1

The Discovery Informatics Framework

Pat RougeauPresident and CEO

MDL Information Systems, Inc.

Delivering the Integration Promise

American Chemical Society Meeting

San Francisco, CAMarch 27, 2000

2

Integrating informatics into the Discovery process

TargetsTargets

Inventory

Proposals

X XX

Standard Test Set

X X XProof

Candidates

Descriptors (chem., physicochem. etc.)Met

ho

do

log

y (

alg

or.

)

Early Validation

safe new effectiveeconomical

LeadLead

Synthesis

RepeatAnd Repeat

3

DB

DB

Information sources for the Discovery process

Journals

Journals

Journals

Standard Test Set

TargetsTargets

Inventory

Proposals

X XX

X X XProof

Candidates

Descriptors (chem., physicochem. etc.)Met

ho

do

log

y (

alg

or.

)

LeadLeadSynthesis

Early Validation

safe new effectiveeconomical

DB

DB

DBJournals

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

ApplicationDrive

out cost

Drive up capability

InnovateEducate

GlobalizeIntegrate

StandardizeReduce 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

AR1

OH

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

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Spotfire

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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

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Interoperating informatics solutions for Discovery

TargetsTargets

Inventory

Proposals

X XX

Standard Test Set

X X XProof

Candidates

Descriptors (chem., physicochem. etc.)Met

ho

do

log

y (

alg

or.

)

Early Validation

safe new effectiveeconomical

LeadLead

Analysis

CL ToolsCentral Lib

SMARTReagent Selector

CompoundWarehouse

CompoundWarehouse

ToxicityEcoPharm

Visualization

Assay Explorer

CompoundSelection

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Accessing disparate data sources

BeilsteinDB

MDLDBs

EnterpriseDB

3rd PartyDB’s

ProjectDB

Compound Warehouse

Beilstein’s Application

MDL’s Application

YourApplication

YourApplication

3rd PartyApplication

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Provide access to data anywhere: Compound Warehouse and LitLink

Beilstein MDL Enterprise 3rd PartyProject 3rd Party

Native Application

One query access to multiple databases

Compound Warehouse

LitLink Server

One click access from multiple databases

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Facilitating interoperability

Decision SupportDecision Support Database BrowserDatabase Browser

Procurement Procurement

CWCW ResultResult Drill downDrill downQueryQuery

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ContentContent TechnologyTechnology

Interoperability requires software and database resources

DecisionSupport

Your Application

Compound Locator

Database Browser ProcurementExperimental

Workflow

Knowledge Extraction

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Knowledge—what scientists create

Recognizing and generalize patterns

Differentiating causality from coincidence

Recording conclusions in papers and reports, supported by data

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Knowledge capture is key

In Discovery, capturing knowledge means capturing Decisions Analysis methodology Supporting data Context (e.g., experimental protocol)

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Knowledge mining today

Today’s technology can help the scientist Search disparate sources Review the results Navigate between the sources

Recreate the knowledge

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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

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Creative Science on a Systems Engineering Framework

Creative science is ad hoc interactive intuitive

Systems engineering is disciplined ordered structural

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Creative Science on a Systems Engineering Framework

Change is a constant

Transitions require management

Take into account strategy pace values culture

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Link business and scientific concerns

Science Business

People

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Thank You