key companies
Targets
Status
Structure (initial fragments are coloured§)
IC‑776
Lilly, ICOS
LFA1
Phase I‡
Structure not in public domain
LP‑261
Locus
Pharmaceuticals
Tubulin
Phase I‡
H
N
N
O
S
N
H
O
LY2811376
Lilly
BACE1
Phase I‡
O
O
N
S
N
N
F
PF‑06650833
Pfizer
IRAK4
NH2
F
Phase I
O
F
HN
O
O
N
H2N
O
PLX5568
Plexxikon
RAF
Phase I
‡
F
O
Cl
SGX Pharmaceuticals
BCR–ABL
F
N S
H O
N
H
N
F
F
N
H
N
SGX393
F
O
Phase I‡
O
N
N
H2N
N
SGX523
SGX Pharmaceuticals
MET
Phase I
‡
N
N N
N
S
N
N
SNS‑314
Sunesis
Pharmaceuticals
Aurora
N
Phase I‡
N
N
Cl
H
N
H
N
O
S
S
NH
N
BACE1, β-secretase 1; BCL‑2, B cell lymphoma 2; CDK1, cyclin-dependant kinase 1; cIAP1, cellular inhibitor of apoptosis protein 1; FBDD, fragment-based drug
discovery; FGFR1, fibroblast growth factor receptor 1; FLT3, FMS-like tyrosine kinase 3; FXA, factor Xa; HSP90, heat shock protein 90; IRAK4, interleukin‑1
receptor-associated kinase 4; ITD, internal tandem duplication; JAK2, Janus kinase 2; LFA1, lymphocyte function-associated antigen 1; LTA4H, leukotriene A4
hydrolase; MMP2, matrix metalloproteinase 2; PPAR, peroxisome proliferator-activated receptor; ROCK, RHO-associated protein kinase; S6K1, ribosomal protein
S6 kinase 1; XIAP, X-linked inhibitor of apoptosis protein. *Drugs are listed alphabetically within each phase. ‡Compounds for which development is not active or
might not still be active as determined by www.clinicaltrials.gov or company websites. §Where possible, elements of the initial fragments are coloured. In some
cases, the initial fragment hit has not been disclosed, and in other cases the medicinal chemistry sufficiently altered the structure of the molecule such that it is
difficult to discern the initial fragment.
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Surface plasmon resonance
(SPR). An assay that detects
binding between a surfaceimmobilized molecule (such as
a protein) and a molecule in
solution (such as a fragment).
Ligand-observed NMR
Detection of ligand binding by
nuclear magnetic resonance
(NMR) spectroscopy using
methods such as saturation
transfer difference (STD) NMR
(to measure transfer of
magnetization between protein
and ligand), T1ρ relaxation
(to exploit faster relaxation of
bound ligands), waterLOGSY
(to detect binding by transfer
of magnetization between
ligand and bound water) or
19
F T2 (to detect faster
relaxation of fluorinated
bound ligands).
ALARM
A nuclear magnetic resonance
(NMR)-based method to
detect false positives, such
as pan-assay interference
compounds (PAINS).
Pan-assay interference
compounds
(PAINS). Compounds
containing substructures that
give rise to apparent but
artefactual activity in assays.
The specific mechanisms vary
and are not always known,
but include forming covalent
adducts with the protein or
producing hydrogen peroxide.
Thermal shift assays
(TSAs). Assays, such as
differential scanning
fluorimetry, that measure the
denaturation (or melting)
temperature of a protein,
which is often increased in the
presence of a binding partner.
initial fragment screen uses mixtures of compounds.
For example, if 10% of a fragment library consists of
bad actors, and the library is screened in mixtures of 10,
every mixture is, on average, flawed. Computational
screens can be used to exclude the most obvious bad
actors when building a library. Sorting out other mis­
behaving molecules can be difficult, although fragments
can be run over a neutral sensor surface to ensure cleanliness in surface plasmon resonance (SPR) experiments,
or can be checked by ligand-observed NMR experiments
in the absence of protein to remove self-aggregators29.
In addition to compounds containing reactive groups,
several classes of molecules that have been described in
the literature as bad actors should also be removed from
screening libraries. These include compounds identified by an NMR method called ALARM30, the so‑called
pan-assay interference compounds (PAINS)31–33 and known
aggregators34,35. After removing these compounds, further work is still needed: fragment screens should be
conducted using the existing fragment library against
a panel of proteins to weed out the hits that occur in
all of these preliminary screens18. At that point, the
library should be relatively free of bad actors and ready
to use. Of course, as discussed below, caution is still warranted, as some proteins may be particularly sensitive to
nonspecific effects.
Knowing what types of compound to include in a
library can be as complex as knowing what to exclude.
In recent years, there have been initiatives to increase the
complexity of fragments. Proponents argue that fragment
libraries may contain too many planar fragments, and
that these would not be suitable to probe more ‘difficult’
drug targets. Some propose that fragments exhibiting
more ‘3D features’ have a higher probability of interacting with binding sites involved in protein–protein
interactions and a lower tendency toward promiscuous
binding 36. Although some of these initiatives may be
useful to increase the chemical diversity of fragment
libraries, other studies have found that higher hit rates
against more difficult targets are not achieved using this
approach23. On the contrary, hit rates would theoretically
be lower with libraries that contain more 3D fragments
because they will have relatively higher molecular weights
and increased molecular complexity. (Recall that Hann’s9
original molecular complexity framework predicts that as
the complexity of the fragment increases, the probability
of matching a binding site decreases). Therefore, initiatives directed at the generation of 3D fragments must
ensure that the balance between complexity, size and
diversity of the fragments is considered very carefully.
Other, more specialized fragment libraries include
libraries derived from fragments of natural products37,38
and libraries containing CF or CF3 groups that can be
conveniently screened by 19F NMR (see below)39.
One question that often arises is the extent to which
a library should be maximally diverse versus that to
which it should contain some analogues to provide initial SAR. On the one hand, given the vast number of
possibilities for even small fragments, an argument can
be made to try to cover as much as possible. On the other
hand, initial signs of SAR among related compounds
can be reassuring, and it might make sense to include
multiple examples of particularly tractable chemotypes.
Both approaches can work, and even the authors of this
Review differ in their strategies. In cases in which the
diversity of the screening library is maximized, it is prudent to have access to a secondary set of fragments for
rapid follow‑up studies.
‘Target-directed’ fragment libraries are generally not
necessary. According to molecular complexity theory
— and as further discussed below — fragments should
be universal, and the screening of targeted fragment
libraries reduces the chances to discover novel chemical
matter for a particular target. In one case, a fragment
library designed to target kinases did produce a higher
hit rate against kinases, but it also provided a higher hit
rate against other proteins40. However, for some proteases,
only targeted fragment libraries have led to successful
screens41,42.
Triumph of biophysics: fragment finding
An assay to screen the fragment library against the target
of interest is at the heart of any experimental FBDD
campaign. As any hit from the fragment library is likely
to bind to the target protein weakly, the assay has to be
sufficiently sensitive to detect weak interactions. It also
has to be robust in order to avoid false detection of fragment hits, which, for example, could be caused by interference with the assay readout. In practice, biophysical
assays have proven to satisfy the needs for sensitivity and
robustness. Among these biophysical assays, NMR spectroscopy, SPR and X‑ray crystallography belong to the
‘classical’ methods for fragment screening. More recent
developments, such as microscale thermophoresis,
thermal shift assays (TSAs) and weak affinity chromatography have the potential to develop into useful methods that
have a wide range of applications in fragment screening.
These assays are described in more detail below.
NMR spectroscopy was the first method successfully
used to screen fragments15, and it is still among the most
commonly used methods. NMR experiments for fragment screening can be divided into protein-observed
methods and ligand-observed methods43. In protein
observation, fragment binding is directly seen by chemical shift changes in an isotope-labelled target protein.
Ligand titration can provide the dissociation constant
(KD) of the interaction, and resonance assignment can
reveal the ligand binding site. Protein-observed NMR
can be considered the gold standard for fragment screening owing to its robustness and sensitivity. However,
this method requires large amounts of isotope-labelled
(15N and/or 13C) protein and is most useful for proteins
with molecular weights below 50 kDa. Ligand-observed
methods such as saturation transfer difference (STD),
T1ρ relaxation, waterLOGSY or 19F T2 experiments44
require less protein than protein-observed NMR experi­
ments do, they do not require isotope labels and they
have no upper size limits for the protein. However, the
methods are not quite as robust, and no information on
the ligand binding site is obtained. Hints about binding
mode can be obtained using quantitative STD NMR45, or
by ‘reporter screen’ NMR ligand observation, in which
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N
H
N
Lead fragment
IC50 (PIM1) >200 μM
N
H
N
F
Cl
O
F
O
HN
Vemurafenib
IC50 (BRAF-V600E) = 50 nM
S
O
Figure 1 | Discovery of vemurafenib. Vemurafenib
(bottom panel) is a marketed drug that was derived from
Nature
Reviews adopts
| Drug Discovery
a lead fragment (top panel).
The fragment
multiple
binding modes and, in fact, was originally discovered
owing to its activity against another kinase,
serine/threonine-protein kinase PIM1. Structure-based
optimization of the fragment led to a selective inhibitor of
BRAF‑V600E. IC50, half-maximal inhibitory concentration.
fragments are evaluated by their ability to displace a
weakly binding ‘reporter ligand’ or ‘spy’, which ideally
contains a 19F nucleus for 19F detection. 19F-based
reporter screening is very robust and sensitive and
requires relatively small amounts of unlabelled protein46.
This technique does demand a ligand that is known to
have specific interactions with the protein, which is not
always available.
SPR is the other most widely used technology for fragment screening 47. SPR requires immobilization of the target protein to a sensor chip, which is commonly achieved
by either direct immobilization through lysine residues
or by a streptavidin–biotin interaction using biotin-tagged
protein. Any fragment that binds to the protein increases
the mass near the sensor surface; this changes the refractive index of the interface, and the change is detected by
the SPR instrument. After developing methods to immobilize the target and testing that the immobilized protein
remains functional, SPR experiments require only minute amounts of protein, as the immobilized protein can
typically be used to test dozens or even hundreds of ligands. In addition, SPR is a quantitative method and can
yield KD values from dose–response curves, assuming that
ligand solubility is much higher than the KD. In practice,
fragment KD values are often in the same range as their
solubilities, so the titration curve cannot be fully determined and responses from specific interactions at high
concentrations are difficult to deconvolute from nonspecific interactions. Active-site mutants are commonly
used to distinguish specific from nonspecific ligands,
an approach that works well in the search for active-site
inhibitors. However, allosteric ligands can be mistaken
for nonspecific inhibitors in such a screening format.
Overall however, the quantitative nature of SPR makes
it an ideal method to investigate analogues of fragment
hits to determine their SAR and their binding kinetics48.
X‑ray crystallography is generally viewed as a method
for structure determination, but it can also be used for
fragment screening 49. This requires a robust crystallization system, usually a soaking system, and it works best
with exceptionally soluble fragments. Crystallographic
screening has the great advantage that a hit comes
directly with a crystal structure, typically a boon in efforts
to advance fragments50. There can be issues wherein the
high concentrations (sometimes >100 mM) of compounds used to soak crystals can give compounds that
bind so weakly that their affinity is not detectable with
any method other than crystallography. However, this
may be a strength rather than a weakness of this method,
as once these hits are elaborated they are often detected
using other techniques, which underlines the sensitivity
of crystallography as a screening method51. Of course, it
must be remembered that the protein is in a crystalline
lattice and not in a physiological environment, which
may give rise to artefactual binding. However, fragment
screening by X‑ray crystallography has yielded a number of success stories52, and some companies, such as
Astex Pharmaceuticals, have been founded around this
concept. Furthermore, although crystallographic screening can and generally does result in false negatives, it is
very effective at avoiding false positives — if you observe
electron density, then the compound has bound in an
ordered manner. One disadvantage is that the throughput
is generally lower than that for other methods, although
the introduction of automation in crystal handling and
electron density software analysis has improved this
issue. Still, throughput concerns make it less commonly
used as a primary screen unless the organization has been
specifically set up for this purpose53.
TSAs, such as differential scanning fluorimetry (DSF),
have recently gained widespread attention54. In contrast
to the methods previously discussed, this method is fast
and cheap. In principle, an ordinary thermal cycler can
be used, and with plate-based systems, hundreds or even
thousands of compounds can be screened in a day. In
DSF, the thermal unfolding (‘melting’) of proteins is
monitored by the addition of a fluorescent dye that binds
to hydrophobic patches on the protein that are exposed
as it denatures. Ligand binding can thermally stabilize
proteins, so a shift in the melting temperature indicates
a hit. For some series of compounds, the extent of thermal stabilization correlates with ligand affinity. However,
both the binding affinity and the thermodynamics of protein unfolding determine the melting temperature of a
protein in the presence of a ligand55, and so not all proteins
are amenable to the approach, and both false positives
and false negatives are common.
Other recent developments for fragment screening
include microscale thermophoresis and weak affinity
chromatography. Thermophoresis is the physical pheno­
menon of compound migration in a temperature gradient.
Some proteins move towards higher temperature and
some move towards lower temperature, and this migration is monitored by fluorescent detection. Ligand
binding changes migration behaviour, for example, by
altering the charge or hydration shell, and can therefore
be detected by microscale thermophoresis56. For weak
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affinity chromatography, proteins are immobilized in a
similar manner to SPR, and compounds are flowed over
the array. However, rather than recording the change
in refractive index, this set-up measures differences in
compound retention time caused by interactions with
immobilized protein57.
Thus, a variety of fragment-finding methods exist,
and many have been extensively tested and refined. This
poses the question of which to choose. Given the risks
of false positives and false negatives with each individual
method, the sensible strategy is to identify an optimal
combination or cascade. One approach is to combine
two or more screening techniques and follow up only
on common hits. This stringent approach reduces the
risk of finding false positives in the common hit list but
increases the risk of false negatives. In fact, some reports
show that the overlap of hits determined by different
methods can be disturbingly low 57–60. Although low overlap can be partially explained by the difference between
‘screening mode’ and ‘validation mode’, using overlap to
guide triage essentially gives veto power to the least sensitive method. An alternative strategy is to select a primary method that has been shown to work well for the
particular target of interest and rely on this method until
the compounds reach an affinity that allows them to be
detected by other methods. Primary methods include:
NMR spectroscopy, especially for proteins that can be
isotopically labelled; SPR, especially if tool compounds
are available that prove functionality of the immobilized proteins; or another robust method that has been
validated for the target of interest.
Understanding protein–fragment interactions
The fundamental motivation for using fragments lies
in their ability to sample chemical space efficiently.
At one level, proteins seem to present extremely complex surfaces for small molecules to bind to, but this
view depends on the molecular complexity of the probe
molecules. If the probe is sufficiently small, so that only
one or two interactions are required to bind, the apparent complexity of the protein is greatly reduced. Hence,
a simple fragment with a limited number of potential
interactions is relatively unconstrained when exploring
the protein surface as it only needs to make one or two
highly efficient interactions in order to bind22. Even
protein–protein interaction surfaces can be viewed,
from the perspective of a fragment, as being similar to
substrate-binding pockets in a protein. Consider the
ordered binding of water (perhaps the most efficient
of fragments) to protein surfaces and pockets. Similar
reductions in molecular complexity using fragment
screening have allowed the routine detection of ‘hotspots’ and novel pockets on various proteins61, and has
also yielded relatively high hit rates. Fragments with
10–15 heavy atoms, and therefore limited numbers of
functional groups, have been shown to make well-defined
interactions in protein binding sites. Despite binding
with affinities in the millimolar range, the quality of the
interactions between protein and fragment is typically
high, as reflected in the conservation of the binding
mode as the fragments are grown into larger molecules62.
NH
N
Lead fragment
IC50 (AKT1) >100 μM
HN
N
N
NH
N
Modified compound
IC50 (AKT1) = 6.9 μM
R
O
NH2
N
H
Cl
N
N
N
NH
Compounds modified for specificity
R = H; IC50 (AKT1) = 13 nM
IC50 (ROCK2) = 65 nM
IC50 (hERG) = 5 μM
R = C2H4OH; AZD5363
IC50 (AKT1) = 3 nM
IC50 (ROCK2) = 60 nM
IC50 (hERG) >65 μM
Figure 2 | Discovery of AZD5363. Development of the
Nature
Reviews
| Drug
Discovery
clinical-stage AKT inhibitor
AZD5363
started
with
a screen that identified the same fragment that led to the
development of vemurafenib. Structure-based design
was originally conducted using a mutagenized version
of protein kinase A. Extensive analysis of the structure–
activity relationships (SAR) was necessary in order to
achieve good selectivity against other kinases (such as
RHO-associated protein kinase 2 (ROCK2)), reduce binding
to potassium voltage-gated channel subfamily H member 2
(hERG) and improve oral bioavailability. IC50, half-maximal
inhibitory concentration.
Because fragments often bind so weakly, deciding what
counts as a hit can be difficult. Indeed, fragments often
show no activity in functional assays. To help to prioritize
fragments, the concept of ligand efficiency was introduced, which is defined simply as the free energy of binding of a ligand divided by the number of non-­hydrogen
atoms it has63,64. In general, fragments with a ligand efficiency of more than 0.3 kcal mol−1 atom−1 are prioritized,
although obviously factors such as chemical tractability,
SAR and availability of a binding model are important.
There have been several reports that some fragments
exhibit clear selectivity, such as in kinase panels when
targeting the ATP-binding site65. These observations
imply that initial recognition of fragments by highly
related proteins remains sensitive to small differences
in the binding pockets.
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H
N
H
N
N
N
N
CH3
CF3
Fragment from SPR screen
KD = 16 μM
Fragment from SPR screen
KD = 5.6 μM
Optimized compound
Ki = 0.07 μM
Cyanopindolol
Ki < 1 nM
Figure 3 | Discovery of new ligand chemotypes for the β1 adrenergic receptor.
The two fragments in the top panels were identified from
a surface
plasmon
Nature
Reviews
| Drug resonance
Discovery
(SPR) screen, and they bind to the β1 adrenergic receptor. The binding modes of these
fragments were modelled on the crystal structure of cyanopindolol (bottom left panel;
Protein Data Bank identifier (PDB ID): 2VT4) bound to the β1 adrenergic receptor.
This information was used to guide the design of the optimized compound (bottom
right panel PDB ID: 3ZPQ). KD, dissociation constant; Ki, inhibitory constant.
Even fragments that do not display selectivity for
a particular protein can still lead to selective binders.
Examples of both selective and also nonselective fragments being developed into highly selective inhibitors
have been reported for kinases66. Likewise, fragments
that are planar in nature are routinely developed into
lead compounds with substantial 3D features23. In both
cases, careful analysis of the orientation and potential
growth vectors emanating from the fragment is required
to build additional interactions during the hit‑to‑lead
and lead-optimization processes. Such analysis greatly
benefits from the availability of multiple protein–ligand
crystal structures as the fragment is elaborated. These
structures often show that the original binding orientation of the initial fragment is conserved during the
fragment-growing process62. Even though there are
relatively few interactions between a fragment and the
protein, these interactions are often very ligand efficient;
such interactions must be very thermodynamically
favourable to offset the rotational and translational freedoms lost in a binding event. Indeed, fragment binding
is generally considered to be driven by enthalpy that can
overcome loss of rigid-body entropy.
Recently, the concept of the ‘minimal pharmacophore’
has been introduced in an attempt to define and categorize the functional complexity exhibited by a fragment, and hence guide the next steps in the evolution of
FBDD67. According to this strategy, no more than two
functional groups should be represented within a single
fragment. Fragments should contain a balance of lipophilic groups and hydrogen-bond donors or acceptors
but, according to molecular complexity theory, limiting
the functionality ensures that the resulting fragment
library is able to sample chemical space efficiently.
It is tempting to consider whether a screening collection
could represent all possible arrangements of functional
groups that could bind to a protein target and that fall
within the molecular weight limits for conventional
fragment libraries. If so, such a fragment library would
have a good hit rate across a range of proteins. Indeed,
this would be consistent with the theoretical proposition
that fragments are (and should be) nonselective. This
notion is supported by the fact that approximately 50%
of the fragments in the Astex libraries have been seen
to bind productively to more than one protein target
(M. L. Verdonk, personal communication). This ‘promiscuity’ has also been observed by others working in the
kinase field65,68.
However, fragments that exhibit substantial promiscuity and seem to bind to multiple proteins through an
unclear mechanism should be regarded with suspicion69.
Several fragments, notably the 2‑aminothiazoles, fall
into this category. Halogenated fragments have also been
reported to exhibit promiscuous binding, but in these
cases the binding seems to be dominated by the halogen,
with few productive interactions among the rest of the
fragment atoms70. Although these halogenated fragments
may serve as interesting molecular probes, especially to
induce protein conformational changes, they may not be
ideal starting points for hit‑to‑lead chemistry.
A concept similar to minimal pharmacophores in
fragments is the notion of ‘minimal binding motifs’
in proteins. All proteins are made from the same building
blocks, amino acids, arranged in diverse constellations
to give rise to complex molecular structures. However,
by fragmenting their molecular architecture, one can
reduce complex surfaces to simpler binding motifs. This
conceptual framework suggests why fragments are often
found bound to multiple sites on proteins. Indeed, the
ability of fragments to identify additional or ‘secondary
sites’ on proteins is becoming well established, and in
some cases, multiple sites on a single protein have clearly
been identified71. Some of these secondary sites have been
shown to have a functional (sometimes allosteric) role;
however, most of these sites have not been characterized
extensively. More generally, it has been shown that proteins typically contain at least two pockets that have the
capacity to bind to fragments, and even this is likely to be
a conservative estimate61.
The intrinsic binding energy of a protein pocket probably determines whether the binding mode of a fragment
will be conserved during the fragment growing process72.
Therefore, it is possible that secondary sites may be less
likely to maintain the original binding modes of the
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a
O
b
OH
OH
F
KD = 0.3 mM
KD = 4 mM
c
NO2
H
N
O
O
O
N
H
N
O
S
O
HN
N
N
Cl
ABT-199 (Venetoclax)
Figure 4 | Discovery of the B cell lymphoma 2‑selective candidate ABT‑199.
Fragments (parts a and b) that bound to different sites on B cell lymphoma 2 (BCL‑2)
Nature Reviews
Drug Discovery
were identified by protein-observed nuclear magnetic resonance
(NMR)| spectroscopy.
Subsequent structure-guided optimization (through compounds ABT‑737 and ABT‑263
(also known as navitoclax)) led to the selective BCL‑2 candidate ABT‑199 (also known as
venetoclax; part c), which has been approved for some forms of chronic lymphocytic
leukaemia. KD, dissociation constant.
fragments in elaborated compounds. Nevertheless, the
ability of fragments to uncover these sometimes cryptic
binding sites may enable the development of novel drug
molecules for difficult protein–protein targets and even
for well-established therapeutic targets73,74.
Applications of fragments
Bearing in mind the lessons above, finding fragments is
relatively straightforward75–77. The challenge is in evolving
the fragments to molecules ready for lead optimization
by structure-guided or more conventional medicinal
chem­istry. Three main strategies have been used: fragment growing, in which atoms are added to the fragment;
fragment merging, in which features of the fragment
and other hits are combined; and fragment linking, in
which two fragments that bind to distinct sites are linked
together. The distinction between merging and linking
can be arbitrary, particularly as it is often necessary to
modify one or both fragments to accommodate the linker.
The first practical successes in FBDD firmly associated
the approach with fragment linking. In theory, joining two
fragments can boost the affinity of the resulting molecule
substantially over the sum of the initial fragments14. Some
of the earliest SAR by NMR papers demonstrated this16,
and model systems have shown affinity enhancements
of as much as 500‑fold78. However, in practice, fragment linking can be challenging, because slight length
or geometric deficiencies in the linker can have a dramatically negative effect on binding 79. Moreover, linked
compounds are often larger than ideal. Thus, although
linking can be effective, particularly for more challenging
targets with extended binding sites80, fragment growing
tends to be more common.
In most of the success stories published to date
(including those in the clinic; see TABLE 1), a robust model
of how the fragment binds was required to define where
(and sometimes how) to grow, merge or link the fragment (or fragments), usually from structures determined
by X‑ray crystallography or NMR spectroscopy. It is difficult to overstate the role that structural information has
in advancing fragments; some organizations will not even
pursue fragments unless structures can be determined.
Nonetheless, modelling is now getting to the point at
which it can be used to advance fragments in the absence
of empirical structures81, and there are even examples of
advancing fragments solely on the basis of activity 82.
A veritable plethora of examples shows that chemistry
has an early, central role. Once a fragment hit is obtained
and characterized structurally, the next step is to begin
to optimize it. Whether or not the fragment library is
maximally diverse or contains multiple analogues of the
hit, initial optimization can pay dividends later. In some
cases, optimization can be accomplished by purchasing
commercially available analogues (or scouring the
screening deck in a larger organization), but ultimately,
chemists will need to make compounds to address specific questions, such as the utility of adding a hydrogenbond acceptor or filling a small lipophilic pocket. This
step can often be a bottleneck, and there have been
recent calls to incorporate synthetic tractability up front
in library design83. The required interplay between biophysics and chemistry can be highly productive, but it
does require organizational will to bring together teams
of disparate scientists at an early stage in a project.
Getting to leads faster or better. In several cases, FBDD
has accelerated the identification of clinical candidates. A high-profile example is the identification of
vemurafenib, an inhibitor of mutant BRAF kinase and
the first fragment-derived drug on the market (FIG. 1).
This project exploited a chemical library and a structural biology programme that was focused on kinases.
An initial, ligand-efficient fragment was identified
by a biochemical screen against the serine/threonineprotein kinase PIM1. Hit optimization used the crystal
structures of compounds bound to PIM1, and then of
compounds bound to the kinase domain of fibroblast
growth factor receptor 1 (FGFR1) as a surrogate, until
more-potent compounds were successfully crystallized
in BRAF, ultimately leading to the clinical candidate,
which is approved to treat melanoma84.
The speed of progression from hit to candidate for
vemurafenib was exceptional — about 6 years from
project initiation to approval by the US Food and Drug
Association (FDA). Despite early enthusiastic claims,
fragment-based methods are not necessarily quicker
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b
NH2
Cl
a
Cl
N
H
Cl
Cl
HN
O
OH
c
N
HN
O
H
N
O
S
H
N
S
N
Figure 5 | Fragments binding to RAS. Three independent studies identified fragments
that bind to sites on RAS. This schematic shows the secondary structure of RAS (with
α‑helices in red and β‑sheets in blue) bound to guanosine
diphosphate
in grey)
Nature
Reviews | (shown
Drug Discovery
(Protein Data Bank identifier (PDB ID): 4EPY). a | A molecule (shown in yellow) was
designed to covalently tether to an oncogenic RAS‑G12C mutant (PDB ID: 4LYF) through
the sulfur atom of Cys12 (bonding indicated by the arrow). b,c | Small fragments,
discovered using nuclear magnetic resonance (NMR) screening, bind to similar sites on
RAS (green fragment, PDB ID: 4DST; purple fragment, PDB ID: 4EPY).
than optimization of a HTS hit for conventional targets
such as kinases. However, the fragment screen usually
identifies new chemotypes, which provides opportunities for selectivity and intellectual property. If optimized
with careful consideration of drug-like properties, a fragment hit results in a smaller, more efficient compound
that may have fewer issues in the clinic12. An example
that demonstrates this advantage is the AstraZeneca
project on the kinase AKT (also known as protein
kinase B (PKB))85, which exploited leads from an initial fragment-based collaboration with Astex 86. The key
issues with the original lead were that it also inhibited
potassium voltage-gated channel subfamily H member 2
(hERG) — which is a known drug safety liability — and
lack of selectivity over a closely related kinase (RHOassociated protein kinase 2 (ROCK2)). Modifications
generated a compound with the desired profile (FIG. 2)
that is now in clinical trials against solid tumours as
AZD5363. This is another example wherein detailed
structure-based design used crystal structures of a surrogate; in this case, difficulties obtaining AKT structures
led researchers to work with structures of a related protein, protein kinase A (PKA), with four key mutations.
It is also notable that both the BRAF and the AKT project
began with the same azaindole fragment, demonstrating
that very different intellectual property can arise from the
same starting point.
G protein-coupled receptors (GPCRs) are another
major class of therapeutic target in which fragments
are starting to play a part. For many GPCRs, the natural ligand has a low molecular weight, and thus, there
have been some successes screening fragment libraries
directly in functional assays to find hits with sufficient
activity to be optimized by conventional medicinal
chemistry 87,88. An issue with this approach (and fragment
screening of purified membrane proteins in general) is
nonspecific binding to the lipid or detergent required to
stabilize and maintain the function of the protein. The
recent success in stabilizing GPCRs has generated proteins that are suitable for biophysical study, including
fragment screening 89,90. SPR has also been applied to
GPCRs: a screen against the β1 adrenergic receptor identified a number of fragments91 (FIG. 3). Computational
docking of these fragments into the crystal structure of
a known drug bound to the receptor suggested modifications that produced promising new lead series as well as
insights into the structural basis of binding and hypotheses on features to tune agonist and antagonist behaviour.
This is an active area of research, and it seems likely that
membrane proteins will increasingly be amenable to
fragment-based methods92,93.
Finding tractable hits when HTS fails. One of the
distinctive successes of FBDD is that it has identified
tractable hits against targets for which HTS has failed.
The most striking examples are for inhibitors of protein–
protein interactions, wherein the binding sites are often
fairly flat and initial hit compounds bind weakly. NMR
spectroscopy has been the most successful method for
identifying such weak-binding compounds, and it has
been possible to find fragments that bind to most sites
on most proteins. Such interactions are sometimes difficult to capture using X-ray crystallography, but NMR
methods can guide fragment optimization.
An early success involved compounds that bind to
members of the anti-apoptotic BCL‑2 family, which are
often overexpressed in cancer cells. Fragments identified by protein-observed NMR that bind to two distinct
regions of BCL-XL were linked together, leading to a dual
BCL‑2 and BCL‑XL inhibitor 94. Subsequent optimization
(FIG. 4) gave the BCL‑2 selective venetoclax, which gained
the FDA’s ‘breakthrough therapy’ designation for recalcitrant chronic lymphocytic leukaemia95 and was approved
in the United States in April 2016. Fragments have also
driven progress in generating inhibitors of induced
myeloid leukaemia cell differentiation protein (MCL1),
another high-profile member of the BCL‑2 family that is
overexpressed in many different tumours27,96,97.
Advancing chemical biology — unusual mechanisms
and binding sites. Fragment screening has identified
new ways to modify protein behaviour and explore
biology. One notable success here has been in identifying
possible routes to inhibit KRAS, a small GTPase whose
deregulation has long been recognized as a major driver
for many forms of cancer. Multiple teams have used fragment methods to identify various sites where small molecules can bind98–101 (FIG. 5), and some of these have been
optimized to cell-active molecules102. Two other fascinating examples are the use of fragment methods to identify
enzyme activators, which suggest methods for improving or altering the activity of industrial enzymes103, and
the discovery of a fragment that binds between two
functional domains of the protease and helicase protein
in Hepatitis C virus. This fragment was subsequently
optimized to a potent inhibitor 73 (FIG. 6). This example
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a
Moreover, FBDD does not necessarily have to be a
stand-alone technique. Indeed, one highly productive
endeavour is to use information from fragment screens to
optimize leads discovered using other approaches, such as
HTS. This ‘fragment-assisted drug discovery’ is becoming
increasingly common, particularly at larger organizations
that run multiple approaches in parallel107. A nice example of this was reported by researchers from Boehringer
Ingelheim, in which a polar fragment was used to replace
a more lipophilic moiety to improve the physicochemical
and pharmacokinetic properties of the lead108.
b
H2N
O
Fragment from screen
c
NH2
HN
O
Cl
HN
O
F
Optimized fragment
Figure 6 | Discovery of a novel binding site in Hepatitis C virus NS3. A crystallographic
fragment screen identified a fragment (part a) that bound in a previously unrecognized
cleft of the Hepatitis C virus (HCV) protease/helicase NS3 (part b), between the
Nature Reviews | Drug Discovery
helicase (blue) and protease (red) domains (Protein Data Bank identifier: 4B6F).
Subsequent optimization gave a compound (part c) that locked the domains together,
inhibiting activity.
again demonstrates the potential of fragment screening
as a powerful tool in chemical biology to discover new
ligand-binding sites. Once alternate sites are known,
the fragment screen can be configured to identify compounds that bind there, such as in the imaginative use of
NMR methods to discover allosteric modulators of the
oncogenic fusion protein BCR–ABL104.
Conclusions and perspectives
Changing the mindset: the impact of FBDD on other
hit-finding approaches. The previous sections have documented the impact that FBDD has had as a method for
lead generation. But the impact of FBDD goes further:
the fundamental principles of FBDD have changed the
strategies of drug discovery researchers and have therefore influenced drug discovery beyond fragment-based
approaches105. Basic principles of FBDD include emphasis on highly quality-controlled chemical libraries, biophysical validation of protein–ligand interactions, hit
optimization aided by structural biology and hit assessment by ligand efficiency 22,64. All of these principles have
gained widespread use in other approaches to drug discovery and are applied in hit-finding campaigns beyond
fragments, such as HTS. Large companies are cleaning
up their compound archives, biochemical HTS hits are
being validated and characterized by biophysical methods such as NMR, SPR or DSF before a chemistry project
is initiated106, structural information on protein–ligand
interactions has become a key driver for lead optimization, and molecular weight and lipophilicity are being
viewed as key criteria to select hits for chemical optimization. This change in mindset is not solely a consequence of FBDD, but fragments — with their focus
on quality and validation — have left their footprints
in this area.
The next 20 years: remaining challenges. Three distinct
areas can be identified for improvements in the methods
that underpin FBDD. The first is the content of fragment
libraries. Experiences over the past 10 years in particular
have underscored the importance of quality control and
of ensuring that fragments have suitable physicochemical properties, such as solubility and avoidance of interfering readouts (such as aggregation), for the conditions
under which the screening experiments are conducted.
It is also important to recognize the advantage of keeping
the compounds small enough to sample large tracts of
chemical space. Although there is a continuing debate
about how important 3D character is for the core scaffold of fragments, most fragment libraries reflect available compounds and a medicinal chemistry prejudice
that favours flat heterocycles. Perhaps some binding
sites (such as those for carbohydrate recognition or
processing) would be better satisfied by more‑3D compounds, but so far there are not enough data to answer
the question109.
The second area for improvement of methods is the
technology available for monitoring fragment binding.
A sometimes bewildering array of technologies has
emerged, with most requiring labelling of the protein
in some way — either with an instrument (as for biosensors) or with labels (such as techniques that detect
changes in fluorescence). Each technique requires skilled
operators, and it is all too easy for novices to make errors
in the experiments or interpretation. There is still scope
for new biophysical techniques that can reliably detect
the binding of a fragment to a protein with 1 mM affinity,
use very small amounts of protein, and for which detection
can be set up for a wide range of proteins with minimal
specialist preparation.
The final technical challenge for fragment discovery
is knowing how to optimize fragments to leads. As previously discussed, structural information has been vital in
identifying the optimal vectors for fragment evolution, as
most changes will result in negative or flat SAR which are
difficult to interpret. One recent innovation that may help
is off-rate screening 110. Most improvements in affinity of
compound binding, particularly in the early stages of
optimization, come from slowing the dissociation of the
protein–compound complex. This can be measured relatively quickly using SPR, and as it is a first order process,
it does not require purification or knowledge of the concentration of the compound. This means that the reaction
mixture itself can be tested, and resource-intensive steps
to purify compounds or to make and test solutions of
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known concentration can be avoided. This speed allows
the synthetically accessible vectors on a fragment to be
explored rapidly and cheaply — perhaps accelerating the
optimization of fragment hits in the absence of structure.
The cultural and organizational changes needed to
integrate fragment-based approaches alongside other
hit-finding techniques (such as HTS) have, at times,
been more difficult than the technical challenges.
Historically, it was difficult to convince an experienced
medicinal chemist to start with a fragment that binds at
a concentration of 500 μM instead of trying to improve
the properties of a sub-micromolar hit from a HTS
screen. Fortunately, the growing list of compounds in
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is helping to change this reluctance. Another
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Acknowledgements
The Authors thank U. Schopfer and the anonymous reviewers
for helpful comments and suggestions on the manuscript.
Competing interests statement
The authors declare competing interests: see Web version
for details.
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