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The Discovery Informatics Framework

The Discovery Informatics Framework. Delivering the Integration Promise. Pat Rougeau President and CEO MDL Information Systems, Inc. American Chemical Society Meeting. San Francisco, CA March 27, 2000. Synthesis. Inventory. Candidates. Lead. Repeat. And Repeat. Proof. X X X.

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The Discovery Informatics Framework

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  1. The Discovery Informatics Framework DeliveringtheIntegrationPromise Pat Rougeau President and CEO MDL Information Systems, Inc. American Chemical Society Meeting San Francisco, CA March 27, 2000

  2. Synthesis Inventory Candidates Lead Repeat And Repeat Proof X X X Methodology (algor.) Early Validation safe new effective economical XX X Descriptors (chem., physicochem. etc.) Proposals Integrating informatics into the Discovery process Targets Standard Test Set Hypothesis

  3. Targets Journals Journals Journals Journals Synthesis Inventory Candidates Standard Test Set Hypothesis Lead Proof X X X Methodology (algor.) Early Validation safe new effective economical XX X Descriptors (chem., physicochem. etc.) Proposals Information sources for the Discovery process DB DB DB DB DB

  4. Prioprietary information is exploding • High Throughput Screening • Combinatorial Chemistry • Genomics • Partnerships and Outsourcing • Mergers

  5. Public information is more accessible • Globalized research • Globalized publishing • Electronic media • World Wide Web • Patent literature

  6. Drive up capability Information Application Drive out cost IT infrastructure Turn data into information assets Innovate Educate Globalize Integrate Standardize Reduce costs

  7. Turn information assets into actionable decisions & knowledge • Provide workflow tools that help ensure quality data • Provide access tools that give the right data at the right time • Provide analysis tools that help turn information into action • Capture the knowledge derived from this process for future use

  8. Workflow tools: Assay Explorer

  9. R1 A OH OH Access Tools

  10. Analysis Tools • Humans are the best decision makers • Informatics must • Aid the human ability to recognize patterns through easy to manipulate visualizations of data • Improve UI’s to be more natural

  11. Spotfire

  12. Going beyond analysis to decision support • A truly effective decision support environment is build on an open informatics framework to • Access all of the information available, in context • Visualize and analyze against all or subsets of the information • Access tools for calculating and predicting properties and predicting properties based on existing data

  13. Going beyond analysis to decision support • Discover in silica predictive models • Test those models against existing data • Validate those models through additional screening Result: Provide new leads more quickly, with fewer discovery cycles

  14. Inventory Candidates Standard Test Set Lead Proof X X X Methodology (algor.) Early Validation safe new effective economical XX X Descriptors (chem., physicochem. etc.) Proposals Interoperating informatics solutions for Discovery Targets SMART Reagent Selector Compound Selection Assay Explorer Compound Warehouse CL Tools Central Lib Compound Warehouse Toxicity EcoPharm Analysis Visualization

  15. Compound Warehouse Beilstein DB MDL DBs Enterprise DB Project DB 3rd Party DB’s Your Application Your Application 3rd Party Application MDL’s Application Beilstein’s Application Accessing disparate data sources

  16. One query access to multiple databases Compound Warehouse Beilstein MDL Enterprise Project 3rd Party 3rd Party LitLink Server Native Application One click access from multiple databases Provide access to data anywhere: Compound Warehouse and LitLink

  17. Query Drill down CWResult Decision Support Database Browser Procurement Facilitating interoperability

  18. Content Technology Interoperability requires software and database resources Compound Locator Your Application DecisionSupport Database Browser Procurement ExperimentalWorkflow

  19. Knowledge Extraction

  20. Knowledge—what scientists create • Recognizing and generalize patterns • Differentiating causality from coincidence • Recording conclusions in papers and reports, supported by data

  21. Knowledge capture is key • In Discovery, capturing knowledge means capturing • Decisions • Analysis methodology • Supporting data • Context (e.g., experimental protocol)

  22. Knowledge mining today • Today’s technology can help the scientist • Search disparate sources • Review the results • Navigate between the sources • Recreate the knowledge

  23. Knowledge extraction progress is being made • Automating knowledge base creation • Intelligent indexing • Automatic thesaurus construction • Mining the knowledge base • Relevance based retrieval • Natural language searching

  24. Creative Science on a Systems Engineering Framework • Creative science is • ad hoc • interactive • intuitive • Systems engineering is • disciplined • ordered • structural

  25. Creative Science on a Systems Engineering Framework • Change is a constant • Transitions require management • Take into account • strategy • pace • values • culture

  26. People Science Business Link business and scientific concerns

  27. Thank You

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