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A new knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on specifically designed transform libraries abstracted from reaction databases. The structure generation process is based on conceptual chemistry and the degree of complexity introduced in the new structures can be modulated using specific parameters. Furthermore, this new system allows the integration of the results obtained in different workflows to calculate/predict other important physico-chemical properties of the new suggested molecules.
Citation preview
Knowledge-based
InfoChem GmbH Chemnotia AB
Fernando F. Huerta
powered by
Compound Design
QSAR models
Ligand Based
Pharmacophore models
Structure Based
HTS
Compound (Drug) Design
Fragment Based
How Drugs Look?
N
O
OH
ONH2F
FN
HNN
O
OH
ONH2F
FN
HNN
O
OH
ONH2F
FN
HNN
O
OH
OF
FN
HN
Norfloxacin (Noroxin™)
Ciprofloxacin (Cipro™)
Sparfloxacin (Zagam™)
Grepafloxacin (Raxar™)
Drug Discovery Today 2011, 16, 722 Drug Discovery Today 2011, 16, 779
N
HN
SO
NO
O
N
HN
SO
N
O
O
N
HN
SO
N
O O
OF
F
Nexium® Rabeprazole® PANTOPRAZOLE®
O
NN
N
Cl S
NN
N O
HO
Seroquel® Loxapine®
Protein
Binding
Site
Why Do Drugs Look Similar?
Activity, Selectivity, ADMET,..
Similar structural motif share similar properties(?)
Drug Design Paradigm
What would you do?
Compound (Drug) Design
Knowledge-based Compound Design
Transforms from Reaction Databases
Reaction database e.g. SPRESI
proprietary databases commercial databases
Transform library
1. Pre-processing
Automatic transform extraction
(ICMAP/CLASSIFY)
N
Remove bond 1-3Make new single bond between atoms 3 and 5Remove bond 1-2
Make new single bond between atoms 3 and 4
12
3
O4
5
6
7 8
910
R111
R212
13
14
Template (2 levels):
Transform:
Make new single bond between atoms 2 and 6.
Decrease bond order of double bond 2=4 by 1.
Add group to atom 3: -OH
Remove bond between atoms 3 and 6.
2. Retrosynthesis
Lookup stored
example reactions
Target molecule
O
O
HH
Precursor(s) 1
... Precursor(s) 2
Precursor(s) n ...
Transform x
Transform y
Transform z
Transforms from Reaction Databases
Reaction database e.g. SPRESI
proprietary databases commercial databases
Transform library
2. Forward Reaction
Lookup stored
example reactions
Starting Material or Reactant
Transform x
Transform y
Transform z
Product(s) 1
... Product(s) 2
Product(s) n ...
N
Cl N
O
Retrosynthetic Analysis
• Target orientated • Complexity reduction • Availability of starting
materials • Multistep process
Reaction Prediction
• Unknown product molecules
• Molecular size • Reagents • Single reaction step
Forward Reaction Prediction (cont.)
• Number of transforms
O
S
OHO O
N
OH
H H
NNH2
ICSYNTH Strategy Parameters
• Rating of generated precursors/products
• Strategy: defined by a set of parameters
• 42 parameters (Retrosynthesis / FRP)
O OH
Reactant
Transform
Product(s)
ICSYNTH FRP Parameters
Strategy Parameters Optimization
42 parameters (reactant, transform, product)
optimization algorithm
Reactivity Mapping
FG Library Design
Ring Introduction
Scaffold Modification: Not Scaffold Hopping!!
ICSYNTH FRP Strategies
FRP Examples / Applications
Reactivity Mapping
N NH
N
N OO
N NH
N
N OO
R
N NH
N
N NO R
N NH
N
N OO
Hal
N N
N
N OO
ON NH
N
N OO
R
FRP Examples / Applications
FG Library Design
N NH
N
OHO
reaction center
Knowledge-based Compound Design
Other Databases
Knowledge-based Drug Design
• One synthetic step (reliable?) • Novelty • Drug like
• Physicochemical properties • Lipinski
HN
O N
S
OHO
O
Penicillin G
Example from J. Chem. Inf. Model., Vol. 49, No. 5, 2009, 1163-‐1184
Knowledge-based Drug Design inspired by “de novo design using reaction vectors”
Example 1
HN
O N
S
OHO
O
Penicillin G
Example from J. Chem. Inf. Model., Vol. 49, No. 5, 2009, 1163-‐1184
HN
O N
S
OHO
O
Reaction Vectors
ICSYNTH FRP (precision Medium) ICSYNTH FRP (precision High)
Knowledge-based Drug Design inspired by “de novo design using reaction vectors”
Example 1
Knowledge-based Drug Design Example 1
• 30 suggested cpds from ICSYNTH vs 1000 cpds (Reaxys, penicillinG sss)
• Fingerprint similarities calculated (30000) • Identical compounds filtered off (similarity = 1) • Compounds with Tanimoto distance between 0.5-0.9
were selected • Synthetic data (ICSYNTH) available • Calculated med chem properties of new suggestions as
well as med chem properties of previously reported ones available for analysis
Knowledge-based Drug Design Example 1
Example 2
N
Cl N
O
Knowledge-based Drug Design
Core Structure (Diazepam)
Reaxys search
214 reactions / products
N
Cl
F
N
ON
Cl
HOO
N
O
N OO
N
ON
Cl O
NHO
NH
N O
NCl…
100 suggestions
Example 2
Knowledge-based Drug Design Processing Information
• 80 new reactions identified
• 10 Identical matches
• 8 suggested products with a Tanimoto
distance <0.2
• 2 abstraction errors (knime)
but… it’s not enough for compound design…
Example 2 (part II)
N
Cl N
O
Knowledge-based Drug Design
Core Structure (Diazepam)
80 new suggested reactions/products
versus
Reaxys substructure search
2564 related compounds
• Fingerprints calculated
• Identical compounds filtered off (Tanimoto = 1)
• 13 suggested products with a Tanimoto 0.7-0.9
• Physicochemical properties calculated and compared
Knowledge-based Drug Design Example 2 (part II)
New “suggested” molecules show similar properties to the known-ones
Cl
O
NN
N
Cl
O
HNO
Intermediate* used for ICSynth FRP and Reaxys sss
* Not real precursor for the final molecule
• 61 suggestions ICSYNTH FRP (one synthetic step away from intermediate)
• 922 related compounds in the literature (Loxapine included)
• 6 new compounds with Tanimoto distance between 0.5-0.9 were suggested
Loxapine®
Knowledge-based Drug Design Example 3
O
N
R
Cl O
NN
Cl
ArZ
O
NNH
Cl
Ar
O
NX
Cl
One more thing…
building synthetic confidence
filtering ICSYNTH cpds with low synthetic background • number of precedent reactions of the same type • precedent yield reported • level of similarity from the original reaction
Summary
ACKNOWLEDGMENTS
InfoChem Peter Loew Heinz Saller Christoph Oppawsky Mike Hutchings Hans Kraut Valentina Eigner-Pitto Josef Eiblmaier Ulf Frieske Stephanie North
Chemnotia Anders Bogevig Tobias Rein
THANK YOU