Mining Public Domain Data to Develop Selective DYRK1A Inhibitors

Article abstract of DOI:10.1021/acsmedchemlett.0c00279

Kinases represent one of the most intensively pursued groups of targets in modern-day drug discovery. Often it is desirable to achieve selective inhibition of the kinase of interest over the remaining ～500 kinases in the human kinome. This is especially true when inhibitors are intended to be used to study the biology of the target of interest. We present a pipeline of open-source software that analyzes public domain data to repurpose compounds that have been used in previous kinase inhibitor development projects. We define the dual-specificity tyrosine-regulated kinase 1A (DYRK1A) as the kinase of interest, and by addition of a single methyl group to the chosen starting point we remove glycogen synthase kinase β (GSK3β) and cyclin-dependent kinase (CDK) inhibition. Thus, in an efficient manner we repurpose a GSK3β/CDK chemotype to deliver 8b, a highly selective DYRK1A inhibitor.

Full text of DOI:10.1021/acsmedchemlett.0c00279

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Letter
Mining public domain data to develop selective DYRK1A inhibitors
Scott H Henderson, Fiona Sorrell, James Bennett, Marcus T Hanley, Sean
Robinson, Iva Hopkins Navratilova, Jonathan M Elkins, and Simon E Ward
ACS Med. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acsmedchemlett.0c00279 • Publication Date (Web): 30 Jun 2020
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Mining public domain data to develop selective DYRK1A inhibitors
Scott H. Henderson†,*, Fiona Sorrell#, James Bennett§, Marcus T. Hanley‡, Sean Robinsonǁ, Iva Hopkins
ǁ,≠
#,
‡,

Navratilova , Jonathan M. Elkins , Simon E. Ward *.
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†Sussex Drug Discovery Centre, University of Sussex, Brighton, BN1 9RH, U.K.
#Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, U.K.
§Target Discovery Institute, University of Oxford, Oxford, OX3 7FZ, UK.
ǁExscientia, The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, UK.
≠University of Dundee, Dow Street, Dundee, DD1 5EH, U.K.
Structural Genomics Consortium, Universidade Estadual de Campinas, Cidade Universitária Zeferino Vaz, Av. Dr. André
Tosello, 550, Barão Geraldo, Campinas, SP 13083-886, Brazil.
‡Medicines Discovery Institute, Cardiff University, CF10 3AT, U.K.
ABSTRACT: Kinases represent one of the most intensively pursued groups of targets in modern-day drug discovery. Often it is
desirable to achieve selective inhibition of the kinase of interest over the remaining ~500 kinases in the human kinome. This is
especially true when inhibitors are intended to be used to study the biology of the target of interest. We present a pipeline of open-
source software that analyzes public domain data to repurpose compounds that have been used in previous kinase inhibitor
development projects. We define the dual-specificity tyrosine-regulated kinase 1A (DYRK1A) as the kinase of interest, and by
addition of a single methyl group to the chosen starting point we remove glycogen synthase kinase β (GSK3β) and cyclin-dependent
kinase (CDK) inhibition. Thus, in an efficient manner we repurpose a GSK3β/CDK chemotype to deliver 8b, a highly selective
DYRK1A inhibitor.
KEYWORDS. DYRK1A, polypharmacology, chemoinformatics, selectivity.
Introduction
well as the screening data derived from the Published Kinase
Inhibitor Sets 1 and 2 (PKIS/PKIS2).7,8,9 Many of these
compound sets are comprised of molecules in the realms of
drug-like space, originating from pharmaceutical industry drug
discovery programs. These compounds and associated data
provide opportunities for repurposing kinase inhibitors for
targets of current interest.
The ChEMBL database10 holds records for over 1.3 million
compounds, with around 10 million compound bioactivity
measurements derived from published structure-activity
relationship studies and compound library screening studies. An
effective data-mining strategy is essential to utilise this
information. The Konstanz Information Miner (KNIME®) is an
open source data analytics platform that can be used to mine
ChEMBL using pre-made workflows.11 ChEMBL KNIME®
nodes and the NN-Activity Pairs workflow were developed by
workers at ChEMBL-EBI to highlight the polypharmacology
profiles of similar compounds within a cluster (nearest
neighbours). Differences in the polypharmacology profiles of
nearest neighbours can be interrogated to identify chemical
transformations that lead to better selectivity. The principles of
In recent years clinical successes have demonstrated the large
potential of kinase inhibitors to become approved drugs.1
However, achieving the desired selectivity profile remains a
significant challenge. A number of general methods to gain
selectivity have been documented, such as targeting the non-
conserved, inactive DFG-out (Type II) conformation of the
kinase,2 allosteric-site directed (Type III) inhibition3 or
compounds that access a non-conserved pocket behind the
gatekeeper residue (Type I1/2).4 Thus far, the majority of FDA-
approved kinase inhibitors are found to bind in a conventional
Type I, ATP competitive mode.
Kinase profiling data contains both on- and off-target data for
compounds often from the same chemical class, derived from
the same data source. Routine assessment of off-target activity
facilitates the design of a desired selectivity profile, in order to
minimize off-target-related side effects. With the availability of
large kinase screening panels various large scale sets of
homogenous screening data for many compounds against many
kinases have been reported. These include significant kinase
datasets from Anastassiadis et al.,5 Davis et al.,2 Metz et al.,6 as
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the NN-Activity Pairs workflow is shown in Figure 1 (a
schematic of the workflow is available in supplementary
information Figure SI-1).
DYRK1A inhibitors is the cyclin-dependent kinases (CDKs).32
Despite the availability of structural data, it has been difficult to
gain DYRK1A selectivity against CLKs, GSK3β and the
CDKs, while optimizing DYRK1A inhibitory activity.
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Search nearest
neighbours (NN)
in ChEMBL
Input chemotype
of interest
Rank and Filter
NN
O
H
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N
Cl
O
HN
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Cl
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Cl
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Purvalanol A
SB-216763
Leucettine L41
Retrieve kinase
bioactivity data
for NN
Analyse activity
cliffs
H2
N
N
N
N
NH2
Cl
Cl
HN
N
OH
O
NH
S
N
Br
N
N
H
N
N
H
Figure 1. Principles of workflow used to mine kinase profiling data.
The workflow was implemented in KNIME® using the ‘NN
Activity Pairs’ workflow to mine ChEMBL.
N
Meridianins
Meriolin
EHT 5372
Figure 2. Known DYRK1A inhibitors that also inhibit CMGC
kinases, including GSK3s and CDKs.
In the case where the binding mode of the series is known, the
position of the chemical transformation that has led to an
improvement in selectivity can be valuable in the design of
selective inhibitors. We illustrate the value of this approach in
the context of a target of current interest, dual-specificity
tyrosine-regulated kinase 1A (DYRK1A). We successfully
remove two key off-targets by utilizing the KNIME® workflow.
Results and Discussion
Data mining identified a pyrazolo[1,5-b]pyridazine starting
point
Inspection of publicly available kinase profiling data led to the
identification of a cluster of kinase inhibitors based on the
pyrazolo[1,5-b]pyridazine core with the potential to become a
novel and drug-like DYRK1A template.7-9 The pyrazolo[1,5-
b]pyridazines were attractive starting points in terms of drug-
like properties, DYRK1A binding affinity and kinome-wide
selectivity. However, the series originated from former
GSK3β33 and CDK inhibitor programs.34 Selectively removing
inhibition of these two kinases, while maintaining DYRK1A
binding affinity and the overall kinome-wide selectivity profile,
was envisaged as a way to afford a selective DYRK1A tool
compound.
The NN-Activity Pairs KNIME® workflow was used to mine
ChEMBL to highlight the polypharmacology profiles of nearest
neighbours. Inspection of the Kinase Activity Profile histogram
(supplementary information, Figure SI-2) for the pyrazolo[1,5-
b]pyridazines revealed a number of matched-molecular pairs
(MMPs) and chemical transformations that led to significant
decreases in inhibitory activity against GSK3β and CDK2
(Figure 3).
DYRK1A is of interest for inhibitor development due to its
purported roles in the development and progression of
neurodegenerative disorders such as Alzheimer’s Disease
(AD),12 Huntington’s Disease (HD) and Parkinson’s Disease
(PD).13,14,15, The DYRK1A gene is located on the Down’s
16
syndrome critical region of chromosome 21, consequently,
individuals with Down’s syndrome express elevated levels of
DYRK1A protein.17 Microduplication of the DYRK1A gene
resulted in dysmorphic and intellectual features characteristic of
a Down’s syndrome phenotype, supporting the hypothesis that
DYRK1A represents a novel target for the treatment of Down’s
syndrome.18 Increased tau phosphorylation is observed in
,
trisomy19 20 and Down’s syndrome is associated with early-
onset AD, underscoring a genetic link between DYRK1A, AD
,22,
,
,
23 24 25
and Down’s syndrome.21
Several DYRK1A inhibitors have been developed and
summarized in recent reviews.12,26 Although many are reported
as potent DYRK1A inhibitors, the majority of inhibitors have
not had their kinome-wide selectivity profiles reported; those
that have inhibit other members of the CMGC group27 of
kinases. Given the lack of reported DYRK1A inhibitors
progressing into and beyond clinical trials, there remains a need
to develop high quality chemical matter which can be used for
detailed biological study.28 Investigation of neurodevelopment
or neurodegeneration requires a CNS penetrant inhibitor,
capable of exerting an on-target effect in vivo. Two of the most
advanced DYRK1A inhibitors so far published are L4129 and
EHT 5372,30 which exhibit good potency and kinome-wide
selectivity. However, both possess potent off-target activity at
other CMGC kinases. L41 inhibits CLKs and GSK3β,29 a kinase
for which DYRK1A serves as a priming kinase, and which
regulates many of the same signalling pathways as DYRK1A.31
Another CMGC kinase family that is often inhibited by
ChEMBL359554 (6a)
ChEMBL360866 (6b)
ChEMBL366259 (7a)
N
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H
N
H
CH3
CH3
GSK3
pIC50 > 7
pIC50 < 5
pIC50 < 5
CDK2
CDK4
pIC50 > 7
pIC50 > 7
pIC50 < 5
-
-
-
Figure 3. Activity cliffs identified rapidly from NN-Activity Pairs
KNIME® workflow. Data from ChEMBL.10
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N
N
N
N
N
N
N
Chemistry
N
N
An advantage of using publicly available profiling data as a
starting point for inhibitor discovery is that information
regarding synthetic routes has usually already been published.
Following the published procedure,33,34 the aminated pyridazine
2 was formed in excellent yield after reaction of pyridazine 1
with hydroxylamine-O-sulfonic acid (HOSA). Subsequent
[3+2] cycloaddition between 2 and butyne-2-one afforded the
pyrazolo[1,5-b]pyridazine 3 in average yield. DMF/DMA
condensation furnished the corresponding enamine 4 in
excellent yield. Conversion of 4 to the corresponding
pyrimidine was slow under conventional heating at elevated
temperatures, taking up to 48 hours for the formation of the
desired product to occur (Scheme 1).
(i)
(iii)
O
N
N
Cl
NH
(ii)
N
N
N
R
4
5
Scheme 3. Reagents and conditions: (i) urea, sodium, 140 °C,
10 min, 91%; (ii) phosphorus(V)oxychloride, 110 °C, 3 h, 92%;
(iii) conditions A: anilines, 2-propanol, 150 °C, 2-16 h, 4-44%;
conditions B: anilines, 2-propanol, 150 °C, 15-20 min,
microwave, 43-60%.
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Two approaches were taken to install the N-methyl substituent
onto the aminopyrimidine. Nucleophilic aromatic substitution
of 5 with N-methylbenzylamine afforded 6b in 44% yield.
Alternatively, N-methylation with sodium hydride and
iodomethane was carried out on late-stage analogues, 8a and
10a to access 8b and 10b respectively (Scheme 4).
I
NH2
N
N
N
N
N
(i)
(ii)
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N
O
1
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3
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(i)
(iii)
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N
H
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N
R = Me 8b 68%
OMe 10b 64%
(iv)
O
N
Scheme 4. Reagents and conditions: (i) sodium hydride,
iodomethane, DMF, rt, 2 h.
N
N
R
N
H
4
Scheme 1. Reagents and conditions: (i) hydroxylamine-O-
sulfonic acid, KHCO3, KI, H2O, 70 °C, 1.5 h, 94%; (ii) butyne-
2-one, KOH, CH2Cl2, rt, 16 h, 60%; (iii) DMF-DMA, 100 °C,
16 h, 91%; (iv) phenyl guanidines, 2-methoxyethanol, 110 °C,
16-48 h, 13-37%.
Inhibitor Optimization
The MMPs with the most complete dataset were 6a and 6b
(Figure 3). The data suggested that N-methylation of the
aminopyrimidine N-H reduced the inhibitory activity at both
GSK3β and CDK2 of the pyrazolo[1,5-b]pyridazine series by 2
log units. Pyrazolo[1,5-b]pyridazine analogues that were
inactive against GSK3β and CDKs were of great interest as
DYRK1A inhibitors. N-methylation was an attractive strategy
to pursue for gaining selectivity as the number of hydrogen-
bond donors in the series was reduced simultaneously, which
could improve permeability and CNS penetration of the series.
One disadvantage of the literature synthesis was that the
preparation of each analogue required the respective phenyl
guanidine to be synthesised first and in most cases the synthesis
of the phenyl guanidine was low yielding. Phenyl guanidines
were prepared using concentrated nitric acid and cyanamide
from the corresponding aniline (Scheme 2).33,34
To confirm that N-methylation afforded selectivity against the
original series off-targets, GSK3β and CDK2, 6a and 6b were
profiled in a radiometric kinase assay (33PanQinase® Activity
Assay) provided by Proqinase GMBH. The assay measured the
kinase activity of DYRK1A, GSK3β and CDK2 in the presence
and absence of inhibitors. Gratifyingly, the selectivity profile
predicted in KNIME® was confirmed experimentally, with 6b
displaying selective inhibition of DYRK1A, whereas 6a
inhibited all three CMGC kinases.
(i)
NH
R
R
H2
N
H2
N
N
HNO3
H
Scheme 2. Reagents and conditions: (i) cyanamide (50 wt.% in
H2O), HNO3, EtOH, 100 °C, 16-48 h, 35-62%.
In an effort to improve the versatility of the synthesis, the
chloropyrimidine intermediate 5 was prepared. Nucleophilic
aromatic substitution with a range of nucleophiles was then
possible. Scale-up of 5 was completed in high yield from 4
(Scheme 3).
Table 1: CMGC Selectivity of 6a and 6b at 1 µM
Compound
DYRK1Aa
GSK3βa
CDK2a
6a
6b
97
79
76
98
13
33
a% inhibition in 33PanQinase® Activity Assay (n=1)
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Several pyrazolo[1,5-b]pyridazines were profiled against
Table 3. DYRK1A Binding Affinity of N-methylated
Pyrazolo[1,5-b]pyridazines
1
2
3
4
5
6
7
8
DYRK1A in a binding-displacement assay (Table 2). In
general, analogues possessing meta-substituents on the aniline
ring exhibited stronger binding affinity for DYRK1A. Both
electron-donating (8a and 10a) and electron-withdrawing (11a)
substituents were well-tolerated in the meta-position.
Analogues with ortho and para substituents on the aniline ring
exhibited weaker DYRK1A binding affinity - 7a and 9a
exhibited approximately 10 fold weaker binding affinity than
meta analogues 8a and 10a, whilst 12a displayed 20 fold
weaker binding affinity for DYRK1A than meta analogue 11a.
N
N
N
N
R
N
N
Me
Compound
6b
R
DYRK1Aa
186
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11
12
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8b
76
97
Table 2. DYRK1A Binding Affinity of Pyrazolo[1,5-
b]pyridazines
O
10b
N
N
N
N
a16-point IC50 (nM) in TR-FRET-based ligand-binding
displacement assay (n=1)
R
N
N
H
Compound
6a
R
DYRK1Aa
38
Despite a reduction in DYRK1A binding affinity by more than
5 fold for all N-methylated MMPs, the incorporation of an N-
methyl group was tolerated surprisingly well in DYRK1A. 8b
and 10b both maintained high levels of DYRK1A binding
affinity (IC50 < 100 nM). The binding affinities of MMPs 8a
and 8b were confirmed by surface plasmon resonance (SPR)
measurements, which gave a KD ± SEM values of 52 ± 4.5 nM
for 8b and 8.9 ± 2.1 nM for 8a (both n = 2), in good agreement
with the binding displacement assay (sensograms are available
in supplementary information Figure SI-3).
7a
8a
64
7
O
9a
137
12
O
10a
The broader kinome selectivity of 8b was determined by the
KINOMEscan assay panel (DiscoverX). Percentage inhibition
data for CMGC kinases regularly inhibited by DYRK1A
inhibitors reported in the literature are summarised in Table 4
(the full dataset is available in the supplementary information).
N
11a
12a
5
N
103
Table 4. KINOMEscan Selectivity Profiling of 8b
Kinase
Compound 8b
a16-point IC50 (nM) in TR-FRET-based ligand-binding
displacement assay (n=1)
DYRK1A
DYRK1B
DYRK2
CLK1
65
0
19
59
59
6
Ortho and para-substituted analogues were deprioritized as
they possessed weaker DYRK1A binding affinity. The N-
methylated matched pairs of 6a, 8a and 10a were synthesised
and assayed for DYRK1A binding affinity (Table 3).
CLK2
CLK3
CDK2
12
4
GSK3β
S Score (40)
0.01
a % inhibition at 1 µM inhibitor concentration determined in
competition binding assay (DiscoverX, n=1).
8b exhibited good kinome-wide selectivity, with a Selectivity
Score (S-Score (40)) of 0.01. The successful removal of CDK2
and GSK3β activity with the addition of the N-methyl group
was consistent with previous results and selectivity against
DYRK1B, DYRK2 and CLK3 was also observed. The intrinsic
solubility of 8b was determined by the ‘shaking flask’ method
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in PBS buffer at pH 6.8.35 8b was also profiled for metabolic
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stability (HLM, RLM). These results are displayed in Table 5.
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3
4
5
6
Table 5: Metabolic Stability and Solubility of 8b
Compound
8b
HLM
RLM
Solubility
0.01
75.0 ± 3.1
805.3 ± 21.6
7
8
9
HLM and RLM (µL/min/mg) determinations mean of n = 2.
HLM = human liver microsomes; RLM = rat liver microsomes;
thermodynamic solubility data (mg/mL) derived from single
experiment at pH 6.8.
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Figure 4. Co-crystal structure of 6b bound to DYRK1A in Type
I fashion, with N-methyl group orientated towards the hinge of
DYRK1A and Hydrogen-bonding interactions are depicted
between the ligand and DYRK1A.
8b exhibited a high rate of metabolic turnover in rat liver
microsomes, which would need to be taken into consideration
for rodent in vivo studies. The solubility of 8b was not optimal,
but was comparable to other DYRK1A tool compounds
currently in use. Given the favourable selectivity profile and
strong binding affinity for DYRK1A, 8b was profiled in a
MDCK-MDR1 assay to assess whether there were any P-gp or
permeability issues with the series.
6b bound in a monodentate fashion to the hinge of DYRK1A.
The pyridine-type nitrogen of the pyrazolo portion of the
pyrazolo[1,5-b]pyridazine motif supports a long-range water
network at the back of the kinase pocket. The non-bridging
nitrogen belonging to the pyridazine portion of the
pyrazolo[1,5-b]pyridazine motif serves as a hydrogen-bond
acceptor (HBA) to the catalytic lysine (Lys188), further
stabilizing 6b in the ATP-site. The hinge-binding motif,
bearing an N-methyl substituent appears to occupy a small
lipophilic pocket present at the hinge of DYRK1A.
Table 6. MDCK-MDR1 Permeability of 8b
Direction = Direction =
A2B
B2A
Comp CNS
ound
Mean Papp (10-6 cms-1)b
Efflux
Ratio
(Mean Papp
B2A /
MPOa
score
6b and 8b differ only by the addition of a meta-methyl group to
the phenyl moiety in 8b, and therefore the crystal structure with
6b serves as a good model for the binding of 8b. The lack of
literature precedent for the hinge-binding motif and the high-
degree of kinome selectivity exhibited by 8b (S-Score (40) =
0.01), suggests that this pocket may not be present in the vast
majority of other kinases. Targeting this pocket appears to be a
valid approach to gaining selectivity for DYRK1A.
Mean Papp
A2B)b
8b
4.8
20.9 ± 0.10 20.1 ± 0.90 0.96
aCNS MPO score calculated using CNS MPO KNIME®
b
workflow with ChemAxon nodes. Data generated by Cyprotex
in MDCK-MDR1 assay. Permeability coefficient (Papp
)
calculated across cells in direction: A2B (Apical to Basolateral)
and B2A (Basolateral to Apical). Determinations ± standard
deviation (mean of n =2).
Conclusion
While structure-based drug design (SBDD) is a proven method
for designing selective inhibitors, it remains the case that often,
chemical modifications that yield selectivity are found
serendipitously and the rationale for compound selectivity can
remain unknown. Profiling data already in the public domain
can serve as a repository for chemical transformations that can
lead to improved selectivity. Data mining tools such as
KNIME® can be utilized to extract this information from open-
source databases. The method presented here can enable the
design of kinase inhibitors with improved selectivity versus off-
targets. This strategy serves as a complimentary method to
SBDD, helping to rationalize compound selectivity.
The high CNS MPO score36 calculated for 8b correlated with
good levels of permeability and low P-gp efflux, indicating that
8b, and analogues derived from the pyrazolo[1,5-b]pyridazine
series, stand a good chance of delivering compounds capable of
penetrating the blood-brain-barrier (BBB) and exerting an on-
target effect in vivo.
The co-crystal structure of 6b bound to DYRK1A (PDB 6S1I)
shows that despite possessing an N-methyl group close to the
hinge of DYRK1A, 6b is able to adopt a Type I binding mode
(Figure 4).
The example of DYRK1A, presented here, shows that a
chemical modification, which may have been overlooked if
ligand data was not already available, can lead to more
selectivity against off-targets. A scaffold which was originally
developed as a CDK2/4 / GSK3β inhibitor has been repurposed
into a selective DYRK1A inhibitor using a chemoinformatics
approach. Favourable selectivity against DYRK1B and CLK3
has also been achieved in an efficient manner. This novel class
of DYRK1A inhibitors can serve as a basis for future design of
CNS-penetrant in vivo optimized molecules.
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ABBREVIATIONS
Page 6 of 8
ASSOCIATED CONTENT
Supporting Information
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BBB: blood-brain barrier; CDK: cyclin dependent kinase; CLK:
CDC-like kinases; CMGC: Including cyclin-dependent kinases
(CDKs), mitogen-activated protein kinases (MAP kinases),
glycogen synthase kinases (GSK) and CDK-like kinases; CNS
MPO: Central nervous system multi-parameter optimization;
DYRK: Dual-specificity tyrosine phosphorylation-regulated
kinase; GSK: Glycogen synthase kinases; HBA: hydrogen bond
acceptor; HLM: human liver microsomes; MDCK-MDR1: Madin-
Darby canine kidney cells transfected with the human MDR1 gene;
MMP: matched-molecular pair; PBS: Phosphate-buffered saline;
PDB: Protein databank; PKIS: published kinase inhibitor set;
RLM: rat liver microsomes; SBDD: structure-based drug design.
The Supporting Information is available free of charge on the ACS
Publications website.
Synthetic chemistry, characterization of compounds, KNIME®
workflow, compound SMILES, protein expression, purification,
crystallisation, data collection and structure determination (.PDF)
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DisoverX KINOMEscan of 8b (.xlsx)
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AUTHOR INFORMATION
Corresponding Author
* Scott H. Henderson; Sussex Drug Discovery Centre, University
of Sussex, Brighton, BN1 9RH, U.K.
* Simon E. Ward; Medicines Discovery Institute, Cardiff
University, CF10 3AT, U.K. Email: WardS10@cardiff.ac.uk
Present Addresses
† Scott H. Henderson. Warren Center for Neuroscience Drug
Discovery, Vanderbilt University, Cool Springs, Tennessee,
37067, United States. Email: scott.h.henderson@vanderbilt.edu
Author Contributions
S.H.H. was involved in the conception and design, acquisition of
data, analysis and interpretation of data, and drafting and revising
of the article. F.J.S. was involved in the conception and design,
acquisition of data, analysis and interpretation of data, and drafting
and revising of the article. J.M.B. was involved in the conception
and design, acquisition of data, analysis and interpretation of data.
M.T.H was involved in the acquisition of data. S.R. was involved
in the acquisition of data. I.H.N was involved in the acquisition of
data. J.M.E. was involved in the conception of the project, analysis
of data and revising of the article. S.E.W. was involved in the
conception of the project and revising of the article. All authors
approved the final version to be published.
Funding Sources
S.H.H. is funded by the BBSRC (BB/L017105/1); S.E.W. is funded
by the Wellcome Trust, MRC, BBSRC and the European Structural
Funding via Sêr Cymru scheme. The SGC is a registered charity
(number 1097737) that receives funds from AbbVie, Bayer Pharma
AG, Boehringer Ingelheim, Canada Foundation for Innovation,
Eshelman Institute for Innovation, Genome Canada, Innovative
Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766],
Janssen, Merck KGaA Darmstadt Germany, MSD, Novartis
Pharma AG, Ontario Ministry of Economic Development and
Innovation, Pfizer, São Paulo Research Foundation-FAPESP,
Takeda, and Wellcome [106169/ZZ14/Z].
ACKNOWLEDGMENTS
We thank Kamal R. Abdul Azeez and Ana Clara Redondo for help
with protein purification. An academic license for ChemAxon
(https://www.chemaxon.com) and
a license for ChemAxon
Infocom KNIME nodes is gratefully acknowledged. We would
also like to acknowledge the platform KNIME®
(https://www.knime.com) and the ChEMBL group at EMBL-EBI
for donating the NN-Activity Pairs KNIME workflow to the
KNIME community.
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