This document discusses various aspects of pharmaceutical molecular design. It touches on three key points: 1) Pharmaceutical molecular design aims to control compound behavior through manipulation of molecular properties in a hypothesis-driven or prediction-driven manner. 2) Hypothesis-driven design frameworks help efficiently assemble structure-activity relationships to better understand molecules and ask insightful questions. 3) Prediction-driven design assumes predictive models can be built with sufficient accuracy, though issues like non-uniform sampling of chemical space and overfitting remain challenges.

1. Aspects of pharmaceutical molecular design (Fidelta version)
Peter W Kenny
http://fbdd-lit.blogspot.com | http://www.slideshare.net/pwkenny

2. Some things that make drug discovery difficult
• Having to exploit targets that are weakly-linked to
human disease
• Poor understanding and predictability of toxicity
• Can’t measure free (unbound) physiological
concentrations of drug for remote targets in vivo
– Intracellular
– On far side of blood brain barrier
Dans la merde, FBDD & Molecular Design blog

3. Pharmaceutical Molecular Design
• Control of behavior of compounds and materials by
manipulation of molecular properties
• Hypothesis-driven or prediction-driven
• Sampling of chemical space
– For example, does fragment-based screening allow better
control of sampling resolution?
Kenny, Montanari, Propopczyk, Sala, Sartori (2013) JCAMD 27:655-664 DOI
Kenny JCIM 2009 49:1234-1244 DOI

4. Hypothesis-Driven
• Framework in which to assemble
SAR/SPR as efficiently as
possible
• Understand your molecules and
ask good questions
Prediction-Driven
• Assume that we can build
predictive models with required
degree of accuracy
Molecular Design

5. TEP = log10([𝐷𝑟𝑢𝑔 𝑿,𝑡 ] 𝑓𝑟𝑒𝑒
𝐾 𝑑
)
Target engagement potential (TEP)
A basis for pharmaceutical molecular design?
Design objectives
• Low Kd for target(s)
• High (hopefully undetectable) Kd for anti-targets
• Ability to control [Drug(X,t)]free
Kenny, Leitão & Montanari JCAMD 2014 28:699-710 DOI

6. Property-based design as search for ‘sweet spot’
Green and red lines represent probability of achieving ‘satisfactory’ affinity and
‘satisfactory’ ADMET characteristics respectively. The blue line shows the product of
these probabilities and characterizes the ‘sweet spot’. This molecular design
framework has similarities with molecular complexity model proposed by Hann et al.
Kenny & Montanari, JCAMD 2013 27:1-13 DOI

7. Beware correlation inflation

8. Data-driven design decision-making
• Predictivity of trend determined by its strength rather
than its significance
• Strength of trend determines how rigidly design
guidelines should be adhered to
• Search for strong local correlations rather than for
ways to inflate weak global correlations

9. Preparation of synthetic data sets
(drug-likeness ‘experts’ are reluctant to share their data)
Add Gaussian noise
(SD=10) to Y
Kenny & Montanari (2013) JCAMD 27:1-13 DOI
An equal number of data points are placed at equally spaced intervals on the line of
equality (Y = X) and Normally-distributed noise is added to the values of Y.

10. 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 | Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI
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.
R = 0.34 R = 0.30 R = 0.31
R = 0.67 R = 0.93 R = 0.996
N = 110 = 11  10 N = 1100 = 11  100 N = 11000 = 11  1000

11. Beyond octanol/water

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

13. 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

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

15. 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

16. 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

17. -0.054
-0.086
-0.091
-0.072
-0.104 -0.093
Connection between lipophilicity and hydrogen bonding
Toulmin et al (2008) J Med Chem 51:3720-3730 DOI
DlogP = 0.5
DlogP = 1.3
Minimized electrostatic potential (Vmin) values (atomic
units) are predictive of hydrogen bond basicity

18. logPoct = 0.97
logPalk = 1.48
logPoct = 2.17
logPalk = −0.31
logPoct = 2.23
logPalk = 0.97
logPoct = 2.42
logPalk = 0.26
logPoct = 1.66
logPalk = 1.38
logPoct = 1.35
logPalk = 2.29
logP as probe of steric and conformational effects

19. Relationships between structures
as framework for SAR

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

21. 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

22. Hypothesis-driven molecular design and relationships between
structures as framework for analysing activity and properties
?
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 . Tetrazoles were not synthesised even though their logP values are expected to
be 0.3 to 0.4 units lower than for corresponding carboxylic acids.
Birch et al (2009) BMCL19:850-853 DOI

23. 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

24. 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?

25. A quick look at ligand efficiency

26. Scale activity/affinity by risk factor
LE = ΔG/HA
Offset activity/affinity by risk factor
LipE = pIC50  ClogP
Ligand efficiency metrics
There is no reason that normalization of activity with respect to risk factor
should be restricted to either of these functional forms.
Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI

27. There’s a reason why we say standard free energy
of binding
DG = DH TDS = RTln(Kd/C0)
• Adoption of 1 M as standard concentration is
arbitrary
• A view of a chemical system that changes with
the choice of standard concentration is
thermodynamically invalid (and, with apologies to
Pauli, is ‘not even wrong’)
Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI
Efficient voodoo thermodynamics, FBDD & Molecular design blog

28. Use trend actually observed in data for normalization
rather than some arbitrarily assumed trend
Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI
Can we accurately claim to have normalized a data set if we have
made no attempt to analyse it?
Green: line of fit
Red: constant LE
Blue: constant LipE

29. NHA Kd/M C/M (1/NHA) log10(Kd/C)
10 10-3 1 0.30
20 10-6 1 0.30
30 10-9 1 0.30
10 10-3 0.1 0.20
20 10-6 0.1 0.25
30 10-9 0.1 0.27
10 10-3 10 0.40
20 10-6 10 0.35
30 10-9 10 0.33
Effect on LE of changing standard concentration
Analysis from Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI
Note that our article overlooked similar observations 5 years earlier by
Zhou & Gilson (2009) Chem Rev 109:4092-4107 DOI

30. Water
Octanol
pIC50
LipE
What we try to capture when we use lipophilic efficiency
Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI
There are two problems with this approach. Firstly octanol, is not ideal non-polar reference state
because it can form hydrogen bonds with solutes (and is also wet). Secondly, logP does not
model cost of transfer from water to octanol for ligands that bind as ionized forms
logP

31. Linear fit of ΔG to HA for published PKB ligands
Data from Verdonk & Rees (2008) ChemMedChem 3:1179-1180 DOI
HA
ΔG/kcalmol-1 ΔG/kcalmol-1  0.87  (0.44  HA)
R2  0.98 ; RMSE  0.43 kcalmol-1
-ΔGrigid

32. Ligand efficiency, group efficiency and residuals
plotted for PKB binding data
Resid|GE
GE/kcalmol-1HA-1
Resid/kcalmol-1
LE/kcalmol-1HA-1
Residuals and group efficiency values show similar trends with pyrazole (HA = 5) appearing
as outlier (GE is calculated using ΔGrigid ). Using residuals to compare activity eliminates
need to use ΔGrigid estimate (see Murray & Verdonk 2002 JCAMD 16:741-753 DOI) which is
subject to uncertainty.

33. • Data can be massaged and correlations can be
inflated but it won’t extract us from ‘la merde’
• How can we make hypothesis-driven design more
systematic and more efficient?
• There is life beyond octanol/water (and atom-
centered charges) if we choose to look for it
• Even molecules can have meaningful relationships
• Ligand efficiency: nice concept, shame about the
metrics
Stuff to think about

34. Spare slides

35. In tissues
Free in
plasma
Bound to
plasma
protein
Dose of drug
Eliminated drug
A simplified view of what happens to drugs after dosing

36. Do1 Do2
Ac1
Kenny (2009) JCIM 49:1234-1244 DOI
Illustrating hypothesis-driven design with
DNA base isosteres: H bond acceptor & donor definitions

37. Watson-Crick Donor & Acceptor Electrostatic Potentials for Adenine Isosteres
Vmin(Ac1)
Va (Do1)

38. DlogP
(corrected)
Vmin/(Hartree/electron)
DlogP
(corrected)
Vmin/(Hartree/electron)
N or ether O
Carbonyl O
logPalk as perturbation of logPoct
Prediction of contribution of acceptors to DlogP
Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730
DlogP = DlogP0 x exp(-kVmin)

39. logPoct = 0.89
predicted logPalk = -4.2
PSA/Å2 = 53
logPoct = 1.58
predicted logPalk = -1.4
PSA/Å2 = 65
Lipophilicity/polarity of Morphine & Heroin
Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730

40. 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

41. 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

42. 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

43. 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

44. Neighborhoods and library design

45. Prediction-driven design and descriptor-
based QSAR/QSPR
• How valid is methodology (especially for validation) when
distribution of compounds in training/test space is non-uniform?
• Are models predicting activity or just locating neighbors?
• To what extent are ‘global’ models just ensembles of local models?
• How should we account for number of degrees of freedom when
comparing model performance?
• How should we account for sizes of descriptor pools when
comparing model performance?
• How does sampling affect correlations between descriptors?
• How well do methods recognize ‘activity cliffs’?