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SEURAT: A Tool for Navigating SAR

SEURAT: A Tool for Navigating SAR . Authors: J Baxendale, K Scott, N Southall, T Nguyen, L Verma, H. Wolters, Ajay Celera Genomics 45 W. Gude Drive Rockville, MD 20850 ChemAxon UGM 19 th May 2005. What is SEURAT?. S tructured E xploratory U tility for Ra tional T herapeutics

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SEURAT: A Tool for Navigating SAR

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  1. SEURAT: A Tool for Navigating SAR Authors: J Baxendale, K Scott, N Southall, T Nguyen, L Verma, H. Wolters, Ajay Celera Genomics 45 W. Gude Drive Rockville, MD 20850 ChemAxon UGM 19th May 2005

  2. What is SEURAT? • Structured Exploratory Utility for Rational Therapeutics • Named after Georges Seurat, founder of Pointillism • Brings together many data points into complete picture • Retrieves compounds and assay data from corporate DB • Computes compound properties • Performs SAR analysis via holistic views

  3. Why SEURAT? • Diverse set of users • Existing retrieval GUI (RS3) not well utilized • Database storage fine but GUI cumbersome • Users forced to hand craft presentations • Lack of incentive to capture data • Commercial and free software did not meet our needs

  4. Reasons for Successful Adoption • Highly knowledgeable full-time development team members • Simple things simple, hard things possible • Visual query interface that allows for incremental complication • Facilitates presenting ideas to colleagues • Orthogonal Perspectives from Tables • Table based (detail) views linked to holistic (pattern) views • Something for everyone

  5. Simple things simple, hard things possible

  6. Incremental Complication “Decision script paradigm”1 • 1J. Med. Chem 1999 42 (26) 5369-5389, J. Med. Chem. 2002 45 (6) 1300-1312

  7. Incremental Complication “Replay Script Each Time”

  8. Facilitates presenting ideas to colleagues2 • Toolbar to easily control font, header height, grouping, cell height and cell detail • Many export options to get the data into a more familiar form. As hard as we tried this was not avoidable • Columns can be renamed, re-ordered and hidden. Rows can also be hidden • 2Nature Biotechnology (23) (3) March 2005

  9. Orthogonal Perspectives from Tables - Holistic Views • Holistic view that considers series rather than single compounds • Intelligent grouping of assays • Dynamic filtering • Extends 2-D views to higher dimensions using size, color, order and label • Context of structure maintained using Marvin • Many views (Heat Map, Scatter Plot, Scatter Plot Matrix, Time Series and Graphs) are linked. Selection or filtering in one is coordinated with all other views in real time • Can support a reasonable number of data points ~100,000 before rendering starts to slow • Can be sped up using Agile 2D

  10. Orthogonal Perspectives from Tables - Holistic Views1,3 • 1J. Med. Chem 1999 42 (26) 5369-5389, J. Med. Chem. 2002 45 (6) 1300-1312 • 3Views developed on top of the InfoVis toolkit ver 0.8 alpha 2 http://ivtk.sourceforge.net/

  11. Orthogonal Perspectives from Tables - SAR Matrix1 • Alternate but linked views of results: • Tabular and most familiar • Matrix view with zooming and cell coloring for emphasis of “hot spots” • Can still organically grow the tabular view to better inform the matrix view • We will see a demo later of SAR capabilities using a public dataset1 • 1J. Med. Chem 1999 42 (26) 5369-5389, J. Med. Chem. 2002 45 (6) 1300-1312

  12. Something for Everyone Other Views and ChemAxon Uses Crystal Structure (Gleevec)4 Available Vendor Compounds Lipinski Rule Prediction1 Functional Group Replacement Kinome Map Viewer • 1J. Med. Chem 1999 42 (26) 5369-5389, J. Med. Chem. 2002 45 (6) 1300-1312 • 4J.Biol.Chem 279 pp. 55827 (2004)

  13. Core Technology ConsiderationsChemAxon • ChemAxon’s Marvin • All rendering • Local sub-structure search emphasis • Structure walking for SAR analysis • Property predictions • Oracle Cartridge • Substructure searches on vendor supplied libraries • Library design and in house molecules for lead optimization.

  14. Core Technology ConsiderationsOther • Use of Web Start was key to allowing deployment of application to large and growing user base. • Application rather than applet for richness of interface. • Java because we need to support several platforms • AIX, Linux, Max and Windows • RMI to bridge into applications lacking a Java binding • home grown property predictors • SAR searches • similarity sorting

  15. Demo Time • SAR • Presentation and collaboration enablers • Visualization extensions • Questions

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