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
2

What makes a molecule “drug-like” ?

Drug-like screening libraries from commercial sources

Reagent selection

Combinatorial library design
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
3
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
4
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
WEIGHT
X2
Optimum
Prediction
Space
(OPS)
R
a
n
g
e
o
f
Query
X2
Q
0
Range of X1
HEIGHT
5
X1
Diamond DiscoveryTM Property
Calculation & Storage
Desktop
clients
Database
host
Tsar
Diva
Excel
Screening data
Predicted data
… data
Inventory
Diamond Calculation Manager
Compute
servers
6
Diamond
Properties
Diamond
Toxicity
Diamond
Pharmacophores
John Holland Richard Postance Steve Moon
Diamond
Descriptors
Core Library Compound Selection
7

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

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
8
- Likely to be insoluble in 10% DMSO/Water
Property Based Compound Selection
9
Core Library Compound Selection

All Structures

Preferred suppliers

Mw, LogP, H-Bond
Rot Bond

Ames, LD50

425K
265K
133K
89K
78K
Solubility
- LogP < 3.5
20K
- 3.5 < LogP <4.7
& #Ar6 rings <3
19K
Stock
10
15K
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

15K Compound Screening Library
- Drug-like
- Non toxic/reactive
- Enhanced solubility
- Diverse
- Visually checked

Samples available for collaborators
- 2mg / well
- 80 compounds / plate
13
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

14
47 hits, 47 aldehydes
H
O
MR = 67
AlogP = 3.5
# Ar6 = 2
Structure & property-based reagent
selection
15
Structure & property-based reagent
selection
16
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
17
- 2-D fingerprint / 3-D pharmacophore / 3-D similarity
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”
18
Precursor and property based virtual
library selection
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)

20
Rapidly identify precursors which result in undesirable
product properties
Product-based reagent selection

21
Select reagent sub-set and maintain product diversity
Sulfonamide - hydroxamate virtual
library
O
O
NH2
O
HO
R1 O
H
N
N
R2
S
O
O
11 tBuamino acids
94 sulfonyl
chlorides
R3
H
H
Br
Caldarelli, Habermann & Ley
Bioorg & Med Chem Lett
9 (1999) 2049-2052
22
Cl
S
O
68 benzyl
bromides
70,312 virtual products from available reagents
Reagent selection & enumeration
O
H
O
NH2
O
R1 = 11
R1 = 9
H
Cl
S
O
Br
R2 = 94
R2 = 40

Reject high molecular wt., reactivity

Enumerate 24K products (Afferent)

Calculate product properties (Tsar)
R3 = 68
R3 = 68
- Mol wt, AlogP
- Estimated Tox. (LD50, Ames)
- Diversity
23

Profile & select (Diva)
Greg Pearl
Virtual Library Profile (Diversity)
Mol Wt.
24
AlogP
LogLD50
Cluster
R1
R2
R3
Virtual Library Profile (Toxicity)
Mol Wt.
25
AlogP
LogLD50
Cluster
R1
R2
R3
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)
26
Computational strategies and methods
for building drug-like libraries
27

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