Data Mining on the Farm Accelerating the search for a better pesticide John B. Kinney, Senior...
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![Page 1: Data Mining on the Farm Accelerating the search for a better pesticide John B. Kinney, Senior Research Associate DuPont Biosolutions Enterprise Spotfire.](https://reader035.fdocuments.us/reader035/viewer/2022070409/56649e9e5503460f94ba08c0/html5/thumbnails/1.jpg)
Data Mining on the Farm
Accelerating the search for a better pesticide
John B. Kinney, Senior Research Associate
DuPont Biosolutions Enterprise
Spotfire User’s Conference, May 3-4 2001© 2001, DuPont, Inc. - All Rights Reserved.
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DuPont Biosolutions Enterprise
Crop Protection Products* Control weeds, insects, and plant diseases
Pioneer Hi-Bred High performance seeds
Protein Technologies Soy protein isolates used in the food industry
Qualicon Food safety
* Focus of today’s talk
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CPP Goals
Control pests Efficaciously Safely Environmentally Cost effectively
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CPP Research & Data Styles
in vitroin vivoField
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in vivo CPP Research
“Treating the bed to cure the patient”
Plants in potsLength of test is a factor “Extra” data
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Herbicide Test Unit
CRL
BYG
FTI
MOG
PWX
VEL
BGC
Test SubstanceControl
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Field Tests
Same as in vivo, but with less control!
“Extra” dataDegradation and movement in the
environment are major issues
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Data Issues
Biological variability(Highly) Multivariate dataEC50 results are uncommonHistorical data is valuable
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Successful Applications of Data Visualization
Sourcing: Preformatted data sets for sample acquisition analysis
Hit Followup: R-group visualization and analysis
Lead Optimization: Color-coded reports for rapid, high-dimensional comparisons
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Browsing Acquisition Analysis Data
Challenge: Characterize and evaluate offerings from compound brokers and collaborators
Solution: External system to characterize offerings and build tables for browsing in Spotfire
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Minimal interface...
User selection from existing “evaluation tables”
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Spotfire for browsing
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Parallel Synthesis Hit Followup
Visualization and analysis of combinatorial library
Row and Column layout useful, but not chemically relevant!
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Merging synthetic schemes combined with biology
Hansch-style characterization often helpful for identifying trends and features
Fragment properties and whole molecule data can provide insights
NR1
R2
R1 == methyl, ethyl, propyl, etc
R2 == -F, -Cl, -Br, -I
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Plate layout vs. Fragment Data
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Lead Optimization
Numerous test and characterization values for each compound
History of complex, printed data reportsPRIMARY PLANT RESPONSE (WEEDS)
INCODE = CPD1 DEPT = 8 DATE = 891127 ############################################### SUBMITTER = # # N.B = 056898 N.B.PAGE = 021 #INCODE= CPD1 # AMT =.21G % = 100 FORM = # # LEAD AREA = # # #/MOLNM # #/Info= CHEMICAL NAME AVAILABLE UPON REQUEST # # # # # ###############################################
YY/MM/DD TYPE RATE UNITS MORN COCKL VELV PIG CRAB GIANT FOXTL B Y CHEAT DOWNY WILD SOR COMMENT TEST GLORY BUR LEAF WEED GRASS FOXTL MILLT GRASS GRASS BROME OATS GUM-------- ---- ------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -----------90/01/02 POST 1.0 KG/HA 90H 70H 70H 10C 10C 0 40G 0 30C HERBICIDE90/01/02 PRE 2.0 KG/HA 0 10H 30H 0 20H 0 0 0 30C
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HeLo
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Project Overview w/Heat Maps
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Future Challenges
Better data extraction/formatting techniques
Expanding data warehouse to include non-traditional data sources
Computer screen real estate!
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Acknowledgements
At the risk of missing someone...Kevin Kranis (retired)Laurie ChristiansonDan Kleier
The entire Discovery Organization -- They generated the data!