I used this talk on visits to International Medical University (Kuala Lumpur), Nanyang Technological University (Singapore) and Novartis Institute for Tropical Diseases (Singapore)

1. Some new directions for pharmaceutical molecular design
Peter W Kenny (pwk.pub.2008@gmail.com)

2. Some things that make drug discovery difficult
• Having to exploit targets that are weakly-linked to
human disease
• Inability to predict idiosyncratic toxicity
• Inability to measure free (unbound) physiological
concentrations of drug for remote targets (e.g.
intracellular or on far side of blood brain barrier)
Dans la merde : http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html

3. Molecular Design
• Control of behavior of compounds and materials by
manipulation of molecular properties
• Hypothesis-driven or prediction-driven
• Sampling of chemical space
– Does fragment-based screening allow better control of
sampling resolution?

4. Illustrating hypothesis-driven design
DNA Base Isosteres: Acceptor & Donor Definitions
Do1
Do2
Ac1
Kenny (2009) JCIM 49:1234-1244 DOI

5. Vmin (Ac1)
Watson-Crick Donor & Acceptor Electrostatic Potentials for
Adenine Isosteres
Va (Do1)
Kenny (2009) JCIM 49:1234-1244 DOI

6. The lurking menace of correlation inflation
Kenny & Montanari (2013) JCAMD 27:1-13 DOI

7. Preparation of synthetic data for correlation
inflation study
Add Gaussian
noise (SD=10) to Y
Kenny & Montanari (2013) JCAMD 27:1-13 DOI

8. Correlation inflation by hiding variation
See Hopkins, Mason & Overington (2006) Curr Opin Struct Biol 16:127-136 DOI
Leeson & Springthorpe (2007) NRDD 6:881-890 DOI
R = 0.34
R = 0.30
R = 0.31
R = 0.67
R = 0.93
R = 0.996
Data is naturally binned (X is an integer) and mean value of Y is calculated for
each value of X. In some studies, averaged data is only presented graphically
and it is left to the reader to judge the strength of the correlation.

9. Correlation Inflation in Flatland
See Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI
N
1202
N
R r 0.247 ( 95% CI: 0.193 | 0.299)
8
R
0.972 ( 95% CI: 0.846 | 0.995)
Kenny & Montanari (2013) JCAMD 27:1-13 DOI

10. Choosing octanol was the first mistake...

11. An alternative view of the Rule of 5
N
ClogP ≤ 5
Acc ≤ 10; Don ≤5
Polarity

12. Does octanol/water ‘see’ hydrogen bond donors?
--0.06
-0.23
--1.01
-0.24
--1.05
-0.66
Sangster lab database of octanol/water partition coefficients: http://logkow.cisti.nrc.ca/logkow/index.jsp

13. Octanol/water is not the only partitioning system
Octanol/Water
Alkane/Water

14. Differences in octanol/water and alkane/water logP values
reflect hydrogen bonding between solute and octanol
logPoct = 2.1
logPoct = 1.5
logPoct = 2.5
logPalk = 1.9
logPalk = -0.8
logPalk = -1.8
DlogP = 0.2
DlogP = 2.3
DlogP = 4.3
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI

15. Polar Surface Area is not predictive of
hydrogen bond strength
DlogP
=
0.5
PSA/ Å2 = 48
DlogP
=
4.3
PSA/ Å2 = 22
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI

16. Hydrogen bonding of esters
-0.086
-0.104
-0.091
-0.072
-0.054
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
-0.093

17. Prediction of contribution of acceptors to DlogP
N or ether O
DlogP
(corrected)
Vmin/(Hartree/electron)
Carbonyl O
DlogP
(corrected)
Vmin/(Hartree/electron)
DlogP = DlogP0 x exp(-kVmin)
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI

18. logPalk
Basis for ClogPalk model
MSA/Å2
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

19. ClogPalk from perturbation of saturated hydrocarbon
𝐶𝑙𝑜𝑔𝑃 𝑎𝑙𝑘 = 𝑙𝑜𝑔𝑃0 + 𝑠 × 𝑀𝑆𝐴 −
∆𝑙𝑜𝑔𝑃 𝐹𝐺,𝑖 −
𝑖
logPalk predicted
for saturated
hydrocarbon
∆𝑙𝑜𝑔𝑃 𝐼𝑛𝑡,𝑗
𝑗
Perturbation by
functional groups
Perturbation by
interactions
between
functional groups
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

20. Performance of ClogPalk model
Cortisone
logPalk  ClogPalk
Hydrocortisone
Papavarlne
Atropine
Propanolol
(logPalk  ClogPalk)/2
Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

21. Another way to look at SAR?

22. (Descriptor-based) QSAR/QSPR:
Some questions
• How valid is methodology (especially for validation)
when distribution of compounds in training/test space
is highly non-uniform?
• Are models predicting activity or locating neighbours?
• To what extent are ‘global’ models just ensembles of
local models?
• How well do the methods handle ‘activity cliffs’?
• How should we account for sizes of descriptor pools
when comparing model performance?

23. Measures of Diversity & Coverage
•
•
•
• •
•
•
•
•
•
•
•
•
•
•
2-Dimensional representation of chemical space is used here to illustrate concepts of diversity
and coverage. Stars indicate compounds selected to sample this region of chemical space.
In this representation, similar compounds are close together

24. Neighborhoods and library design

25. Examples of relationships between structures
Tanimoto coefficient (foyfi) for structures is 0.90
Ester is methylated acid
Amides are ‘reversed’

26. Leatherface molecular editor
From chain saw to Matched Molecular Pairs
c-[A;!R]
bnd 1 2
[nX2]1c([OH])cccc1
hyd 1 1
hyd 3 -1
bnd 2 3 2
c-Br
cul 2
hyd 1 1
Kenny & Sadowski Structure modification in chemical databases, Methods and Principles in Medicinal
Chemistry (Chemoinformatics in Drug Discovery 2005, 23, 271-285 DOI

27. Glycogen Phosphorylase inhibitors:
Series comparison
DpIC50 0.38 (0.06)
DlogFu -0.30 (0.06)
DlogS -0.29 (0.13)
DpIC50 0.21 (0.06)
DlogFu 0.13 (0.04)
DlogS 0.20 (0.09)
DpIC50 0.29 (0.07)
DlogFu -0.42 (0.08)
DlogS -0.62 (0.13)
Standard errors in mean values in parenthesis; see Birch et al (2009) BMCL 19:850-853 DOI

28. Effect of bioisosteric replacement
on plasma protein binding
?
Mining PPB database for carboxylate/tetrazole pairs suggested that bioisosteric
replacement would lead to decrease in Fu so tetrazoles were not synthesised.
Date of Analysis
N
DlogFu
SE
SD
%increase
2003
7
-0.64
0.09
0.23
0
2008
12
-0.60
0.06
0.20
0
Birch et al (2009) BMCL 19:850-853 DOI

29. Bioisosterism: Carboxylate & tetrazole
-0.262
-0.296
-0.268
-0.316
-0.315
-0.268
-0.295
Kenny (2009) JCIM 49:1234-1244 DOI
-0.261

30. Effect of amide N-methylation on aqueous solubility
is dependent on substructural context
Amide
N
DlogS
SE
SD
%Increase
Acyclic (aliphatic amine)
109
0.59
0.07
0.71
76
Cyclic
9
0.18
0.15
0.47
44
Benzanilides
9
1.49
0.25
0.76
100
Birch et al (2009) BMCL 19:850-853 DOI

31. Relationships between structures
Prediction of activity &
properties
Direct
prediction
(e.g. look up
substituent
effects)
Indirect
prediction
(e.g. apply
correction to
existing model)
Discover new
bioisosteres &
scaffolds
Recognise
extreme data
Bad
measurement
or interesting
effect?

32. MUDO Molecule Editor
• SMIRKS-based re-write of Leatherface using
OEChem
• Can process 3D structures (e.g. form covalent bond
between protein and ligand)
• Identification of matched molecular pairs is much
easier than with Leatherface
Kenny, Montanari, Propopczyk, Sala, Rodrigues Sartori (2013) JCAMD 27:655-664 DOI

33. Stuff to think about
• Molecular design is not just about prediction so
how can we make hypothesis-driven design more
systematic?
• Data can be massaged and correlations can be
inflated but it won’t extract us from ‘la merde’
• There is life beyond octanol/water (and atomcentered charges) if we choose to look for it
• Even molecules can have meaningful relationships