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

JKlustor Chemical clustering by similarity and structure

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

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

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

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

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

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

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

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

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)

Ward Clustering Example Centroids

Ward Clustering Example Cluster of the D2 antagonists

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

Bemis-Murcko frameworks

Bemis-Murcko frameworks

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

LibraryMCS Identifies the largest subgraph shared by several molecular structures

LibraryMCS: Hierarchical MCS

SAR table view

R-group decomposition

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)

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

Clustering performance comparison

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