Slide 1

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

Slide 2

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

Slide 3

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

Slide 4

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

Slide 5

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

Slide 6

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

Slide 7

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

Slide 8

8 Workflow tools: Assay Explorer

Slide 9

9 Access Tools A R1 OH

Slide 10

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 UIs to be more natural

Slide 11

11 Spotfire

Slide 12

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

Slide 13

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

Slide 14

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

Slide 15

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

Slide 16

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

Slide 17

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

Slide 18

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

Slide 19

Knowledge Extraction

Slide 20

20 Knowledgewhat scientists create Recognizing and generalize patterns Differentiating causality from coincidence Recording conclusions in papers and reports, supported by data

Slide 21

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

Slide 22

22 Knowledge mining today Todays technology can help the scientist Search disparate sources Review the results Navigate between the sources Recreate the knowledge

Slide 23

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

Slide 24

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

Slide 25

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

Slide 26

26 Link business and scientific concerns ScienceBusinessPeople

Slide 27

27 Thank You