Computational strategies and methods for building drug-like libraries

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

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

Drug-like properties l Solubility, bio-availability - Mw, Log. P, H-bonds l Toxicity, reactivity - Topkat l Relatively quick and easy to calculate - Robust desk-top access can be an issue 3

Quantitative structure-toxicity relationships log (1/[T*i]) = log Ai - ( Gi/2. 303 RT) + log. K T: Measure of toxicity - LOAEL, Carcinogenicity, LD 50, etc. A (Pre-exponential factor): Transport quantifiers - Shape (k), Symmetry (S) G (Free energy term): Electronic properties - Atomic charges, E-state indices 4 Kier, Quant. Struct. -Act. Relat. , 5, 1 -7 (1986) Gombar and Jain, Indian J. Chem. , 26 A, 554 -55 (1987) Hall et al. , J. Chem. Inf. Comput. Sci. , 31, 76 -82 (1991)

Example Representation of OPS WEIGHT X 2 Optimum Prediction Space (OPS) R a n g e o f Query X 2 Q 0 Range of X 1 HEIGHT 5 X 1

Diamond Discovery. TM Property Calculation & Storage Desktop clients Database host Tsar Diva Excel Screening data Predicted data … Inventory data Diamond Calculation Manager Compute servers 6 Diamond Properties Diamond Toxicity Diamond Pharmacophores John Holland Richard Postance Steve Moon Diamond Descriptors

Core Library Compound Selection l l 7 Identify ~15, 000 compounds from the ~425, 000 compounds in our database of commercially available suppliers Previous experience of Maybridge, Bio. Net, Menai Organics, As. In. Ex, Chem. Star, Contact Service & Specs indicates their compounds are what they say they are and are >80% pure

Screening Library Selection l Remove unsuitable compounds using calculated properties - Mol wt. between 200 and 600 - ALog. P 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 8 - Likely to be insoluble in 10% DMSO/Water

Property Based Compound Selection 9

Core Library Compound Selection l l Preferred suppliers l Mw, Log. P, H-Bond Rot Bond l 425 K All Structures Ames, LD 50 l 265 K 133 K 89 K 78 K Solubility - Log. P < 3. 5 20 K - 3. 5 < Log. P <4. 7 & #Ar 6 rings <3 19 K Stock 10 15 K

Screening Library Property Profiles Mean 2. 5 80% 0. 6 -4. 1 Mean 335 80% 246 -427 11

Screening Library Property Profiles Mean 5. 4 12 Mean 1. 1 Mean 3. 3

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

Structure & property-based reagent selection l Customer request to include b-Ph cinnamaldehyde - Unsuitable for chemistry (reductive amination) - Suggest alternatives - Similarity l 166 hits, 9 aldehydes - Substructure + property l 14 47 hits, 47 aldehydes MR = 67 Alog. P = 3. 5 # Ar 6 = 2

Structure & property-based reagent selection 15

Structure & property-based reagent selection 16

Library design strategies l Focused library design: Reagent-based selection - Maximum diversity is not required in focused libraries l Systematically optimise substituents - Synthesise fully enumerated libraries l Difficult to cherry-pick and fully enumerate l Reagent selection is compatible with plate layout (8 x 12 etc. ) - We never know everything about a target l l Some diversity always necessary Diverse library design: Product-based selection - Balance of diversity vs. practical issues - Product based reagent selection 17 - 2 -D fingerprint / 3 -D pharmacophore / 3 -D similarity

Library enumeration & profiling l SD file of enumerated library - Calculate properties (TSAR, Batch TSAR, Diamond Discovery) l Direct calculation from SD file / RS 3 Database l Mol wt. , Log P, H-bond donors & acceptors l Toxicity - Analyse profiles (DIVA) l Replace any “problem” reagents - Check for pharmacophores (Chem-X) - Register as “Work in Progress” 18

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

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

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

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

Reagent selection & enumeration R 1 = 11 R 1 = 9 R 2 = 94 R 2 = 40 l Reject high molecular wt. , reactivity l Enumerate 24 K products (Afferent) l R 3 = 68 Calculate product properties (Tsar) - Mol wt, Alog. P - Estimated Tox. (LD 50, Ames) - Diversity 23 l Profile & select (Diva) Greg Pearl

Virtual Library Profile (Diversity) Mol Wt. 24 Alog. P Log. LD 50 Cluster R 1 R 2 R 3

Virtual Library Profile (Toxicity) Mol Wt. 25 Alog. P Log. LD 50 Cluster R 1 R 2 R 3

Reagent screen & virtual library profile l Screen reagents - 70, 312 (11 x 94 x 68) 24, 480 (9 x 40 x 68) l Reduce Virtual Lib / Maintain Diversity - 24, 480 (9 x 40 x 68) 8, 160 (3 x 40 x 68) l Remove likely toxic compounds - 8, 160 (3 x 40 x 68) 6549 (3 x 37 x 59) 26

Computational strategies and methods for building drug-like libraries l l Estimated toxicity calculations are useful additions to “standard” molecular descriptors l Calculated properties and analysis tools are readily accessible from a chemists desktop l 27 The ability to calculate, store and search descriptors of hundreds of thousands of compounds is key to both compound selection and library design Property and diversity profiles are very effective, and ensure chemists buy-in to the design process Oxford Molecular / Cambridge Discover Chemistry Booth 737 -740