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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

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 libraries1
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
drug like properties
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
quantitative structure toxicity relationships
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)

example representation of ops

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
diamond discovery tm property calculation storage
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

core library compound selection
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
screening library selection
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
core library compound selection1
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

screening library property profiles
Screening Library Property Profiles

Mean 2.5

80% 0.6-4.1

Mean 335

80% 246-427

screening library property profiles1
Screening Library Property Profiles

Mean 5.4

Mean 1.1

Mean 3.3

screening library from commercial sources
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
structure property based reagent selection
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

library design strategies
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
library enumeration profiling
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”
precursor and property based virtual library selection
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
slide20

Library Profiles (DIVA)

  • Rapidly identify precursors which result in undesirable product properties
product based reagent selection
Product-based reagent selection
  • Select reagent sub-set and maintain product diversity
sulfonamide hydroxamate virtual library
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

reagent selection enumeration
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

virtual library profile diversity
Virtual Library Profile (Diversity)

Mol Wt.

AlogP

LogLD50

Cluster

R1

R2

R3

virtual library profile toxicity
Virtual Library Profile (Toxicity)

Mol Wt.

AlogP

LogLD50

Cluster

R1

R2

R3

reagent screen virtual library profile
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)
computational strategies and methods for building drug like libraries2
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