1 The Discovery Informatics Framework Pat Rougeau President and CEO MDL Information Systems, Inc....
27
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
1 The Discovery Informatics Framework Pat Rougeau President and CEO MDL Information Systems, Inc. Delivering the Integration Promise American Chemical
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