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Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford.

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Presentation on theme: "Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford."— Presentation transcript:

1 Computational strategies and methods for building drug-like libraries Tim Mitchell, John Holland and John Woods Cambridge Discovery Chemistry & Oxford Molecular

2 2 Computational strategies and methods for building drug-like libraries What makes a molecule “drug-like” ? Drug-like screening libraries from commercial sources Reagent selection Combinatorial library design

3 3 Drug-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

4 4 Quantitative structure-toxicity relationships 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* i ]) = log A i - (  G i /2.303 RT) + logK Kier, Quant. Struct.-Act. Relat., 5, 1-7 (1986) Gombar and Jain, Indian J. Chem., 26A, (1987) Hall et al., J. Chem. Inf. Comput. Sci., 31, (1991)

5 5 Example Representation of OPS X2X2 X1X1 0 Rangeof RRaannggeeooffX2X2RRaannggeeooffX2X2 Q Query Optimum Prediction Space (OPS) Range of X 1 W E I G H T H E I G H T

6 6 Diamond Discovery TM Property Calculation & Storage … Diamond Calculation Manager Database host Compute servers Desktop clients DiamondProperties DiamondPharmacophores DiamondToxicity TsarDivaExcel DiamondDescriptors Screening data Predicted data Inventory data John Holland Richard Postance Steve Moon

7 7 Core 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

8 8 Screening Library Selection Remove unsuitable compounds using calculated properties -Mol wt. between 200 and 600 -ALogP between -2 and 6 -Estimated LD 50 > 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

9 9 Property Based Compound Selection

10 10 Core Library Compound Selection All Structures Preferred suppliers Mw, LogP, H-Bond Rot Bond Ames, LD 50 Solubility -LogP < < LogP <4.7 & #Ar 6 rings <3 425K 425K 265K 265K 133K 133K 89K 89K 78K 78K 20K 20K 19K 19K 15K 15K Stock

11 11 Screening Library Property Profiles Mean % Mean %

12 12 Screening Library Property Profiles Mean 5.4Mean 1.1Mean 3.3

13 13 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 Sources

14 14 Structure & property-based reagent selection Customer request to include  -Ph cinnamaldehyde -Unsuitable for chemistry (reductive amination) -Suggest alternatives -Similarity 166 hits, 9 aldehydes -Substructure + property 47 hits, 47 aldehydes MR = 67 AlogP = 3.5 # Ar6 = 2

15 15 Structure & property-based reagent selection

16 16 Structure & property-based reagent selection

17 17 Library 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

18 18 Library enumeration & profiling SD file of enumerated library -Calculate properties (TSAR, Batch TSAR, Diamond Discovery) Direct calculation from SD file / RS 3 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”

19 19 Precursor and property based virtual library selection Register the ID’s of the precursors associated with each product Select reagent combinations and/or property ranges from large virtual libraries

20 20 Library Profiles (DIVA) Rapidly identify precursors which result in undesirable product properties

21 21 Product-based reagent selection Select reagent sub-set and maintain product diversity

22 22 Sulfonamide - hydroxamate virtual library 11 tBu- amino acids 94 sulfonyl chlorides 68 benzyl bromides 70,312 virtual products from available reagents Caldarelli, Habermann & Ley Bioorg & Med Chem Lett 9 (1999)

23 23 Reagent selection & enumeration Reject high molecular wt., reactivity Enumerate 24K products (Afferent) Calculate product properties (Tsar) -Mol wt, AlogP -Estimated Tox. (LD 50, Ames) -Diversity Profile & select (Diva) R1 = 11R2 = 94R3 = 68 R1 = 9 R2 = 40R3 = 68 Greg Pearl

24 24 Virtual Library Profile (Diversity) Mol Wt.AlogPLogLD 50 ClusterR1R1 R2R2 R3R3

25 25 Virtual Library Profile (Toxicity) Mol Wt.AlogPLogLD 50 ClusterR1R1 R2R2 R3R3

26 26 Reagent screen & 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)

27 27 Computational strategies and methods for 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 Chemistry Booth


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