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The Role of Chemoinformatics in the Design of a Comprehensive Drug Discovery Screening Collection
Edgar Jacoby
Center for Proteomic Chemistry
2nd Strasbourg Summer School on ChemoinformaticsVVF Obernai, France, 20-24 June 2010
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| Obernai| 20.06.10 | Edgar Jacoby
NIBR compound collection enhancement project
External compound selection process
Combinatorial library synthesis – scaffold projects
Compilation of annotated knowledge-based sets
Molecular Information System for pharmaceutical ligands
Knowledge-based ligand design and virtual screening strategies
Chemoinformatics: Quo Vadis?
Overview
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Essential Properties of Compounds
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Essential Properties of Compounds - Continued
Jacoby et al. Chemogenomics 2007, 1-38
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| Obernai| 20.06.10 | Edgar Jacoby
LDCStructuresDBOracle and ISIS databases in place
10.000.000+ Unique structures
13.000.000+ Catalogue entries
130+ Collections
50+ Vendors
LDCScaffoldDB Oracle and ISIS databases
18.000+ Unique scaffolds
15 Vendors
Externally Available Chemistry Space
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| Obernai| 20.06.10 | Edgar Jacoby
Chemistry, Biology, Informatics Based Compound Selection
2. Completeselection by with compounds from container 2:(average structures)average density, similarity <= 0.88
descriptor space
3. Fi ll gaps with compounds from container 3 (penalized structures):lower density, similarity <= 0.80
descriptor space
0. The existing archive
descriptor space
1. Select from container 1:(target family related affinity)high density, similarity <= 0.95
descriptor space
2. Completeselection by with compounds from container 2:(average structures)average density, similarity <= 0.88
descriptor space
3. Fi ll gaps with compounds from container 3 (penalized structures):lower density, similarity <= 0.80
descriptor space
0. The existing archive
descriptor space
1. Select from container 1:(target family related affinity)high density, similarity <= 0.95
descriptor spaceSimilar to Know MDDRActives and Drugs
PenalizedDiverse Drug Like
Jacoby et al. Curr Top Med Chem. 2005, 5, 397-411
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| Obernai| 20.06.10 | Edgar Jacoby
ID Structure Chemical Description
33 A
N
O carbonic acid nitriles Y Y N
34 A
OPPOO
* OPSOO
**OSSOO
* *
PO
OO
**S
O
OO
**O
OO
* *
O PO
O PO
OOO
not:
acyclic acid anhydrides including phoshonates and phosphinates, but not di- and triphospates
Y Y N
35 B
O PO
O PO
OOO
PO
OO
**
OPPOO
*
SO
OO
**
OPSOO
**
O
OO
* *
OSSOO
* *
not:
cyclic acid anhydrides including phoshonates and phosphinates, but not di- and triphospates
Y Y N
36 C O
NO
H
H acyclic alkyl carbamates Y N N
Definition of the Exclusion-Criteria:A: Don't store in MCSB: Don't produce as solution or store in SOLARC: Don' buy (but take for free)
Toxic ReactiveUnintersting
Archive Exclusion FlagUninteresting
Substructure/Fragment Filters
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| Obernai| 20.06.10 | Edgar Jacoby
In Silico Substructure Tox Alerts
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| Obernai| 20.06.10 | Edgar Jacoby
Y
Confirm availability and price
N
Sample/catalogue entry domain Structure domain
registered in corporate structure DB
not registered in corporate structure DB
Compound received?N
Register compounds (registered)
Y
Catalogue entry marked as selected NY
Div
ersi
ty
Order placed (ordered)
Decide to order
Don’t order (cancelled)
NY
Load Catalogue, add new structures (new), check availability for all compounds
Compound is not available
YN
Catalogue entry marked as rejected
Y
Identity by structure (smiles)
Structures processed in parallel acquisition processes
Structures of “archivecompounds”
N
Export new structures for evaluation (exported) Structure based evaluation of compoundsDrug-Likeness and Target Focus?Diversity?
Y
Confirm availability and price
N
Sample/catalogue entry domain Structure domain
registered in corporate structure DB
not registered in corporate structure DB
Compound received?N
Register compounds (registered)
Y
Catalogue entry marked as selected NY
Div
ersi
ty
Order placed (ordered)
Decide to order
Don’t order (cancelled)
NY
Load Catalogue, add new structures (new), check availability for all compounds
Compound is not available
YN
Catalogue entry marked as rejected
Y
Identity by structure (smiles)
Structures processed in parallel acquisition processes
Structures of “archivecompounds”
N
Export new structures for evaluation (exported) Structure based evaluation of compoundsDrug-Likeness and Target Focus?Diversity?
Cheminformatics Platform for Selection and Logistics
Schuffenhauer, A. et al. Comb. Chem. High Throughput Screen. (2004), 7, 771-782.
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| Obernai| 20.06.10 | Edgar Jacoby
Schuffenhauer et al. J Chem Inf Model. 2007, 47, 47-58.
Scaffold Tree Example for HTS ResultsPubChem Pyruvate Kinase Data Set
Color intensity by fraction of actives
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| Obernai| 20.06.10 | Edgar Jacoby
Library Evaluation
Pat
enta
bilit
y
Nov
elty
of S
caffo
ld
Nov
elty
of P
rodu
cts
Mod
ular
ity
Pla
nned
Siz
e
Rat
io o
f Low
est a
nd
Hig
hest
Nr.
of B
Bs
Com
plem
enta
rity
Rul
e of
Fiv
e
InS
ilico
Tox
BB
Ava
ilabi
lity
BB
Div
ersi
ty
Chi
ral P
urity
HT
Sol
ubili
ty
HT
logP
HT
Per
mea
bilit
y
CY
P 4
50 (M
etab
olis
m
HT
HE
RG
cha
nnal
as
say
Am
es te
st
Eas
e of
Rea
lisat
ion
Tim
e of
Dev
elop
men
t
Che
mic
al S
tabi
lity
1 1 1 0 0 -1 1 1 1 1 1 1 0 0 0 0 0 -1 0 1 1
Prototype CriteriaPreprototype Criteria
-1
0
1
Evaluation Scheme for Combinatorial Hit Discovery Libraries
noveltytargetfocus
syntheticcomplexity
potentialfor HLO
computedproperties
2004
2007
Jacoby et al. Curr Top Med Chem. 2005, 5, 397-411
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| Obernai| 20.06.10 | Edgar Jacoby
The Ideal Size of Combinatorial Libraries
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| Obernai| 20.06.10 | Edgar Jacoby
AlogP Distribution
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
AlogP
% o
f sam
ples CHC [%]
SOLAR [%]CMedC [%]
Molecular Weight Distribution
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
0 100 200 300 400 500 600 700 800 900 1000
Molecular Weight
% o
f sam
ples
CHC [%]SOLAR [%]CMedC [%]
H-Bond Donors
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
0 1 2 3 4 5 6 7 8 9 10
Count
% o
f sam
ples
CHC [%]SOLAR [%]CMedC [%]
H-Bond Acceptors
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Count
% o
f sam
ples
CHC [%]SOLAR [%]CMedC [%]
Monitoring of Compound Collections - Lipinski Profiles
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0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0 2 4 6 8 10
AlogP
-logS
n (ex
p, e
quili
briu
m)
logS vs AlogP, no saltlogS vs AlogP, saltGSE Limit -logS >= logP + 0.5Limit -logS >= logP
Prediction of Water Solubility
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
-logSpH6.8(QMPRPlus, Corina 3D structure)
-logS
pH6.
8(ex
p,eq
uilib
rium
)
inside model rangeoutside model rangesaltsexact-1 log unit+1 log unit
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10-logSn (QikProp, Corina 3D structures)
-logS
n (ex
p, e
quili
briu
m)
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
-logSpH6.8(calculated,ACD)
-logS
pH6.
8(ex
p, e
quili
briu
m)
non_saltsaltexact+1 log unit-1 log unit
14
126127
128129
130131
2132
136133
134
135137
138139
76
10810990
140
112142
141
142145143
84147
144148
85149
118150
146
151
152154153
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| Obernai| 20.06.10 | Edgar Jacoby
The “Egan Egg”: Statistical Prospectively Useful Model
-20 0 20 40 60 80 100 120 140 160 180 200
-2
0
2
4
6
8
HigherPermeability
HigherSolubility
Poor Solubility
Poor Permeability& Poor Solubility Few marketed drugs lie
in this region
Poor Permeability Drugs in this region usually work via prodrugs of active transport
Good Hunting Grounds
Clo
gP
PSA WJ Egan et al, JMC, 2000
Access Egan Egg via IsisDrawInSilico Profile T2ABS plot
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| Obernai| 20.06.10 | Edgar Jacoby
Chemist Triage Score and Naïve Bayesian Models
Yes/No decision from five individual triaging exercises taken to build one Bayesian models
All five models are used in a consensus score, fraction of models voting against the molecules
Chemist Triage Score
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MCS SLIA ICLF PSEB MCSexternal
2003
MCSexternal
2004
Drugs NAPR HTS Hits2004
Random
Frac
tion
of c
ompo
unds
Series6Series5Series4Series3Series2Series1
1
0.8
0.6
0.4
0.2
0
ChemistTriageScore
Chemist Triage Score
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MCS SLIA ICLF PSEB MCSexternal
2003
MCSexternal
2004
Drugs NAPR HTS Hits2004
Random
Frac
tion
of c
ompo
unds
Series6Series5Series4Series3Series2Series1
1
0.8
0.6
0.4
0.2
0
ChemistTriageScore
Histogram of NB Score
0%
2%
4%
6%
8%
10%
12%
14%
16%
-100 -80 -60 -40 -20 0 20 40 60 80 100%
of s
ampl
es
Training Set
Screening Collection
Primary Hit-list
Less attractive cpds.
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| Obernai| 20.06.10 | Edgar Jacoby
Examples of Unwanted Molecules
NH2
NH
O
NH
O
NH
O
N+N
H
O
N+
O OH
NH
Cl
Cl
Cl
Cl N
O
Br
F
F
F
N
N+
O
O
1.0
0.8 0.8
S
OHOH
OH
O
O
F FF
ClCl
NHN
ON
OH
OH
O
0.6 0.6
0.4
OH
NH
OHNH
OH
OH
NH
O
OHO
OH
F
N
O
NON
S
0.0012
ChemistTriageScore: Excluded
ChemistTriageScore: Penalized
Similog density: Excluded
Similog density: Penalized
0.0017
0.0029
0.0036
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| Obernai| 20.06.10 | Edgar Jacoby
The Challenge of Quality in Candidate Optimization
Biller S. et al. Biotechnology: Pharmaceutical Aspects, 2004, 413-429.
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| Obernai| 20.06.10 | Edgar Jacoby
Safety Pharmacology Prediction
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| Obernai| 20.06.10 | Edgar Jacoby
Which Annotated Compound Libraries Make Sense ?
1. Approved drugs – FDA orange book - SOSA
2. Other reference compounds (USAN, INN) with known bioactivity (Tocris, Prestwick, etc.)
3. Compounds processed by profiling
4. Primary metabolites, natural products and derivatives (KEGG, etc.)
5. Target family focused libraries
6. Diversity selection – DOS / BIOS
7. Protein mimetics library: β-turn/α-helix mimetics
8. Fragment library for NMR/Xray screening
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| Obernai| 20.06.10 | Edgar Jacoby
Wess DDT 2002, 7, 533-535
Focusing : Optimizing the Overlap Between Chemical and Biological Spaces
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Annotation ?No
YesKnownStructure ?
NoYes
Is binding site ‘Drug
like’ ?
No
Yes
Ligand-based
Yes No
Similaritybased list
Known Actives ?
Yes Select compounds
similar to known actives
Are Actives
‘Drug like’ ?
Yes
No
Structure-based
TARGET Select
compounds compatible to binding site
Structure based list
Diversity screening
Knowledge-Based Virtual Screening
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| Obernai| 20.06.10 | Edgar Jacoby
Molecular Information System for Pharmaceutical Ligands of the Main Target Families
Target Compound Selections MIS
Ligand -Target Sequence
Ligand -Target Classification
Ligand - Interaction Classification
Ligand - Functionality
Reference Ligands
LigandsNovartisMDDR,...
TargetsSwiss-Prot
GPCRDB,...
Schuffenhauer, A. and Jacoby, E. BioSilico ( 2004), 2, 190-200
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| Obernai| 20.06.10 | Edgar Jacoby
Databases of Known Pharmaceutical Ligands and Drugs
Lit & PatentBiologyIdentificationModelStructure
Structure
NN
NNN
O
O
O
Chiral185987
Generic nameValsartan < Rec INN; USAN >
Extreg
Preview
TrademarkValpression (Menarini, IT)Provas (Schwarz, DE)Varexan (Egis, HU)Di (N ti CA DE
185987Pref#
Launched
Act.invest?
SourceNovartis
Company.codeCGP-48933CGP-63170 (comb. with
MDDR-3D 2001.1(23.01)
Phase
Index Activity28000 Cardiotonic31000 Antihypertensive31432 Angiotensin II
AT1 Antagonist
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| Obernai| 20.06.10 | Edgar Jacoby
Knowledge Based Approaches : GPCR IMDL Patent Database
Activity Based Search
38 Patent Families
1810 Reference Structures
Similarity Search Naïve Bayes Model
542 SOLAR in silico hits
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| Obernai| 20.06.10 | Edgar Jacoby
Similarity Searching using Similog Keys
1NN 3NN avg cent
P. Floersheim’s Similog keyscounts of DABE triplets in 2D space
O
OO
H
0010-4-1100-6-0100-6-
4
6
6
0010
0100
1100
Schuffenhauer, A. et al. J. Chem. Inf. Comp. Sci. ( 2003), 2, 391-405.
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NH
N
Cl
N
N
O
SNH
O O
N
Cl
O
O
N
NH
N
NN
Cl
N
OO
OH
HCl
NH
N
N
O
SNH
O O
N
Cl
NNH
SNH
N
OO
Cl
ON
O
NH
N O
FO
OH
N
OOH
NH
OHN
OH
NH2
O
NH
N
NS
NH
N
N
ClCl
NH
NH2
OH
N
N
N
O
N
O
Propranololβ antago.
8-OH-DPAT5HT1A part. ago.
Serotonin5HT ago.
Ketanserin5HT2A antago.
Janssen_1D4 antago.
Kissei_1D2 antago.
RO-16814β ago.
5HT7 antago.pKB = 7.25
Known Reference Architectures Novel Prototypes
WAY-1006355HT1A antago.
Design of TAM Libraries for Monoamine-Related GPCRs
Jacoby, E. Quant. Struct. Activity. Relations. (2001) 20, 115-123.
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CAS DB
Deconvolute
Recompose
Databases of Active Fragments
NOVARTIS DB
New Leads + New Affinity Profiles
Deconvolute and recombine : Drug like molecules
Currently applied knowledge-based strategy
Jacoby et al. Drug News Perspect. (2003),16, 93-102.
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Ligand selectivity in a subfamily with conserved molecular recognition
Do we understand and master the principles of profile composition in terms of molecular recognition ?
α2A α2B α2C β1 β2 D2.L D3 D4.2 5HT1A 5HT1D 5HT1E 5HT1F 5HT2A H1
Sumatriptan < 6 < 6 < 6 < 6 < 6 < 6 < 6 < 6 6.4 8.4 < 6 7.8 < 6
Alniditan 7.4 6.8 7.7 < 6 < 6 < 6 6.5 7.1 8.4 9.4 6.6 6.4 < 6 < 6
Haloperidol < 6 6 .3 6.2 < 6 < 6 8.7 8.1 7.9 < 6 6.6 < 6 < 6 6.7 6.1
Pipamperone 6.1 7.5 6.5 < 6 < 6 6.9 6.6 8.3 < 6 6.8 < 6 < 6 8.3 < 6
Clozapine 7.3 7.7 8.1 < 6 < 6 6.7 6.6 7.4 6.9 6.4 6.4 6.9 8.0 9.6
Olanzapine 6.3 6.7 6.7 < 6 < 6 7.5 7.3 7.6 < 6 6.2 < 6 6.5 8.6 9.2
pKi
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0
1
2
3
4
5
60
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9 1
Maximum Tanimoto Index
% C
ompo
unds
Focus of TAM library – Similarity Histograms
Monoamine GPCRs
Peptide-binding GPCRs
LGIC
R1N
R1
R2
'
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Okram et al. Chemistry & Biology 2006, 13, 779–786
A general strategy for creating “inactive” conformation kinase inhibitors - ABL
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0%
20%
40%
60%
80%
100%
BCG-001
BET-001
BET-02A
DIA-002
IMO-00
1
AAL-001
BET-02B
HOP-002
HOP-003
DIA-001
PUR-003
SYK-001
AAL-002
BZI-001
GUA-003
IMI-2
51
OXI-001
SRC-001
TAM-003
IAB-451
IPO-825
PUR-001
TAM-001
TAM-002
TSA-003
TAM-004
TAM-005
TSA-001
GPCRKinaseProteaseother EnzymesNURion channelPPIfunctional
Is Target Family Focus a Myth ?
Selectivity Analysis of Combinatorial Libraries
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Extend Knowledge-based Approaches
Target family based approaches
BIOS – Protein domain based designs
Privileged Scaffolds
Protein Secondary Structure Mimetics: α-helix/β-turn
Co-factor mimetics: ATP, NADP, FAD, Co-A, SAM, etc.
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Privileged structures / Scaffold target matrix
NH
O
N
ON
O
N
NH
O
N
N
Cl
N
N
O
O
N
N
N
O
NO
N
OOH
O
NN
N NH
NO
NH
O
NH2
NN
N NH
NH
NH
N
O
O
O NH
O
NH
O
NH
OH
RS504393 α1A / CCR2b antagonist
Diovan AT1 antagonist
Merck_2 GHr agonist
Meiji Seko µ1 agonist / D2 antagonist
Merck_1 NK1 antagonist
Pfizer_1 CCK antagonist
Naftopidil α1 antagonist
Servier_1 NK1 antagonist
5HT1
A5H
T1B
5HT1
D5H
T1F
5HT2
A5H
T2B
5HT2
C5H
T4A
1AA
2AB
1B
3D
1D
2D
3D
4M
1M
3H
2Fr
eque
ncy
PS1 0 0 0 0 2 0 0 0 0 4 0 0 1 11 0 0 0 0 0 4PS2 0 0 0 0 0 1 1 0 0 0 0 0 0 2 0 14 0 0 0 4PS3 6 0 0 0 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 4PS4 2 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 4PS5 2 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 4PS6 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 3 0 0 0 4PS7 2 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 4
N
N
N
N
N
N
NH
NH
N
NH
OO
O
NH
O
NH N
PS1
PS2
PS4
PS3
PS5
PS6
PS7
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Privileged structures GPCRsThe 2-aryl-indole case
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Co-factorsBioactive Motifs with Conserved Binding Sites in Enzymes
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Ligands vs. domains and folds that bind themAnalysis of PDB – Cofactor Binding Sites Open Large Potential Target Space
Distribution patterns of small-molecule ligands in the protein universe and implications for origin of life and drug discoveryHong-Fang Ji , et al.Genome Biology 2007, 8:R176doi:10.1186/gb-2007-8-8-r176
Ligands Full name of ligands Number of
domains Number of
folds
PLP Pyridoxal-5'-phosphate 17 10 TDP ThiaminDiphosphate 36 10 SAM S-adenosylmethionine 41 13 COA CoenzymeA 29 14 GTP Guanosine-5'-triphosphate 26 15 AMP AdenosineMonophosphate 29 15 NAP NadpNicotinamide-adenine-dinucleotidePhosphate 73 16 FMN FlavinMononucleotide 122 16 NDP NadphDihydro-nicotinamide-adenine-dinucleotidePhosphate 44 18 ANP PhosphoaminophosphonicAcid-adenylateEster 59 19 FAD Flavin-adenineDinucleotide 152 21 NAD Nicotinamide-adenine-dinucleotide 149 27 GDP Guanosine-5'-diphosphate 86 29 ADP Adenosine-5'-diphosphate 137 31 ATP Adenosine-5'-triphosphate 97 35
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Sam: S-AdenosylmethionineJack of all Trades and Master of Everything?
Loenen WA.Biochem Soc Trans. 2006 Apr;34(Pt 2):330-3
NH2
O
OH
O
OH
OHN
NN
N NH2
S+
Conformational Analysis of 252 SAM PDBs3 main Clusters – Pharmacophorfamilies (J. Priestle MLI)
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MDM4
HIT
SET
46511
HTD Homology model based on SPIRO-OXINDOLE and NUTLIN MDM2 structures Constrained SP Glide docking Selection of top 20000 Compounds
Compounds From In-house MDM2 Assays and Similars Hit-finding and Hit-to-lead assays - compounds with IC50 < 50 µM (650) FCFP4 similarity search (1873) Hopfen QSAR (5000)
Literature and Patent Mining WOMBAT Literature Data and 14 Patent families from MDL Patent Database FCFP4 similarity search (675) and NB model (5000) FEPOPS (1000) similarity search, Substructure search (1707)
UNITY Searching Homology model based on SPIRO-OXINDOLE and NUTLIN MDM2 structures Constrained search: Hydrophobic centers and HBonds (49305) Constrained SP Glide docking Selection of top 20000 Compounds
Knowledge-Based Virtual Screening – MDM4/2
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FEPOPS 3D similarity searching
4 feature centroids and distances
Jenkins et al. J. Med. Chem. (2004), 47, 6144-6159.
2 4 2 4
Balanol PKC inhibitorATP
Balanol low 2D similarity to ATP
High similarity in FEPOPS as in Xray
P
O
OH
OH
NO
N
N
OH OH
O
N
NH2
P
OH
O O
PO
O
OH
NH
NH
O
O
O
OH
OH
O
OH
OO
OH
Computational Procedure
1. Generate conformers
2. Calculate FPs and properties
3. Compute fingerprints
4. Search based on fingerprints
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HNH
H
O NH
N
O
H
ClF
O
NH
Cl
ON
H
H
Cl
N
ON
NH
O
Cl
O
Pharmacophore Screening – MDM4/2
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Knowledge-Based Virtual Screening – MDM4/2
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Comparing HTS and Virtual Screening – MDM4/2
Virtual Screen HTS
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Comparing HTS and virtual screening – MDM4/2
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Chemoinformatics: Quo vadis?Towards prediction paradise?
Van de Waterbeemd, H. et al. Nat Rev Drug Discov. 2003,192-204
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Field of Informatics
Primary Data
Similarity Relationships
Key Applications
Bio- Protein
Sequences Sequence Alignments Function Inference
Protein Structures Structure Alignments Function Inference
Binding Sites Site Alignments
Target Hopping, Cross Reactivity,
Selectivity, Opportunity Mining
Structural
Sites+Ligands Binding Mode Alignments
Enhanced Screening, Scaffold Hopping, Novel Scaffolds
Chem- Small Molecules Molecular Similarities Screening
Structure Determination
Small Molecule Docking
Site Annotation
Chemoinformatics: Quo vadis?Molecular Informatics
Courtesy of Dr. D. Debe, Eidogen-Sertanty
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Chemoinformatics: Quo vadis?Chemical Systems Biology – Compound-pathway maps
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Acknowledgements
Ansgar Schuffenhauer
Maxim Popov
Kamal Azzaoui
Joerg Muehlbacher
Jeremy Jenkins
Meir Glick
Bill Egan
Peter Ertl
Bernard Faller
Hans-Joerg Roth
Peter Fuerst
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Recent Publications
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