This document discusses using 3D molecular comparisons to identify activity cliffs during lead optimization. It introduces the concepts of activity cliffs, where small molecular changes lead to large changes in activity, and 3D molecular disparity, which identifies structural differences beyond 2D metrics. The document outlines Cresset Technology's approach to generating 3D conformers, aligning molecules, calculating similarity matrices, and visualizing disparity pairs. It presents case studies on acetylcholinesterase and PERK kinase inhibitors where 3D analysis revealed substitutions and interactions correlating with improved activity. The document concludes 3D cliff analysis is a powerful SAR tool that provides insights into both what structural changes improve activity and why those changes may be effective.

1. Finding and using activity cliffs in 3D: Gaining more SAR
information during lead optimization
Tim Cheeseright, Rae Lawrence, Mark Mackey, Martin Slater

2. © 2013 Cresset Group Ltd.
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Agenda
> Activity Cliffs
> Why 3D
> Generating Comparisons
> Disparity
> Visualisation
> Case Studies

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Similarity Hypothesis
> Similar molecules have similar activities
> Small changes lead to small changes
> QSAR, virtual screening, LO
O
OH
OH
OH
N
F
O
NH O
OH
OH
OH
N
O
NH

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Activity Cliffs
> What about the bits where the similarity
hypothesis breaks down?
O
N
H
N
O
N
H
N
6.8
4.4

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The Interesting Bits

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What “distance”
> Usually distance = 1 – similarity
> Similarity in 2D
> Are we missing anything?
OH
O
NH
N
N
N
H2N
O
OH
H2N
O
OH
N
Cl
N
H
N
O
N
Cl
N
H
N
O
Bioisosteres
Chirality
Fingerprint locality (ECFP4 sim=1.0)

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3D molecular similarity
> 2D metrics are easy: 1:1 map to topology
> 3D is defined for conformers, not for molecules
0.93 0.94

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3D molecular similarity
> 2D metrics are easy: 1:1 map to topology
> 3D is defined for conformers, not for molecules
0.58 0.94

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Context is everything
> Don’t need/want generic 3D similarity
> Have activity context
> Align all molecules to known bioactive reference
conformer
> Provides a conformation context to each
molecule

10. © 2013 Cresset Group Ltd.
Cresset Technology
> Condensed representation of electrostatic, hydrophobic
and shape properties (“protein’s view”)
 Molecular Field Extrema (“field points”)
3D Molecular
Electrostatic
Potential (MEP)
Field Points
= Positive
= Negative
= Shape
= Hydrophobic
2D

11. © 2013 Cresset Group Ltd.
Non-Classical Comparisons
Fields
0.66
Shape
0.98
Cheeseright et al, J. Chem Inf. Mod., 2006, 665

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Non-Classical Comparisons
Fields
0.66
Shape
0.98
Cheeseright et al, J. Chem Inf. Mod., 2006, 665
Combined
0.82

13. © 2013 Cresset Group Ltd.
3D disparity
1. Generate conformers
2. Align to reference(s)
3. Calculate similarity matrix on aligned
conformations
> Allow small movements
4. Calculate disparity matrix from similarity
numbers
> Similarity cutoff of 0.95
5. Visualise
> Difficult – 100 molecules gives 4950 pairs!

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Visualisation
> Existing ways to visualise
> Matrix views
> Graph view (Guha/van Drie 2008)
> Activity landscapes (Bajorath)

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Snail plot

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Flower plot

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Fields

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Field differences

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AChE
> Sutherland QSAR data set
Magic methyl
position
6.8 4.4
Disparity 49

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Phenyl to difluorophenyl
20
Has an effect here
Also has an effect here!

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Example: PERK Kinase Inhibitors
> When PERK is inhibited in cancer cells, the
viability of those cells in nutrient or oxygen
deprived conditions is reduced, which can lead
to apoptosis and inhibition of tumor
> Two related series:
J. Med. Chem., 2012, 55, 7193.

22. © 2013 Cresset Group Ltd.
Activity View: Focus on Molecule #31
pIC50 = 8.8
> Short slices = high
Similarity
> Darker colour =
higher Disparity
> Green = Improved
Activity
> Areas of structural
changes are
“haloed”

23. © 2013 Cresset Group Ltd.
Investigating Molecule #31 (Activity View)
pIC50 = 8.8
> Substituent position on the
terminal ring
pIC50 Similarity Substituent
25 9.8 0.897 meta-Cl
21 9.6 0.893 meta-F
24 9.7 0.862 meta-F
meta-F
ortho-F
22 9.7 0.859 ortho-CH3
19 9.4 0.857 meta-CF3
35 9.1 0.840 ortho-Cl
18 9.4 0.830 2-F(ortho),
5-F(meta)
23 9.7 0.810 meta-Cl
> Small structural changes
(high Sim) resulting in pIC50
improvement.
> Removal of para substituent
and replacement with meta
and/or ortho groups improves
activity.
> Consistent with observations
reported by GSK. (J. Med.
Chem., 2012, 55, 7193)
3D Sim = 50% Fields, 50% Shape

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Activity View: Focus on Molecule #25
#25, pIC50 = 8.1 #18, pIC50 = 9.4
> Furanopyrimidine core replaced by a N-methyl
pyrrolopyrimidine gives >1 log unit improvement.
> Examine shape and electrostatics to understand

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#25 (pIC50=8.1) vs. #18 (pIC50=9.4)

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#25 (pIC50=8.1) vs. #18 (pIC50=9.4)
> N-methyl substitution changes dipole across the
ring, making NH2 less +ve (weaker H bond donor),
BUT, adjacent N becomes more –ve (stronger H
bond acceptor)
> Activity increase in the N-methyl could be due to
stronger aromatic-aromatic interaction through its
larger π-cloud, rather than simply due to the
substituent itself.

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Pi-stacking probably part of the activity increase
4G31

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Conclusions
> Disparity/Activity Cliff analysis in 3D is a powerful
tool
> What
> But also Why
> Needs reliable 3D alignment and scoring
> Data sets can be complicated, so clear
visualisations are necessary
> Activity Miner due for release very soon!

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Acknowledgements
> Tim Cheeseright
> Rae Lawrence
> Martin Slater
> Nigel Palmer
> Andy Vinter