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Getting Past Diversity in Assessing Virtual Library Designs Bob Clark Tripos, Inc. St. Louis, Missouri USA [email protected] www.tripos.com 2001 Tripos, Inc.

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

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Page 1: Getting Past Diversity in Assessing  Virtual Library Designs

Getting Past Diversityin Assessing

Virtual Library Designs

Bob ClarkTripos, Inc.

St. Louis, Missouri USA

[email protected]

2001 Tripos, Inc.

Page 2: Getting Past Diversity in Assessing  Virtual Library Designs

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

Page 3: Getting Past Diversity in Assessing  Virtual Library Designs

N

NN

R3R2

R1

R3R2

R1 R4R1

R2

R3

PyrPymChex

Position All libraries Chex & Pym Pyr only

R1 F, Br, NO2, Et H, Cl, CF3 noneNMe2, Ac, COCF3 Me, iPr, SMeSPh, OPh, CH2Ph Ph

R2 F, Et, CF3, COCF3 Br, NO2, NMe2 Cl, Me, SMe, PhOPh, CH2Ph Ac, SPh

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

Et, NMe2, AcSPh, OPh, CH2Ph

R4 none none F, iPr, CF3, SMeAc, COCF3, PhSPh, OPh, CH2Ph

Cyclohexane, Pyrimidine and Pyridine Library Compositions*

*RD Clark. J Chem Inf Comput Sci 1997, 37, 1181-1188.

Page 4: Getting Past Diversity in Assessing  Virtual Library Designs

ChexPyr0.311±0.04

ChexPym0.271±0.05

NN similarityfr

eque

ncy

(%)

NN similarity

freq

uenc

y (%

)

Nearest Neighbor Database Comparisons(wrt UNITY 2D substructural fingerprints)*

* 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.

Page 5: Getting Past Diversity in Assessing  Virtual Library Designs

NN similarity

freq

uenc

y (%

)

Pyr5500Pyr5000.932±0.05

Pyr500Pyr55000.834±0.08

Asymmetry ofNearest Neighbor Profiles

Page 6: Getting Past Diversity in Assessing  Virtual Library Designs

C D

NN similarityfr

eque

ncy

(%)

NN similarity

freq

uenc

y (%

) Pyr*Pyr*0.544±0.02

Pyr2K*Pyr2K*0.560±0.02

Pyr*Pyr0.722±0.08

Pyr2K*Pyr2K0.729±0.09

Nearest Neighbor ProfilesUsing Maximally Diverse Subsets*

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

Page 7: Getting Past Diversity in Assessing  Virtual Library Designs

NOCN tBOCR1CH2NH2 R2SO2Cl N

NH

NH

R1

SO2R2O

4-Ureidopiperidine SulfonamideLibrary*

Primary Amines Sulfonyl 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.

Page 8: Getting Past Diversity in Assessing  Virtual Library Designs

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

Page 9: Getting Past Diversity in Assessing  Virtual Library Designs

A1

B1

A2

B1B1B1 B1 B2

B1 B2B1 B2

B1 B2B1 B2 B3

b21 b22 b23

b31 b32 b33b41 b42 b43B1 B2 B3B1 B2 B3 B4

a21

a22

a23

a31

a32

a33

A1

A3

A1A1

B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4 B5B5B5b51 b52 b53

A2

A1

A3

A2

A1

A3

A2

A1

A3

A2

A1

A3

A2

A1

A3

A2

A1

A3

A2

A1

A3

A2

A2

A1

A2

A1

A2

A1

A5

A4

A1

A3

A2

A4A4a41

a42

a43

a51

a52

a53

OptiSim Design Scheme

Page 10: Getting Past Diversity in Assessing  Virtual Library Designs

Ureidopiperidine SulfonamideNearest Neighbor Profiles

NN similarityfr

eque

ncy

(%)

NN similarity

freq

uenc

y (%

)

single block cherry picked cherry picked single block

0.74 ± 0.09(median 0.72)

0.81 ± 0.09(median 0.80)

Page 11: Getting Past Diversity in Assessing  Virtual Library Designs

Self-similarity Profiles forDiverse Subsets from Sub-libraries

(20 compound subsets)

NN similarityfr

eque

ncy

(%)

NN similarity

freq

uenc

y (%

)

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)

Page 12: Getting Past Diversity in Assessing  Virtual Library Designs

Nearest Neighbor Profilesfor Diverse Subsets are Symmetric

NN similarityfr

eque

ncy

(%)

NN similarity

freq

uenc

y (%

)cherry picked four blockfour block cherry picked

cherry picked single blocksingle block cherry picked

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)

Page 13: Getting Past Diversity in Assessing  Virtual Library Designs

PCA(Euclidean)

NLM(Tanimoto)

Page 14: Getting Past Diversity in Assessing  Virtual Library Designs

1

42

3

1

4

2

3

1

4

23

1

4

3

2

1

4

3

2

1

4

3

2

Effect of Horizon Distance (cyclohexanes)

Page 15: Getting Past Diversity in Assessing  Virtual Library Designs

Homolosine Projection

source: Cartography Laboratory Indiana State University

www.indstate.edu/gga/gga_cart

Page 16: Getting Past Diversity in Assessing  Virtual Library Designs

CH2 NH

S CCl3

O

O

X

F

F

NH

S

O

O

XCH3

CH3O

CH2 NH

S

O

O

X

N

HOCH2

ON

Cl

NH

S CCl3

O

O

XN

NH

S CH2CH3

O

O

XSHO

O

O

NH

S CH2CH3Cl

O

O

XOMeMeO

CH2 NH

S

O

O

X

NO

NOCF2H

25

26

33

34

35

36

37

PCA NLMwith Horizon

Page 17: Getting Past Diversity in Assessing  Virtual Library Designs

CH2 NH

S

O

O

X

OMe

H3C

Br

Cl

NH

S

O

O S NOX

O

CH2 NH

S

O

O

X

CH3

HO

Br

MeO

NH

S

O

O S NNXCH2O CH3

CF3

O

HO

NH

S

O

O

X OCF3

OEtEtO

NH

S

O

O S ClXCH2F

Cl

CH2 NH

S

O

O

XO

N

CH2 NH

S

O

O

XO

O

CH2 NH

S

O

O

X N

CF3

FN

Cl

CH3

CH3

CH2 NH

S

O

O

X NO

CH3

CH3

NOH3C

O

NH

S

O

O

XS

CH3

CH3

CH3O

22

23

24

27

28

29

30

31

32

38

39

PCA NLMwith Horizon

Page 18: Getting Past Diversity in Assessing  Virtual Library Designs

cherry pickingfour blockssingle block

42

45

4648

51

53

NH

S

O

O S NNXCH2

CH3

CF3CH3

CH3

Me2NCH2

NH

S CH2CH2CH2CH3

O

O

XN

NH OH

NH

S

O

O S

OMeCOOMe

CH3

CH3

H3CX

NH

S

O

O

S

N

N

X

OH

O

Cl

NH

S

O

O

XMe2N

NN

Cl

CH3

CH3

NH

S CH2

O

O

X

OH

Et2N

Comparison of Sub-Libraries

Page 19: Getting Past Diversity in Assessing  Virtual Library Designs

cherry pickingfour blockssingle block

41

42

43

44

45

46

47

48

49

50

51

53

54

55

CH2 NH

S

O

O

X

FN

N

Cl

F

Br

NH

S

O

O S NNXCH2

CH3

CF3CH3

CH3

Me2NCH2

NH

S CH2CH2CH2CH3

O

O

XN

NH OH

CF3CF2CH2 NH

S CH2CH2CH2Cl

O

O

X

NH

S

O

O SXCH2

O

ON

N

SMe

NH

S

O

O SX

OCH2

CH3

N

NH

S

O

O

X

F

SiMeO

MeO

CH3F

Br

NH

S

O

O S

OMeCOOMe

CH3

CH3

H3CX

NH

S

O

O

S

N

N

X

OH

O

Cl NH

S

O

O NX

NCl

CF3

OH3C

NH

S

O

O

XMe2N

NN

Cl

CH3

CH3

NH

S

O

ONN

X

CH3Cl

O

OMe

CH2 NH

S CH2CH2CH2Cl

O

O

XNOH3C

O

NH

S CH2

O

O

X

OH

Et2N

Comparison of Sub-Libraries

Page 20: Getting Past Diversity in Assessing  Virtual Library Designs

cherry pickingfour blockssingle block

40

42

44

45

46

47

48

49

51

52

55

NH

S

O

O S NNXCH2

CH3

CF3CH3

CH3

Me2NCH2

NH

S

O

O

XN

O

NH

S

O

O SX

OCH2

CH3

N

NH

S

O

O

X

F

SiMeO

MeO

CH3F

Br

NH

S

O

O S

OMeCOOMe

CH3

CH3

H3CX

NH

S

O

O

S

N

N

X

OH

O

Cl

NH

S

O

O NX

NCl

CF3

OH3C

NH

S

O

O

XMe2N

NN

Cl

CH3

CH3

NH

S

O

ONN

X

CH3Cl

O

OMe

NH

S CH2

O

O

X

OH

Et2N

NH

S

O

O S NOX

O

CH3

CH3

H3CO

Comparison of Sub-Libraries

Page 21: Getting Past Diversity in Assessing  Virtual Library Designs

Acknowledgements

NIH SBIR grant 1R43GM58919 David Patterson

• Sr. Fellow

Fred Soltanshahi• Technologist

Trevor Heritage, VP Software R&D

1999 Tripos, Inc.

Page 22: Getting Past Diversity in Assessing  Virtual Library Designs

Take-home:fingerprint similarity

isbiologically relevant (good neighborhood

behavior)