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Published on January 9, 2008

Author: Teresa1


Compound Library Annotation:  Miklós Vargyas May, 2005 Compound Library Annotation Compound Library Annotation:  Compound Library Annotation Overview of the Screen Package Virtual screening Optimized dissimilarity metrics Clustering Library Annotation – a real-life application of the Screen tool Approach #1: use command line applications Approach #2: API programming Overview of the Screen Package:  Overview of the Screen Package 0000000100001101000000101010000000000110000010000100001000001000 0100010110010010010110011010011100111101000000110000000110001000 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0100011100011101000100001011101100110110010010001101001100001000 0101110100110101010111111000010000011111100010000100001000101000 0100010100111101010000100010000000010010000010100100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010011000000000000000000010100000010000000000000000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0100010100010100000000100000000000010000000000000100001000011000 0001000100001100010010100000010100101011100010000100001000101000 0100011100010100010000100001001110010010000010001100000000101000 0101010100010100010100100000000000010010000010010100100100010000 queries targets hypothesis fingerprint metric target fingerprints 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0101110100110101010111111000010000011111100010000100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0101110100110101010111111000010000011111100010000100001000101000 Virtual hits Need for Optimization:  Need for Optimization 0.47 0.55 0.57 Optimized Metrics:  Optimized Metrics asymmetry factor scaling factor asymmetry factor weights Improved Similarity by Optimization:  Improved Similarity by Optimization 0.47 0.55 0.57 0.20 0.28 0.06 Enrichment by Optimization:  Enrichment by Optimization Clustering:  Clustering 8 active compound sets ACE inhibitors angiotensin 2 antagonists D2 antagonists delta antagonists FTP antagonists mGluR1 antagonists Thrombin inhibitors 5-HT3-antagonists Ward Centroids:  Ward Centroids Maximum Common Substructure Clustering:  Maximum Common Substructure Clustering Compound Library Annotation:  Compound Library Annotation ActACE=0.5 Actß2=0.98 ActACE=0.78 Actß2=0.45 Annotate library: predicted activity in some therapeutic areas Similarity Based Activity Prediction:  Similarity Based Activity Prediction ActACE=0.55 Actß2=0.98 Use sets of known actives to predict activity of compounds 0101110100110101010111111000010000011111100010000100001000101000 0101110100110101010111111000010000011111100010000100001000101000 Approach #1: Off the Shelf ChemAxon Tools:  Approach #1: Off the Shelf ChemAxon Tools Parameter setting Pharmacophore fingerprint Tanimoto dissimilarity metric Median Pharmacophore Hypothesis screenmd library.sdf ace.sdf \ –o SDF annotated-library.sdf \ -k PF –M Tanimoto –H Median Single Active Family :  Single Active Family Multiple Active Families:  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 ... ... Annotated Library File:  Annotated Library File Approach #2: Using ChemAxon JChem API:  Approach #2: Using ChemAxon JChem API API programming – custom solution PharmacophoreFingerprint and the MolecularDescriptor class hierarchy Tanimoto dissimilarity calculation Median Hypothesis calculation Description generation for structure in SDfile Writing structures in SDfile MolecularDescriptor class hierarchy:  MolecularDescriptor class hierarchy Molecular Descriptor Chemical Fingerprint Pharmacophore Fingerprint MACCS BCUT CUSTOM MolecularDescriptor Sets:  MolecularDescriptor Sets Molecular Descriptor Set Molecular Descriptor 1 (e.g. CFp) Molecular Descriptor 2 (e.g. PFp) Molecular Descriptor 3 (e.g. logP) Dissimilarity Calculation:  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 ) ); Tanimoto Dissimilarity Calculation:  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 ) ); Median Hypothesis Calculation:  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(); Reading Descriptors from Structure File:  Reading Descriptors from Structure File MDFileReader inputReader = new MDFileReader( “library.sdf”, MDSet.newInstance( new String[]{"PF"} ) ); MDSet mdRead =; Writing structures in SDfile:  Writing structures in SDfile MolExporter outputWriter = new MolExporter( new PrintStream( new BufferedOutputStream( new FileOutputStream( fileName ))), "sdf"); Molecule m = getAMolecule(); outputWriter.write( m ); LibAnnot class:  LibAnnot class Full source code avaialable at Future plans:  Future plans New MolecularDescriptors (e.g. 3D Pharmacophore) Non-hierarchical MCS clustering, better GUI Library diversity estimation Summary:  Summary Screen+JKlustor for optimized virtual screening and hit set profiling Library annotation by screenmd Library annotation by custom program Acknowledgements and Credits:  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 Thank you for your attention:  Máramaros köz 3/a Budapest, 1037 Hungary Thank you for your attention

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