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Miklós Vargyas
May, 2005
Compound Library Annotation
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Slide 2
Compound Library Annotation — UGM 2005
Compound Library Annotation
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
Compound Library Annotation — UGM 2005
Overview of the Screen Package
000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000
queries
targets
hypothesis fingerprint
metric
target fingerprints
010001010001110101000011000010100001001100001010000000010010000000011011100111011111101000001000100001101101100000001001101000000100010100110100010000000010000000010010000000100100001000101000010111010011010101011111100001000001111110001000010000100010100000010001000101000101001000000000000010100000100001000001000000000100010100010100000000000000101000010010000000000100000000000000010101010111110011111010000000000001101010001110010000110010100001000101000110000100000110000000000100010000001100000000011000000000000100000000010000100000000000001010100000000100000100100000
0101110100110101010111111000010000011111100010000100001000101000
Virtual hits
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Slide 4
Compound Library Annotation — UGM 2005
Need for Optimization
0.47 0.55
0.57
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Slide 5
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Improved Similarity by Optimization
0.47 0.55
0.57
0.20
0.28
0.06
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Slide 7
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Ward Centroids
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Slide 10
Compound Library Annotation — UGM 2005
Maximum Common Substructure Clustering
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Slide 11
Compound Library Annotation — UGM 2005
Compound Library Annotation
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
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Single Active Family
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Slide 15
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Annotated Library File
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Slide 17
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
MolecularDescriptor class hierarchy
Molecular Descriptor
Chemical
Fingerprint
Pharmacophore
Fingerprint
MACCS
BCUT
CUSTOM
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Slide 19
Compound Library Annotation — UGM 2005
MolecularDescriptor Sets
Molecular Descriptor Set
Molecular Descriptor 1
(e.g. CFp)
Molecular Descriptor 2
(e.g. PFp)
Molecular Descriptor 3
(e.g. logP)
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Slide 20
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Tanimoto Dissimilarity Calculation
MDSet s1 =
MDSet.newInstance( new String[]{“PF”} )
MDSet s2 =
MDSet.newInstance( new String[]{“PF“} )
. . . Generate s1 and s2 somehow . . .
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
Compound Library Annotation — UGM 2005
Median Hypothesis Calculation
MDSet s1 =
MDSet.newInstance( new String[]{“PF”} )
MDSet s2 =
MDSet.newInstance( new String[]{“PF“} )
. . . Generate s1 and s2 somehow . . .
MDHypothesisGenerator medianHypoGenerator = MDHypothesisCreator.create( "Median" );
medianHypoGenerator.add( s1 );
medianHypoGenerator.add( s2 );
MDSet hypothesis = medianHypoGenerator.generate();
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Slide 23
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
LibAnnot class
Full source code avaialable at http://www.chemaxon.com/
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Slide 26
Compound Library Annotation — UGM 2005
Future plans
• New MolecularDescriptors (e.g. 3D Pharmacophore)
• Non-hierarchical MCS clustering, better GUI
• Library diversity estimation
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Slide 27
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
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
Compound Library Annotation — UGM 2005
Máramaros köz 3/a Budapest, 1037Hungary
www.chemaxon.com
Thank you for your attention