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

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

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

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 Turn data into information assets IT infrastructure Information Application Drive out cost Drive up capability 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 Access Tools A R1 OH

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

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

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

17 Facilitating interoperability Decision Support Database Browser Procurement CW Result Drill down Query

18 ContentTechnology Interoperability requires software and database resources Decision Support Your Application Compound Locator Database Browser Procurement Experimental Workflow

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 èRecreate 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 Link business and scientific concerns ScienceBusinessPeople

27 Thank You