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Getting Past Diversity in Assessing Virtual Library Designs. Bob Clark Tripos, Inc. St. Louis, Missouri USA. bclark@tripos.com www.tripos.com.  2001 Tripos, Inc. Where be the dragons?. Stylized data sets pyridine, pyrimidine & cyclohexane libraries semi-homologous “series”

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Getting past diversity in assessing virtual library designs

Getting Past Diversityin Assessing Virtual Library Designs

Bob Clark

Tripos, Inc.

St. Louis, Missouri USA

bclark@tripos.com

www.tripos.com

 2001 Tripos, Inc.


Where be the dragons
Where be the dragons?

  • Stylized data sets

    • pyridine, pyrimidine & cyclohexane libraries

    • semi-homologous “series”

  • Nearest-neighbor profiles

    • problems & advantages of subsetting

  • 4-Ureidopiperidine Sulfonamides

    • combinatorial sub-libraries  OptSim™ design

  • Fingerprint visualization

    • horizon NLM


Cyclohexane pyrimidine and pyridine library compositions

R

3

R

R

R

R

R

2

2

3

2

3

R

R

R

R

1

4

1

1

Cyclohexane, Pyrimidine and Pyridine Library Compositions*

N

N

N

Chex

Pym

Pyr

Position All libraries Chex& PymPyr only

R1 F, Br, NO2, Et H, Cl, CF3 none

NMe2, Ac, COCF3 Me, iPr, SMe

SPh, OPh, CH2Ph Ph

R2 F, Et, CF3, COCF3 Br, NO2, NMe2 Cl, Me, SMe, Ph

OPh, CH2Ph Ac, SPh

R3 CF3, Ac, COCF3 F, Br, NO2 CN, CO2Me, CONH2

Et, NMe2, Ac

SPh, OPh, CH2Ph

R4 none none F, iPr, CF3, SMe

Ac, COCF3, Ph

SPh, OPh, CH2Ph

*RD Clark. J Chem Inf Comput Sci1997, 37, 1181-1188.


Nearest neighbor database comparisons wrt unity 2d substructural fingerprints
Nearest Neighbor Database Comparisons(wrt UNITY 2D substructural fingerprints)*

frequency (%)

frequency (%)

Chex Pym

0.271±0.05

Chex Pyr

0.311±0.04

NN similarity

NN similarity

* RD Clark. Relative and Absolute Diversity Analysis of Combinatorial Libraries.In: Combinatorial Library Design and Evaluation, pp 337-362; AK Ghose & VN Viswanadhan, Eds.; Marcel Dekker, New York, in press.


Asymmetry of nearest neighbor profiles
Asymmetry ofNearest Neighbor Profiles

Pyr5500 Pyr500

0.932±0.05

Pyr500 Pyr5500

0.834±0.08

frequency (%)

NN similarity


Nearest neighbor profiles using maximally diverse subsets
Nearest Neighbor ProfilesUsing Maximally Diverse Subsets*

C

D

Pyr* Pyr*

0.544±0.02

Pyr2K* Pyr2K*

0.560±0.02

Pyr* Pyr

0.722±0.08

frequency (%)

Pyr2K* Pyr2K

0.729±0.09

frequency (%)

NN similarity

NN similarity

* RD Cramer, DE Patterson, RD Clark, F Soltanshahi & MS Lawless.J Chem Inf Comput Sci 1998, 38, 1010-1023.


4 ureidopiperidine sulfonamide library

4-Ureidopiperidine SulfonamideLibrary*

Primary AminesSulfonyl chlorides

Property cut-off passed cut-off passed

structure -- 436 -- 178

mol. weight 200 361 350 163

mol. volume 190 Å3 363 255 Å3 165

cLogP 2.6 370 5.0 168

aromatic rings 1 394 2 171

combined -- 308 -- 154

*RD Clark, DE Patterson, F Soltanshahi, JF Blake & JB Matthew. J Mol Graph Modelling 2000, 18, 404-411.


Ureidopiperidine sulfonamide sublibraries
Ureidopiperidine SulfonamideSublibraries

  • All were constructed using an extension of “standard” OptiSim™ selection technology

    • subsample size k = 5

    • exclusion radius 0.10

    • incremental pivot method

  • Sublibrary 1: Cherry picked

    • 200 diverse representative products

  • Sublibrary 2: four blocks, 10 x 5 each

    • 32 amines + 20 sulfonyl chlorides

  • Sublibrary 3: single 20 x 10 block

    • 20 amines + 10 sulfonyl chlorides


Optisim design scheme

B1

B2

B1

B1

B1

B1

b21 b22 b23

B1

B2

B1

B2

OptiSim Design Scheme

A1

A1

A1

A1

A1

A1

A1

a21

a22

a23

A2

A2

A2

A2

A2

a31

a32

a33

A3

B1

B2

B3

B4

b41 b42 b43

B1

B2

B3

b31 b32 b33

B1

B2

B3

B1

B2

A1

A1

A1

A1

A2

A2

A2

A2

A3

A3

A3

A3

B1

B2

B3

B4

B5

B1

B2

B3

B4

B5

B1

B2

B3

B4

B1

B2

B3

B4

B5

b51 b52 b53

A1

A1

A1

A1

A2

A2

A2

A2

A3

A3

A3

A3

a41

a42

a43

A4

A4

A4

a51

a52

a53

A5


Ureidopiperidine sulfonamide nearest neighbor profiles
Ureidopiperidine SulfonamideNearest Neighbor Profiles

single block  cherry picked

cherry picked single block

frequency (%)

frequency (%)

NN similarity

NN similarity

0.74 ± 0.09

(median 0.72)

0.81 ± 0.09

(median 0.80)


Self similarity profiles for diverse subsets from sub libraries 20 compound subsets
Self-similarity Profiles forDiverse Subsets from Sub-libraries(20 compound subsets)

frequency (%)

frequency (%)

NN similarity

NN similarity

cherry-picked: 0.52 ± 0.02 (median 0.515)

four-block: 0.55 ± 0.02 (median 0.545)

single block: 0.60 ± 0.05 (median 0.615)


Nearest neighbor profiles for diverse subsets are symmetric
Nearest Neighbor Profilesfor Diverse Subsets are Symmetric

cherry picked four block

four block  cherry picked

cherry picked single block

single block  cherry picked

frequency (%)

frequency (%)

NN similarity

NN similarity

0.61 ± 0.09 (median 0.61)

0.62 ± 0.09 (median 0.61)

0.63 ± 0.10 (median 0.58)

0.62 ± 0.11 (median 0.58)


Getting past diversity in assessing virtual library designs

PCA

(Euclidean)

NLM

(Tanimoto)


Effect of horizon distance cyclohexanes

Effect of Horizon Distance (cyclohexanes)

1

1

1

2

2

4

4

2

3

3

4

3

3

3

3

4

2

2

2

4

4

1

1

1


Homolosine projection
Homolosine Projection

source: Cartography Laboratory

Indiana State University

www.indstate.edu/gga/gga_cart


Getting past diversity in assessing virtual library designs

PCA NLMwith Horizon

37

36

25

35

34

26

33


Getting past diversity in assessing virtual library designs

PCA NLMwith Horizon

22

39

23

38

32

24

30

27

31

28

29


Comparison of sub libraries
Comparison of Sub-Libraries

cherry picking

four blocks

single block

45

53

42

51

48

46


Comparison of sub libraries1
Comparison of Sub-Libraries

cherry picking

four blocks

single block

55

54

41

53

42

51

43

50

44

45

49

47

46

48


Comparison of sub libraries2
Comparison of Sub-Libraries

cherry picking

four blocks

single block

40

55

42

52

51

44

45

47

49

46

48


Acknowledgements
Acknowledgements

  • NIH SBIR grant 1R43GM58919

  • David Patterson

    • Sr. Fellow

  • Fred Soltanshahi

    • Technologist

  • Trevor Heritage, VP Software R&D

 1999 Tripos, Inc.


Getting past diversity in assessing virtual library designs

Take-home:

fingerprint similarity

is

biologically relevant

(good neighborhood

behavior)