Presented at Vertex and Novartis during July 2013 trip to Cambridge MA. A correlation inflation teaser followed by alkane/water logP and SAR/SPR based on relationships between structures. The photograph in the title slide was taken in Asunción.

1. I’m a molecule designer…. Get me out of here!
Peter W Kenny (pwk.pub.2008@gmail.com)

2. Some things that are hurting Pharma
• Having to exploit targets that are poorly-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 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. Achtung!
Spitfire!
Prediction-driven design: Ju 87 Stuka
Stuka on wikipedia

5. “Why can’t we pray for something good, like a tighter
bombing pattern, for example? Couldn’t we pray for a
tighter bombing pattern?” , Heller, Catch 22, 1961
Hypothesis-driven design: B52 Stratofortress
B52 on wikipedia

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

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

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

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

10. Choosing octanol was the first mistake...

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

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

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

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

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

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

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

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

19. 𝐶𝑙𝑜𝑔𝑃𝑎𝑙𝑘 = 𝑙𝑜𝑔𝑃0 + 𝑠 × 𝑀𝑆𝐴 −
𝑖
∆𝑙𝑜𝑔𝑃𝐹𝐺,𝑖 −
𝑗
∆𝑙𝑜𝑔𝑃𝐼𝑛𝑡,𝑗
ClogPalk from perturbation of saturated hydrocarbon
logPalk predicted
for saturated
hydrocarbon
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
Hydrocortisone
Cortisone
(logPalk  ClogPalk)/2
logPalkClogPalk
Atropine
Propanolol
Papavarlne
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
c-Br
cul 2
hyd 1 1
[nX2]1c([OH])cccc1
hyd 1 1
hyd 3 -1
bnd 2 3 2
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
DlogFu
DlogS
0.38 (0.06)
-0.30 (0.06)
-0.29 (0.13)
DpIC50
DlogFu
DlogS
0.21 (0.06)
0.13 (0.04)
0.20 (0.09)
DpIC50
DlogFu
DlogS
0.29 (0.07)
-0.42 (0.08)
-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
?
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
Mining PPB database for carboxylate/tetrazole pairs suggested that bioisosteric
replacement would lead to decrease in Fu so tetrazoles were not synthesised.
Birch et al (2009) BMCL 19:850-853 DOI

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

30. 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
Effect of amide N-methylation on aqueous solubility
is dependent on substructural context
Birch et al (2009) BMCL 19:850-853 DOI

31. Relationships between structures
Discover new
bioisosteres &
scaffolds
Prediction of activity &
properties
Recognise
extreme data
Direct
prediction
(e.g. look up
substituent
effects)
Indirect
prediction
(e.g. apply
correction to
existing model)
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
• Submitted (source code in supplemental information):
– Kenny, Montanari, Propopczyk, Sala, Rodrigues Sartori
(2013) Automated molecule editing in molecular design.
JCAMD (submitted)

33. Stuff to think about
• Data can be massaged and correlations can
be enhanced but it won’t extract us from ‘la
merde’
• There is life beyond octanol/water (and atom-
centered charges) if we choose to look for it
• Even molecules can have meaningful
relationships