1. JKlustor clustering chemical libraries presented by … maintained by Miklós Vargyas
Last update: 25 March 2010

2. JKlustor
Chemical clustering by similarity and structure

3. JKlustor Description of the product Availability
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

4. 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

5. Benefits Versatile Intuitive
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

6. Similarity based clustering
Hierarchical
Ward
Non-hierarchical
Sphere exclusion
k-means
Jarvis-Patrick

7. 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 < 100K structures

8. 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

9. 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 <100Ks of structures

10. 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

11. 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
ChemAxon’s 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)

12. Ward Clustering Example
Centroids

13. Ward Clustering Example
Cluster of the D2 antagonists

14. Structure based clustering
Non-hierarchical
Bemis-Mucko frameworks
Hierarchical
LibraryMCS

15. Bemis-Murcko frameworks

16. Bemis-Murcko frameworks

17. 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

18. LibraryMCS
Identifies the largest subgraph shared by several molecular structures

19. LibraryMCS: Hierarchical MCS

20. SAR table view

21. R-group decomposition

22. 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)

23. LibraryMCS integration at Abbott
“Clustering for the masses…”,
presented by Derek Debe at ChemAxon’s US UGM, Boston, 2008

24. Clustering performance comparison

25. Jklustor roadmap In the development pipeline Planned Blue sky
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