JKlustor clustering chemical libraries presented by

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JKlustor clustering chemical librariespresent
ed by maintained by Miklós Vargyas
Last update 25 March 2010
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JKlustor

• Chemical clustering by similarity and structure

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JKlustor
Description of the product
JKlustor performs similarity and structure based
clustering of compound libraries and focused sets
in both hierarchical and non-hierarchical fashion.
Availability

• part of Jchem
• IJC (parts)
• server version (accessible via API)
• batch application programs
• HTML user interface
• one desktop application with GUI
• GUI is available as an applet

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Summary of key features
Summary of key features

• Wide range of methods
• Unsupervised, agglomerative clustering
• Hierarchical and non-hierarchical methods
• Similarity based and structure based techniques
• Flexible search options
• Tanimoto and Euclidean metrics, weighting
• Maximum common substructure identification
• chemical property matching including atom type,
  bond type, hybridization, charge
• Interactive display
• interactive hierarchy browser (dendrogram viewer)
• SAR-table
• R-table
• Efficient
• performance of tools varies between linear and
  quadratic scale

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Benefits

• Versatile
• Choose the most appropriate method to the
  clustering problem
• Combine methods to achieve best results
• Use your trusted molecular descriptors in
  similarity calculation
• Easy integration in corporate discovery pipelines
• Cluster chemical files directly no need to import
  structures in database
• Intuitive
• Cluster formation is self-explanatory

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Similarity based clustering

• Hierarchical
• Ward
• Non-hierarchical
• Sphere exclusion
• k-means
• Jarvis-Patrick

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Ward Clustering Features

• Ward's minimum variance method results in tight,
  well separated clusters
• Murtagh's reciprocal nearest neighbor (RNN)
  algorithm to speed it up
• quadratic scaling of running time (with respect
  to number of input structures)
• memory consumption scales linearly
• best used with smaller sets (like focused
  libraries), copes with lt 100K structures

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Sphere Exclusion Clustering Features

• based on fingerprints and/or other numerical data
• running time linear with respect to number of
  input structures
• memory scales sub-linearly
• can easily cope with 1Ms of structures
• suitable for diverse subset selection

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k-means Clustering Features

• based on fingerprints and/or other numerical data
• minimises variance within each clusters
• number of clusters can directly be controlled
• finds the centre of natural clusters in the input
  data
• running time scales exponentially with respect to
  number of input structures
• can cope with lt100Ks of structures

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Jarp Clustering Features

• variable-length Jarvis-Patrick clustering
• based on fingerprints and/or other numerical data
• takes structures/fingerprint and data values from
  either files or form database tables
• running time scales better than quadratic but
  worse than linear (with respect to number of
  input structures)
• memory scales linearly
• Jarp can cope with 100Ks of structures
• depending on data and parameters may create large
  number of singletons

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Ward Clustering Example

• 8 different sets of know active compounds mixed
  together
• 5-HT3-antagonists
• ACE inhibitors
• angiotensin 2 antagonists
• D2 antagonists
• delta antagonists
• FTP antagonists
• mGluR1 antagonists
• thrombin inhibitors
• ChemAxons 2D Pharmacophore fingerprint was
  generated
• Fingerprints of the mixture were clustered by
  Ward
• 9 clusters were formed
• 8 centroids (cluster representative element)
  corresponded to the 8 activity classes
• 1 was a singleton
• All 8 real clusters contained structures only
  from the activity class of the centroid (over 95
  true positive classification)

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Ward Clustering Example
Centroids
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Ward Clustering Example
Cluster of the D2 antagonists
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Structure based clustering

• Non-hierarchical
• Bemis-Mucko frameworks
• Hierarchical
• LibraryMCS

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Bemis-Murcko frameworks
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Bemis-Murcko frameworks
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Bemis-Murcko frameworks features

• based on structure of molecules
• cluster formation is apparent, visual, meets
  human expectations
• running time linear with respect to number of
  input structures
• memory scales sub-linearly
• can easily cope with 1Ms of structures
• suitable for quick overview of very large sets
• spots scaffold hops

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LibraryMCS
Identifies the largest subgraph shared by several
molecular structures
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LibraryMCS Hierarchical MCS
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SAR table view
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R-group decomposition
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LibraryMCS features

• based on structure of molecules
• cluster formation is apparent, visual, meets
  human expectations
• running time near-linear with respect to number
  of input structures
• can cope with 100K-200K of structures
• suitable for very thorough analysis
• spots scaffold hops
• substituent-activity (property analysis)

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LibraryMCS integration at Abbott
Clustering for the masses, presented by Derek
Debe at ChemAxons US UGM, Boston, 2008
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Clustering performance comparison
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Jklustor roadmap

• In the development pipeline
• Bemis-Murcko generalisations
• IJC integration
• KNIME integartion
• New GUI
• Manual clustering
• Multiple class membership
• Disconnected MCS (MOS)
• Planned
• PipelinePilot integration
• Spotfire integration
• JChemBase, JChemCartridge integration
• JC4XLS integration
• Blue sky
• Multitouch gestures
• LibraryMCS for 1M compound libraries