This was what I presented a the 2012 SancaMedChem course at USP São Carlos . The photograph in the title slide was taken in Asunción.

1. From screening to molecular interactions: A short tour
Peter W Kenny (pwk.pub.2008@gmail.com)

2. Some things that are hurting Pharma
• 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 within blood brain barrier)
Dans la merde: http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html

3. Screening and Chemical Space

4. 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

5. The neighborhood concept

6. Achtung!
Spitfire!
Hitting the target: The old way…
Stuka on wikipedia

7. “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
…and the new
B52 on wikipedia

8. HTS is so glamorous…

9. So, Maria, why do you
think it is that the
Russians are so much
better than the
Germans at tennis
these days?
Actually we started
to beat them at their
national sport almost
70 years ago and...
.... as Uncle Joe
was so fond of
saying, quantity
has a quality all of
its own.
… that even the stars of tennis have heard of it

10. • One measure of the power of an assay the weakness of
the binding that can be detected and quantified
Screening Assays

11. Looking for leads: An overview of screening
Chemical Space
Leads
High throughput
screening
Virtual (directed)
screening
Hit to lead
Fragment
screening

12. A model for molecular complexity
This model is equally relevant to conventional and fragment-based screening. See Hann, Leach
& Harper J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | http://dx.doi.org/10.1021/ci000403i
Success landscape

13. Degree of substitution as measure of molecular complexity
The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be
observed, will deliver a hit against both targets See Blomberg et al JCAMD 2009, 23, 513-525 |
http://dx.doi.org/10.1007/s10822-009-9264-5 | This way of thinking about molecular complexity is
similar to the ‘needle’ concept introduced by Roche researchers. See Boehm et al J. Med. Chem.
2000, 43, 2664-2774 | http://dx.doi.org/10.1021/jm000017s

14. Hopkins, Groom & Alex, DDT 2004, 9, 430-431
Ligand Lipophilicity Efficiency
LLE = pIC50 - ClogP
Leeson & Springthorpe , NRDD 2007, 6, 881-890.
Measured binding is scaled Measured binding is offset
Binding Efficiency
Measures
Ligand Efficiency
LE= DGº/NonHyd

15. FBDD Essentials
Screen fragments
Synthetic
Elaboration
Target
Target & fragment hit
Target & lead

16. Link
Fragment Elaboration Tactics
Merge
Grow

17. P
O
O
O
F F
P
O
O
O
F F
15M
Inactive at 200M
N
S
N
O
O
O
N
S
N
O
O
O
OMe
N
S
N
O
O
O
N
S
N
O
O
O
OMe
AZ10336676
3 mM
conformational lock
150 M
hydrophobic m-subst
130 M
AZ11548766
3 M
PTP1B: Fragment elaboration
Elaboration by Hybridisation: Literature SAR was mapped
onto the fragment AZ10336676 (green). Note overlay of
aromatic rings of elaborated fragment AZ11548766 (blue)
and difluorophosphonate (red). See Bioorg Med Chem Lett,
15, 2503-2507 (2005)

18. Overview of fragment based lead discovery
Target-based
compound selection
Analogues of known
binders
Generic screening
library
Measure
Kd or IC50
Screen
Fragments
Synthetic
elaboration
of hits
SAR
Protein
Structures
Milestone achieved!
Proceed to next
project

19. Why fragments?
• Access to larger chemical space
• Counter the advantage of competitors’ large
compound collections
• Ligands are assembled from proven molecular
recognition elements
• Just a smart way to do Structure-Based Design
• Control resolution at which chemical space is
sampled

20. • Control of properties of compounds and materials by
manipulation of molecular properties
• Prediction-Driven or Hypothesis-Driven
Molecular Design

21. (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?
• Do models predict activity or just locate neighbours?
• Are ‘global’ models ensembles of local models?
• How well do the methods handle ‘activity cliffs’?
• How should we account for sizes of descriptor pools
when comparing models?

22. 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 not synthesised.
Birch et al, BMCL 2009, 19, 850-853

23. Molecular Interactions and Drug Action

24. Molecular Recognition
• Functional behavior of molecules is determined by the
interactions of its molecules with the different
environments in which they exist
• Mutual presentation of molecular surfaces
• For association in water we need to match interaction
potential to maximise affinity.

25. Direct interactions Indirect interactions
‘Non-classical’
e.g. heavy
halogen
Electrostatic
e.g. hydrogen
bonding
Dispersion
forces
Steric clash Hydrophobic
Conformational
strain & entropy
Non-covalent interactions
A taxonomy of non-covalent interactions

26. Hydrogen
Bonding
Interactions between drug
molecules in crystal lattice
(Solubility, melting point
polymorphism, crystallinity)
Interactions between drug and
water molecules
(Solubility, distribution,
permeability, potency, toxicity,
efflux, metabolism)
Interactions between drug
molecules & (anti)target(s)
(Potency, toxicity, efflux ,
metabolism, distribution)
Hydrogen Bonding in Drug Discovery & Development
Interactions between water
molecules
(Hydrophobic effect)

27. Cartoon representation of hydrophobic effect
Polar Surface
Binding Pocket

28. Cartoon representation of hydrophobic forces

29. 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

30. bond basicity
 Plot of V/kJmol-1
against r/Å for pyridine on lone pair axis
showing electrostatic potential minimum 1.2Å from nitrogen
-300
-200
-100
0
V
0 1 2 3 4 5
r
Electrostatic potential as function of position for acceptor
V/kJmol-1
r/År/Å
r

31. Fluorine: A weak hydrogen bond acceptor
-0.122 -0.113 -0.071
-0.038

32. -0.054
-0.086
-0.091
-0.072
-0.104 -0.093
Hydrogen bonding of esters
Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730

33. -0.316
-0.315
-0.296
-0.295
Bioisosteric relationship between carboxylic
acids and tetrazoles
Kenny, JCIM, 2009, 49, 1234-1244
-0.262
-0.261
-0.268
-0.268

34. H
O
H H
O
H H
O
H
H
O
H
N
H
O
Effect of complex formation on predicted
hydrogen bond acidity of water
1.2
(~ Alcohol)
2.0
(~ Phenol)
2.8
(~ 4-CF3Phenol)
Kenny, JCIM, 2009, 49, 1234-1244

35. Summary
• Screening as sampling chemical space
– Fragments are thought to allow better sampling
• Molecular design as a process of tuning
interaction potential
– Design can be hypothesis-driven or prediction-
driven