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Novel methods for visual interpretation of biological screening data

Novel methods for visual interpretation of biological screening data. Columbus Molecular Software, Inc. 30 March 2000. Objectives LeadScope  Components Structural feature hierarchy Structure analysis engine Data visualization and dynamic querying Statistics Applications

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Novel methods for visual interpretation of biological screening data

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  1. Novel methods for visual interpretation of biological screening data Columbus Molecular Software, Inc. 30 March 2000

  2. Objectives LeadScope Components Structural feature hierarchy Structure analysis engine Data visualization and dynamic querying Statistics Applications Future directions Overview of presentation

  3. Tool to interpret High Throughput Screening (HTS) results Analyze very large sets of structures and properties Accessible all scientist that wish to analyze HTS data Computational chemists Medicinal chemists HTS Objectives

  4. Easy-to-use GUI “Window explorer” metaphore Interactive sliders to query by properties Visualization using familiar terms and graphical objects Chemical normalization and analysis Chemical informatics for non-experts

  5. Intellectually derived hierarchy of structural features Currently over 27,000 unique features Based on analyses of drugs Structural feature hierarchy

  6. Query Structure for pyrrole, 3-amino(NH2) andMatching Substructure

  7. Portion of the Pyridine Hierarchy pyridine (level 1) pyridine, 1-R- (level 2) pyridine, 2-R- pyridine, 3-R- pyridine, 3-(alkenyl, acyc)- (level 3) pyridine, 3-(alkenyl, cyc)- pyridine, 3-alkoxy- pyridine, 3-(p-alkyl)- pyridine, 3-(s-alkyl)- pyridine, 3-(t-alkyl)- pyridine, 3-(alkyl, acyc)- pyridine, 3-alkylamino- pyridine, 3-alkylcarbonyl- pyridine, 3-(alkyl, cyc)- pyridine, 3-alkylthio-

  8. Heterocycles Bases, nucleosides Naphthalenes Benzenes Natural products Carbocycles Peptidomimetics Carbohydrates Pharmacophores Elements Protective groups Functional groups Spacer groups User-defined features Major structural classes

  9. Analysis of Dopamine Total of 60 terms

  10. Substructure searching incorporating Aromaticity analysis Tautomerism analysis Generic group analysis Check on atom environment No need to be expert in chemical conventions Structure analysis engine

  11. Use of a variety of visualizations to represent sets of structures Scatter plots, histograms Use of sliders to dynamically query dataset Based around techniques developed at the University of Maryland (Shneiderman and Ahlberg) † Data visualization and dynamic querying †Shneiderman, Ben. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction. 3rd edition. Addison-Wesley.

  12. Uses statistical techniques to determine classes with unusually high numbers of active compounds Uses all the data (active and inactive) Color codes sets according to their correlation Statistical analysis

  13. HTS Data Analysis Selectivity Lead Optimization Monomer Selection Diversity Analysis Structural Alerts Applications of LeadScope

  14. Demonstration HTS Analysis Monomer Selection Diversity

  15. Chemoinformatic tool for non-experts Easy-to-use interactive interface Pre-defined analysis of chemical space Behind-the-scenes chemically intelligent analysis Tool encourages interactive participation Summary

  16. LeadScope Enterprise Client-server Access to entire corporate structure database Links to property data in ORACLE Future directions

  17. CMS Paul Blower Wayne Johnson Julie Roberts Kevin Cross Glenn Myatt Allen Richon Pfizer Mark Lord Mike Snarey Tony Woods Paul Edwards Acknowledgements

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