ACS Green Chemistry Institute®American Chemical Society
The future of green and sustainable design – where’s
the data?
April 30, 2019
David J. C. Constable, Ph.D.
Science Director, ACS Green Chemistry Institute®
ACS Green Chemistry Institute®American Chemical Society
Outline
The chemist’s dilemma
Feedstocks, building blocks, pathways
AI, Machine Learning, PredictionThose machines have a lot to learn
Design and systemsDesign from a systems thinking point of view
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Engaging you to reimagine chemistry and engineering
for a sustainable future.
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We believe sustainable and green
chemistry innovation holds the key to
solving most environmental and human
health issues facing our world today.
• Advancing Science
• Advocating for Education
• Accelerating Industry
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Why Reimagine Chemistry?
The chemistry enterprise as currently
operated is completely unsustainable
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Petrochemical Feedstocks
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Bio-based and Renewable Feedstocks
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Chemical Space Available for Innovation
1060
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Number of Molecules on US EPA’s Radar as of February, 2019
40,655** “key result of the update is that less than half of the total number of chemicals on the current TSCA Inventory (47 percent or 40,655 of the 86,228 chemicals) are currently in commerce.”
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Thinking About Design
“Design is a signal of
intention”“Cradle to Cradle”
William McDonough
2002
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Design Principles
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Design Principles
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Design Principles
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Core Competencies
• Graduates will be able to design and/or select chemicals
that improve product and sustainability performance
from a life cycle and systems perspective.
• Graduates will understand that chemicals and materials
are prepared through transformations of raw materials
via synthetic pathways and be able to design and/or
select chemical syntheses that are highly efficient, take
advantage of alternative feedstocks, and generate the
least amount of waste.
• Graduates will understand how chemicals can be
used/integrated into products to achieve the best benefit
to customers while minimizing life cycle sustainability
impacts
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The System of Systems is Broad and Complex
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MACHINE AND DEEP LEARNING, AI, AUTOMATION
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Machine Learning isProgressing
Predictor
Training set
Output variable
Learning algorithm
Descriptors
Test set DescriptorsOutput
prediction
Training
Prediction
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UL REACHAcross™
Luechtefeld, T., Rowlands, C., Hartung, T., Big-data and
machine learning to revamp computational toxicology
and its use in risk Assessment, Toxicol. Res., 2018, 7,
732
9801 unique substances, 3609 unique study
descriptions and 816,048 study documents
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US EPA CompTox
875 Thousand Chemicals
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U.S. EPA CompTox
1-butanol butanoic acid 4-acetamidobutanal
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ViridisChem
>90M chemicals,
2.4B properties
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ViridisChem
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US EPA CompTox:Glucaric acid
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ViridisChem – Glucaric Acid
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Nature Communications volume8, 15733 (2017)doi:10.1038/ncomms15733
ACS Cent. Sci. 3, 12, 1337-1344
Deep Learning Coupled with Automated Synthesis
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Conclusions
• The world of toxicity prediction is based on existing data on petrochemically derived molecules
• The problem of data quality, curation, coverage, etc., remains no matter what prediction method is used
• Accurate in-silico toxicity prediction should be seen as an essential enabling technology for the implementation of green and sustainable chemistry
• There is a need for researchers who understand the fundamentals and are working at the intersection of chemistry, toxicology and computational (chemistry, mechanistic toxicology, prediction) methods.
• AI approaches are a computationally intensive undertaking so the computing infrastructure needs to be shifted to the cloud and costs reduced.
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Questions?
David J. C. Constable
What’s Your Green Chemistry? TM
We want to hear your story. Contact [email protected]