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![Page 1: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/1.jpg)
Computational strategies Computational strategies and methods for building and methods for building drug-like librariesdrug-like libraries
Tim Mitchell, John Holland and John Woods
Cambridge Discovery Chemistry & Oxford Molecular
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Computational strategies and methods Computational strategies and methods for building drug-like librariesfor building drug-like libraries
What makes a molecule “drug-like” ?
Drug-like screening libraries from commercial sources
Reagent selection
Combinatorial library design
![Page 3: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/3.jpg)
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Drug-like propertiesDrug-like properties
Solubility, bio-availability
- Mw, LogP, H-bonds
Toxicity, reactivity
- Topkat
Relatively quick and easy to calculate
- Robust desk-top access can be an issue
![Page 4: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/4.jpg)
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Quantitative structure-toxicity Quantitative structure-toxicity relationshipsrelationships
T: Measure of toxicity
- LOAEL, Carcinogenicity, LD50, etc.
A (Pre-exponential factor): Transport quantifiers
- Shape (), Symmetry (S)
G (Free energy term): Electronic properties
- Atomic charges, E-state indices
log (1/[T*log (1/[T*ii]) = log A]) = log Aii - ( - (GGii/2.303 RT) + /2.303 RT) +
logKlogK
Kier, Quant. Struct.-Act. Relat., 5, 1-7 (1986)Kier, Quant. Struct.-Act. Relat., 5, 1-7 (1986)Gombar and Jain, Indian J. Chem., 26A, 554-55 (1987)Gombar and Jain, Indian J. Chem., 26A, 554-55 (1987)Hall et al., J. Chem. Inf. Comput. Sci., 31, 76-82 (1991)Hall et al., J. Chem. Inf. Comput. Sci., 31, 76-82 (1991)
![Page 5: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/5.jpg)
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Example Representation of OPSExample Representation of OPS
X2
X10
RRaannggee
ooff
XX22 QQ
Query
Optimum Optimum Prediction Prediction
SpaceSpace(OPS)(OPS)
Range of XRange of X11
W E
I G
H T
W E
I G
H T
H E I G H TH E I G H T
![Page 6: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/6.jpg)
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Diamond DiscoveryDiamond DiscoveryTMTM Property Property Calculation & StorageCalculation & Storage
…
Diamond Calculation ManagerDiamond Calculation Manager
DatabaseDatabasehosthost
Compute Compute serversservers
DesktopDesktopclientsclients
DiamondDiamondPropertiesProperties
DiamondDiamondPharmacophoresPharmacophores
DiamondDiamondToxicityToxicity
TsarTsar DivaDiva ExcelExcel
DiamondDiamondDescriptorsDescriptors
Screening dataScreening dataPredicted dataPredicted dataInventory dataInventory data
John Holland Richard Postance Steve Moon
![Page 7: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/7.jpg)
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Core Library Compound SelectionCore Library Compound Selection
Identify ~15,000 compounds from the ~425,000 compounds in our database of commercially available suppliers
Previous experience of Maybridge, BioNet, Menai Organics, AsInEx, ChemStar, Contact Service & Specs indicates their compounds are what they say they are and are >80% pure
![Page 8: Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.](https://reader036.fdocuments.in/reader036/viewer/2022062621/551c40d6550346a3488b4ab4/html5/thumbnails/8.jpg)
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Screening Library SelectionScreening Library Selection
Remove unsuitable compounds using calculated properties
- Mol wt. between 200 and 600
- ALogP between -2 and 6
- Estimated LD50 > 100 mg/kg (removes reactive compounds)
- Estimated Ames mutagenicity probability <0.9 (removed hyper-conjugated and activated aromatic)
- Rotatable bonds <= 12
- Likely to be insoluble in 10% DMSO/Water
Cluster on atom & bond fingerprint and select representatives
Visually inspect
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Property Based Compound SelectionProperty Based Compound Selection
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Core Library Compound SelectionCore Library Compound Selection
All Structures
Preferred suppliers
Mw, LogP, H-BondRot Bond
Ames, LD50
Solubility
- LogP < 3.5
- 3.5 < LogP <4.7& #Ar6 rings <3
425K425K
265K265K
133K133K
89K89K
78K78K
20K20K
19K19K
15K15KStockStock
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Screening Library Property ProfilesScreening Library Property Profiles
Mean 33580% 246-427
Mean 2.580% 0.6-4.1
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Screening Library Property ProfilesScreening Library Property Profiles
Mean 5.4 Mean 1.1 Mean 3.3
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15K Compound Screening Library
- Drug-like
- Non toxic/reactive
- Enhanced solubility
- Diverse
- Visually checked
Samples available for collaborators
- 2mg / well
- 80 compounds / plate
Screening Library from Commercial Screening Library from Commercial SourcesSources
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Structure & property-based reagent Structure & property-based reagent selectionselection
Customer request to include -Ph cinnamaldehyde
- Unsuitable for chemistry (reductive amination)
- Suggest alternatives
- Similarity 166 hits, 9 aldehydes
- Substructure + property 47 hits, 47 aldehydes
O
H
MR = 67 AlogP = 3.5# Ar6 = 2
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Structure & property-based reagent Structure & property-based reagent selectionselection
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Structure & property-based reagent Structure & property-based reagent selectionselection
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Library design strategiesLibrary design strategies
Focused library design: Reagent-based selection
- Maximum diversity is not required in focused libraries Systematically optimise substituents
- Synthesise fully enumerated libraries Difficult to cherry-pick and fully enumerate
Reagent selection is compatible with plate layout (8x12 etc.)
- We never know everything about a target Some diversity always necessary
Diverse library design: Product-based selection
- Balance of diversity vs. practical issues
- Product based reagent selection
- 2-D fingerprint / 3-D pharmacophore / 3-D similarity
Drug like properties become increasingly more important as a project progresses from lead discovery to lead optimisation
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Library enumeration & profilingLibrary enumeration & profiling
SD file of enumerated library
- Calculate properties (TSAR, Batch TSAR, Diamond Discovery)
Direct calculation from SD file / RS3 Database
Mol wt., Log P, H-bond donors & acceptors
Toxicity
- Analyse profiles (DIVA) Replace any “problem” reagents
- Check for pharmacophores (Chem-X)
- Register as “Work in Progress”
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Precursor and property based virtual Precursor and property based virtual library selectionlibrary selection
Register the ID’s of the precursors associated with each product
Select reagent combinations and/or property ranges from large virtual libraries
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Library Profiles (DIVA)Library Profiles (DIVA) Rapidly identify precursors which result in undesirable
product properties
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Product-based reagent selectionProduct-based reagent selection
Select reagent sub-set and maintain product diversity
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Sulfonamide - hydroxamate virtual Sulfonamide - hydroxamate virtual librarylibrary
NH2
O
OS
O
O
Cl
H
H
Br
11 tBu-amino acids
94 sulfonylchlorides
68 benzyl bromides
70,312 virtual products from available reagents
HO
HN
NS
O
R1
R3
O
O
R2
Caldarelli, Habermann & LeyBioorg & Med Chem Lett9 (1999) 2049-2052
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Reagent selection & enumerationReagent selection & enumeration
Reject high molecular wt., reactivity
Enumerate 24K products (Afferent)
Calculate product properties (Tsar)
- Mol wt, AlogP
- Estimated Tox. (LD50, Ames)
- Diversity
Profile & select (Diva)
R1 = 11 R2 = 94 R3 = 68R1 = 9 R2 = 40 R3 = 68
NH2
O
O SO
O
ClH
H
Br
Greg Pearl
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Virtual Library Profile (Diversity)Virtual Library Profile (Diversity)
Mol Wt. AlogP LogLD50 Cluster R1 R2 R3
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Virtual Library Profile (Toxicity)Virtual Library Profile (Toxicity)
Mol Wt. AlogP LogLD50 Cluster R1 R2 R3
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Reagent screen & Reagent screen & virtual library profile virtual library profile
Screen reagents- 70,312 (11x94x68) 24,480 (9x40x68)
Reduce Virtual Lib / Maintain Diversity - 24,480 (9x40x68) 8,160 (3x40x68)
Remove likely toxic compounds - 8,160 (3x40x68) 6549 (3x37x59)
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Computational strategies and methods Computational strategies and methods for building drug-like librariesfor building drug-like libraries
The ability to calculate, store and search descriptors of hundreds of thousands of compounds is key to both compound selection and library design
Estimated toxicity calculations are useful additions to “standard” molecular descriptors
Calculated properties and analysis tools are readily accessible from a chemists desktop
Property and diversity profiles are very effective, and ensure chemists buy-in to the design process
Oxford Molecular / Cambridge Discover ChemistryBooth 737-740