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Computational strategies and methods for building drug-like libraries

Computational strategies and methods for building drug-like libraries. Tim Mitchell , John Holland and John Woods Cambridge Discovery Chemistry & Oxford Molecular. Computational strategies and methods for building drug-like libraries. What makes a molecule “drug-like” ?

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Computational strategies and methods for building drug-like libraries

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

  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. 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. Quantitative structure-toxicity relationships log (1/[T*i]) = log Ai - (Gi/2.303 RT) + logK T: Measure of toxicity • LOAEL, Carcinogenicity, LD50, etc. A (Pre-exponential factor): Transport quantifiers • Shape (k), Symmetry (S) G (Free energy term): Electronic properties • Atomic charges, E-state indices Kier, Quant. Struct.-Act. Relat., 5, 1-7 (1986) Gombar and Jain, Indian J. Chem., 26A, 554-55 (1987) Hall et al., J. Chem. Inf. Comput. Sci., 31, 76-82 (1991)

  5. X2 Optimum Prediction Space (OPS) R a n g e o f X2 W E I G H T Query Q 0 X1 Range of X1 H E I G H T Example Representation of OPS

  6. Diamond DiscoveryTM Property Calculation & Storage Desktopclients Tsar Diva Excel … Oracle / RS3 Databasehost Screening data Predicted data Inventory data Structures Diamond Calculation Manager Diamond Properties Diamond Toxicity Compute servers Diamond Pharmacophores Diamond Descriptors John Holland Richard Postance Steve Moon

  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. Screening 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

  9. Property Based Compound Selection

  10. Core Library Compound Selection 425K 265K 133K 89K 78K 20K 19K 15K • All Structures • Preferred suppliers • Mw, LogP, H-BondRot Bond • Ames, LD50 • Solubility • LogP < 3.5 • 3.5 < LogP <4.7& #Ar6 rings <3 Diverse Selection Med Chem Approval Stock

  11. Screening Library Property Profiles Mean 2.5 80% 0.6-4.1 Mean 335 80% 246-427

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

  13. Screening Library from Commercial Sources • 15K Compound Screening Library • Drug-like • Non toxic/reactive • Enhanced solubility • Diverse • Visually checked • Samples available for collaborators • 2mg / well • 80 compounds / plate

  14. Structure & property-based reagent selection • Customer request to include b-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. Structure & property-based reagent selection

  16. Structure & property-based reagent selection

  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. Library 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”

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

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

  22. Sulfonamide - hydroxamate virtual library 94 sulfonyl chlorides 11 tBu-amino acids Caldarelli, Habermann & Ley Bioorg & Med Chem Lett 9 (1999) 2049-2052 68 benzyl bromides 70,312 virtual products from available reagents

  23. Reagent 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 = 68 R1 = 9 R2 = 40 R3 = 68 Greg Pearl

  24. Virtual Library Profile (Diversity) Mol Wt. AlogP LogLD50 Cluster R1 R2 R3

  25. Virtual Library Profile (Toxicity) Mol Wt. AlogP LogLD50 Cluster R1 R2 R3

  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. 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 737-740

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