1 miklós vargyas may, 2005 compound library annotation

1

Miklós Vargyas

May, 2005

Compound Library Annotation

2

Slide 2

Compound Library Annotation — UGM 2005


Overview of the Screen PackageVirtual screening

Optimized dissimilarity metrics

Clustering

Library Annotation – a real-life application of the Screen toolApproach #1:

• use command line applications

Approach #2:

• API programming

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Slide 3


Overview of the Screen Package

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000

queries

targets

hypothesis fingerprint

metric

target fingerprints

010001010001110101000011000010100001001100001010000000010010000000011011100111011111101000001000100001101101100000001001101000000100010100110100010000000010000000010010000000100100001000101000010111010011010101011111100001000001111110001000010000100010100000010001000101000101001000000000000010100000100001000001000000000100010100010100000000000000101000010010000000000100000000000000010101010111110011111010000000000001101010001110010000110010100001000101000110000100000110000000000100010000001100000000011000000000000100000000010000100000000000001010100000000100000100100000

0101110100110101010111111000010000011111100010000100001000101000

Virtual hits

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Slide 4


Need for Optimization

0.47 0.55

0.57

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Slide 5


Optimized Metrics

22, 1),(

iiii yx

iiiyx

iiiasymmetricweighted

Euclidean yxwyxwyxD

i iiii iiii ii iiii i

i iiiasymmetricscaledTanimoto

yxsyxsyyxsx

yxsyxD

),min(),min(1),min(

),min(1),(,

1,0 asymmetry factor

Nis scaling factor

1,0 asymmetry factor

1,0iw weights

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Slide 6


Improved Similarity by Optimization

0.47 0.55

0.57

0.20

0.28

0.06

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Slide 7


Enrichment by Optimization

1

10

100

1000

10000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Number of Active Hits

Num

ber

of H

its

Tanimoto Euclidean Optimized Ideal

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Slide 8


Clustering

8 active compound sets• ACE inhibitors

• angiotensin 2 antagonists

• D2 antagonists

• delta antagonists

• FTP antagonists

• mGluR1 antagonists

• Thrombin inhibitors

• 5-HT3-antagonists

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Slide 9


Ward Centroids

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Slide 10


Maximum Common Substructure Clustering

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Slide 11



ActACE=0.5 Actß2=0.98

ActACE=0.78 Actß2=0.45

Annotate library: predicted activity in some therapeutic areas

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Slide 12


Similarity Based Activity Prediction

ActACE=0.55

Actß2=0.98

Use sets of known actives to predict activity of compounds

0101110100110101010111111000010000011111100010000100001000101000

0101110100110101010111111000010000011111100010000100001000101000

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Slide 13


Approach #1: Off the Shelf ChemAxon Tools

Parameter settingPharmacophore fingerprint

Tanimoto dissimilarity metric

Median Pharmacophore Hypothesis

screenmd library.sdf ace.sdf \

–o SDF annotated-library.sdf \

-k PF –M Tanimoto –H Median

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Slide 14


Single Active Family

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Slide 15


Multiple Active Families

screenmd library.sdf ace.sdf \

-o SDF lib-ace.sdf -k PF –M Tanimoto –H Median

screenmd lib-ace.sdf beta2.sdf \

-o SDF lib-ace+beta2.sdf -k PF –M Tanimoto –H Median

screenmd lib-ace+beta2.sdf delta.sdf \

-o SDF lib-ace+beta2+delta.sdf -k PF –M Tanimoto \

–H Median

screenmd lib-ace+beta2+delta.sdf D2.sdf \

-o SDF lib-ace+beta2+delta+D2.sdf -k PF –M Tanimoto \

–H Median

...

...

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Slide 16


Annotated Library File

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Slide 17


Approach #2: Using ChemAxon JChem API

API programming – custom solutionPharmacophoreFingerprint and the MolecularDescriptor class

hierarchy

Tanimoto dissimilarity calculation

Median Hypothesis calculation

Description generation for structure in SDfile

Writing structures in SDfile

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Slide 18


MolecularDescriptor class hierarchy

Molecular Descriptor

Chemical

Fingerprint

Pharmacophore

Fingerprint

MACCS

BCUT

CUSTOM

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Slide 19


MolecularDescriptor Sets

Molecular Descriptor Set

Molecular Descriptor 1

(e.g. CFp)


(e.g. PFp)


(e.g. logP)

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Slide 20


Dissimilarity Calculation

MDSet s1 =

MDSet.newInstance( new String[]{“CF”,”PF”,”LogP”} )

MDSet s2 =

MDSet.newInstance( new String[]{“CF”,”PF”,”LogP”} )

. . . Generate s1 and s2 somehow . . .

System.out.println( “dissimilarity(s1,s2) = “ +

s1.getDissimilarity( s2 ) );

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Slide 21


Tanimoto Dissimilarity Calculation

MDSet s1 =

MDSet.newInstance( new String[]{“PF”} )

MDSet s2 =

MDSet.newInstance( new String[]{“PF“} )


PharmacophoreFingerprint pf1 = s1.getDescriptor(0);

PharmacophoreFingerprint pf2 = s2.getDescriptor(0);

System.out.println( “Tanimoto(pf1,pf2) = “ +

pf1.getTanimoto( pf2 ) );

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Slide 22


Median Hypothesis Calculation

MDSet s1 =

MDSet.newInstance( new String[]{“PF”} )

MDSet s2 =

MDSet.newInstance( new String[]{“PF“} )


MDHypothesisGenerator medianHypoGenerator = MDHypothesisCreator.create( "Median" );

medianHypoGenerator.add( s1 );

medianHypoGenerator.add( s2 );

MDSet hypothesis = medianHypoGenerator.generate();

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Slide 23


Reading Descriptors from Structure File

MDFileReader inputReader

= new MDFileReader( “library.sdf”, MDSet.newInstance( new String[]{"PF"} ) );

MDSet mdRead = inputReader.next();

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Slide 24


Writing structures in SDfile

MolExporter outputWriter =

new MolExporter( new PrintStream(

new BufferedOutputStream(

new FileOutputStream( fileName ))), "sdf");

Molecule m = getAMolecule();

outputWriter.write( m );

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Slide 25


LibAnnot class

Full source code avaialable at http://www.chemaxon.com/

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Slide 26


Future plans

• New MolecularDescriptors (e.g. 3D Pharmacophore)

• Non-hierarchical MCS clustering, better GUI

• Library diversity estimation

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Slide 27


Summary

• Screen+JKlustor for optimized virtual screening and hit set profiling

• Library annotation by screenmd

• Library annotation by custom program

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Slide 28


Acknowledgements and Credits

• JKlustor developed by Ferenc Csizmadia et al

• Optimizer developed by Zsuzsa Szabó

• PMapper developed by Szilárd Dóránt, Nóra Máté

• Pharmacophore definitions by György Pirok

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Slide 29


Máramaros köz 3/a Budapest, 1037Hungary

[email protected]

www.chemaxon.com

Thank you for your attention

1 miklós vargyas may, 2005 compound library annotation

Documents

optimization slide

api programming slide

active compound

screen tool approach

virtual hits

ace inhibitors angiotensin

reallife application

ward centroids