Mark Mackey takes a humourous look at Cresset's origins and the lessons we had to learn to get where we are today.

1. 25 Year of Fields: What Have we Learned?
Mark Mackey

2. Cresset
Biologically relevant method for comparing molecules
Bioisosteres Bioisosteric groups

3. How did we get there?
A glorious tale of
intrigue
skullduggery
sex
deception

4. How did we get there?
A glorious tale of unbelievably expensive
graphics hardware
phosphodiesterases
molecular electrostatics
almost no enrichment
sex at all graphs
Fortran 77

5. How did it all start?
“Some Italians in „73 or „74 did 2D plots of ESP”
Harel Weinstein (1982ish) 2D vectors on 5-HT
DHFR work at Wellcome mid-80s

6. SK&F
> COSMIC modelling package
> Modelling PDE III inhibitors (Davis, Warrington, Vinter, JCAMD
1987, 1(2), 97)

7. Promotion at SK&F
1988
All science ceased as Andy was promoted to
head of IT
1989
All science started again as Andy was fired
as head of IT

8. Lesson 1
Not all brilliant scientists make brilliant managers

9. Cambridge and Consulting
1990 – Jeremy Sanders and Chris Hunter
This led to the development of a full force field
along the same lines (Vinter, JCAMD 1994, 8, 653-
668)

10. Lesson 2
To get good answers using fields, you need good
fields

11. Publication at last!
“Multiconformational
composite molecular potential
fields in the analysis of drug
action. I. Methodology and
first evaluation using 5-HT
and histamine action as
examples”
J. G. Vinter and K. I. Trollope,
JCAMD 9 (1995) 297-307

12. The critics‟ verdict?
“Incomprehensible”
“Multiconformational composite molecular potential fields in the analysis of drug action. II” has yet to appear.

13. Lesson 3
If you write papers that people can‟t read, they
don‟t read them
“Molecular Field Extrema as Descriptors of Biological Activity: Definitions and Validation” T.
Cheeseright, M. Mackey, S. Rose and A. Vinter, JCIM 2006, 46, 655-676
Critics‟ verdict: “Mostly incomprehensible”.

14. James Black Foundation and Napp
> Field analysis now gave good(ish) qualitative
results
> Quantitation was a problem

15. Original idea
> Align and score purely on the position and size
of the field points
> Define a „pseudo-Coulombic‟ potential between
field points:
size( fp1)  size( fp 2)
E fp1 fp 2 
dist offset

16. Original idea
> Align and score purely on the position and size
of the field points
> Define a „pseudo-Coulombic‟ potential between
field points:
size( fp1)  size( fp 2)
E fp1 fp 2 
dist offset

17. Problems: Different well widths

18. Problems: Different well widths
> Not really soluble with a field point
representation– this is some of the information
we „throw away‟ going to a field minimum-based
representation
> Unfortunately, this leads to less-than-optimal
results
> Tried ellipsoidal field points etc but it didn‟t help
much

19. New idea – field sampling
> For a given field point in molecule A, instead of
estimating what the field would be at the
corresponding point in B from the positions of its
field points, why not calculate directly?
A B

20. New idea – field sampling
E A B   size( fp
fp A
A )  FB ( position ( fp A ))
A B

21. New idea – field sampling
E A B   size( fp
fp A
A )  FB ( position ( fp A ))
E A  B  EB  A 
2 E AB
E AB  S AB
2 E AA EBB
A B

22. Advantages
> The entire „true‟ field is used in the calculation
> Potential well widths implicitly included
> Fast to calculate
> Only a few field values need to be calculated
> Samples fields at biologically-relevant points
> Gauge-invariant

23. Lesson 4
Field Points aren‟t enough
You need the field as well

24. More development
> Changed the vdW field
> Used to be scaled by visible surface area, calculated 13C
NMR constants and other stuff
> Added the hydrophobic field
> Improved methods for generating initial alignments
> Field permutations
> Monte Carlo
> Grid-sampled Monte Carlo
> Greedy clique matching

25. Cresset!
> Cresset founded in November 2001
> Business plan:
1. Condense field points into fingerprints
2. Stuff in Oracle
3. $$$$$

26. FieldPrints
Initial testing showed brilliant results
100
90
80
70
% Hits Retrieved
60 Actual
50 Perfect
Random
40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
% Database Retrieved

27. FieldPrints
Later testing showed insipid results

28. Lesson 5
If the experiment works, never repeat it
Ok, not really

29. FieldPrints
Why did it look OK earlier?
Actives Decoys
• Large • Small
• Positively charged • Neutral
Surprise! FieldPrints can tell the difference!

30. Lesson 6
Testing virtual screening methods is hard.
Really hard.
Even when you know how hard it‟s going to be, it‟s
harder than that.
See
“Benchmarking Sets for Molecular Docking”, Huang et al. J. Med. Chem., 2006, 49(23), 6789-6801
“What do we know and when do we know it?”, Nicholls, JCAMD, 2008, 22(3) 239-255
“FieldScreen: Virtual Screening using Molecular Fields”, Cheeseright et al. JCIM, 2008 48(11) 2108-2117
“Better than Random? The Chemotype Enrichment Problem”, Mackey and Melville, JCIM, 2009 49(5), 1154-62
and more

31. So where did we end up?
> FieldPrints didn‟t work very well
> But the full field similarity algorithm did
(T. Cheeseright, M. Mackey, J. Melville, J. G. Vinter. (2008) 'FieldScreen: Virtual Screening Using Molecular Fields.
Application to the DUD Data Set' J. Chem. Inf. Model. 48, 2108)
> Used on ~100 virtual screening projects so far
> ~80% success rate

32. Lesson 7
See Lesson 4*
Sometimes you have to learn lessons twice
*“Field points aren’t enough: you need the field as well”

33. Other uses for field similarity
> FieldAlign
> Small-scale alignments and similarity scoring
> Useful for SAR

34. Other uses for field similarity
> FieldStere - Use field
similarity to score
bioisosteric replacements
> Avoids fragment scoring
limitations
> Allows for electronic influence
of replacing a moiety on the
rest of the molecule and vice
versa
> Allows for neighbouring group
effects

35. Other uses for field similarity
H
N+
N O
OH
O N
H
HO O
O
3 CCR5 actives
O
FieldTemplater
N
N
N+ F
N N H
FF
O
Use Fields to cross compare the actives
F Understand the pharmacophore - a detailed Field map of
N
activity
H H
F H
N N + N N
O
H
Employ the template in FieldAlign, FieldScreen, FieldStere

36. Other uses for field similarity
> Field-based QSAR
9 9
Training Set (1)
8.5 8
Test Set (1)
8 Residuals (Train) 7
7.5 Residuals (Test) 6
Predicted Activity
7 5 Electrostatics
6.5 4
6 3
5.5 2
5 1
4.5 0
4.5 5 5.5 6 6.5 7 7.5 8 8.5 9
Activity
RMSE 0.19, PRESS 0.51, RMSEpred 0.64 Sterics

37. And more research
> Current field similarity algorithm works well
> But could do better
> Improved force field (XED FF3)
> Formal charges
> Dielectric/solvent attenuation
> Clipping
> Up/downweighting different regions of the fields
> Use the protein to determine which parts of the field
are relevant

38. Lesson 8
Even when it‟s good, it could be better.
There‟s always more research to do

39. Lesson 9
If you didn‟t want to listen to me waffle on, you
should never have let me begin

40. Acknowledgements
> Andy (of course)
> Tim Cheeseright
> James Melville
> Rob Scoffin
> Brian Warrington
> Lots of other people

41. 25 Year of Fields: What Have we Learned?
Mark Mackey