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Interactive Datamining of Large-Scale Screening Datasets

Interactive Datamining of Large-Scale Screening Datasets. Frank Oellien, Wolf D. Ihlenfeldt Computer-Chemie-Centrum Universit y Erlangen-Nuremberg. Klaus Engel, Thomas Ertl Visualization and Interactive Systems Group Universit y Stuttgart. Overview.

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Interactive Datamining of Large-Scale Screening Datasets

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  1. Interactive Datamining of Large-Scale Screening Datasets Frank Oellien, Wolf D. Ihlenfeldt Computer-Chemie-Centrum University Erlangen-Nuremberg Klaus Engel, Thomas ErtlVisualization and Interactive Systems Group University Stuttgart

  2. Overview • Multi-variate and multi-dimensional datasets • Motivation • Information Visualization Techniques • Examples (ChemCodes Inc., NCI) • Demo

  3. Overview • Multi-variate and multi-dimensional datasets • Motivation • Information Visualization Techniques • Examples (ChemCodes Inc., NCI) • Demo

  4. Chemical data 18000000 16000000 Merck Katalog Synopsys PG 14000000 ACX 12000000 NCI DTP 10000000 ChemInform 8000000 Spresi 6000000 Beilstein 4000000 CAS Current datasets 2000000 0

  5. Multi-Variate and Multi-Dimensional Numeric Datasets Today • Change in chemical synthesis technology • new technologies (HTS, combinatorial synthesis) • experiments generate terabytes of data per year • development of data mining and visualization tools could not keep pace • most critical bottleneck in R&D today ! • tools for interactive mining and information visualization are needed

  6. Tools for Interactive Visualization of Multi-Variate and Multi-Dimensional Data • Standard applications • barchart, 2D and pseudo 3D scatter plots, molecular spreadsheets • limited to small subsets • platform-dependent • Our goal: applications that are • simple to use • allow straightforward interpretation of results • generalized access to tabular numeric data • platform-independent

  7. Overview • Multi-variate and multi-dimensional datasets • Motivation • Information Visualization Techniques • Examples (ChemCodes Inc., NCI) • Demo

  8. 3D Tools for Interactive Information Visualization • Information Visualization Applications that uses 3D capabilities of modern clients • Glyph-based InfVis approaches • Volume-based InfVis approaches

  9. Glyph-based InfVis Tools • 3 orthogonal axes • color • shape • size • transparency • surface effects • animation • up to ~100 Glyphs

  10. Java/Java3D InfVis Applet • Java3DCanvas • Tool Panel • (filters, selection tools, details) • ControlPanel

  11. Java/Java3D InfVis Applet3D Render Panel • 3D Glyphs • 3D Barchart

  12. Dynamic Filter Tools • Selection Tools • Detail Tools Java/Java3D InfVis Applet3D Tool Panel

  13. Java/Java3D InfVis Applet3D Control Panel

  14. Advantages of Volume-based InfVis Tools • Databases with millions of data points • Glyph-based InfVis approaches • produce millions of geometricprimitives • interactive visualization not possible • Volume-based InfVis approaches • can handle large number of data points • interactive visualization using low-cost graphics hardware is possible

  15. Overview • Multi-variate and multi-dimensional datasets • Motivation • Information Visualization Techniques • Examples (ChemCodes Inc., NCI) • Demo

  16. ChemCodes Reaction Database • 100 most important FGs ~75% chemistry • 100 standard reactions • Limits of standard reactions • Functional Group Compatibility • Generating Rules • Goal: Analysis of the reaction space

  17. ChemCodes - Reaction Optimization I • Goal: Reaction Optimization: > 95% Yield • 7 Dimensions:reagent, solvent, time, temperature,stoichiometry,reagent order,FG-compatibility

  18. ChemCodes - Reaction Optimization II

  19. ChemCodes - Reaction Planning • FunctionalGroupCompatibilityCheck

  20. Example 2: NCI Anti-tumor / Anti-viral Database • Initiated in April 1990 (modified 1994) • ~ 250.000 compounds • ~ 30.000 with anti-tumor screening data • Enhanced NCI Database Browser • > 30 different molecular properties • up to 23 3D conformers per compound

  21. Lead Compound Discovery II

  22. Lead Compound Discovery II

  23. Overview • Multi-variate and multi-dimensional datasets • Motivation • Information Visualization Techniques • Examples (ChemCodes Inc., NCI) • Demo

  24. Acknowledgment • Prof. Johann GasteigerComputer-Chemie-CentrumUniversity of Erlangen-Nuremberg • Prof. Thomas Ertl, Dipl. Inf. Klaus Engel Visualization and interactive SystemsUniversity of Stuttgart • Dr. Patrick Kiser, Dr. Gary Eichenbaum ChemCodes Inc. • Marc NicklausLaboratory of Medicinal ChemistryNCI, NIH • Deutsche Forschungsgemeinschaft

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