Technology watch - AI in chemical industry

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Ref: DOCLOG-XXXX-DOC-A (edit in slide master)Document Title - yyyy.mm.dd (edit in slide master) 1
TEMP-0010-DOT-F-VerhaertPresentation
Technology Watch
AI in Chemical Industry
SMART INDUSTRY
Jochem Grietens
Applied physics engineer – AI engineer
Jochem.grietens@verhaert.com
13.09.2019

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AI technology review – 13.09.2019 2
Content
Demystification
• What is AI ?
• What is AI good at ?
• Classification
• Finding patterns
• Recognizing deviations from patterns
• Predicting
• Structuring the unstructured
• Estimating from proxy information
• Agency
• Model complex systems
• Optimization, search, planning
• Information retrieval
• What AI can’t do… ( very well )
AI approach
AI opportunities in chemistry today
Patent landscape

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Demystification
What is AI ?
What is AI good at ?
What AI can’t do… ( very well )

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What is AI ?

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What is AI ?
There is a lot of debate outside of the A.I. community on how to
define the field. The experts more or less agree:
Artificial intelligence
“The theory and development of computer systems able to
perform cognitive tasks normally requiring human intelligence. “
Cognitive tasks are defined in cognitive science as :
attention/logic and reasoning, decision making, perception ( such
as visual perception, speech/sound recognition), language
understanding and generation (translation).

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AI effect
The above definition leads to problems because of the AI effect:
“As machines become increasingly capable, tasks considered to
require "intelligence" are often removed from the definition of AI, a
phenomenon known as the AI effect. A quip in Tesler's Theorem says
"AI is whatever hasn't been done yet”. For instance, optical character
recognition is frequently excluded from things considered to be AI,
having become a routine technology.”
The field is moving on different fronts but the main advances
have been in prediction and perception and were made possible
by deep learning: speech recognition, visual perception, …

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What about General vs Narrow AI
Long term aim
Develop systems that achieve a level of cognitive performance
similar/comparable/better than that of humans. (General AI)
 Not in the near future, no practical or even noteworthy academic systems
exist now with such capabilities. Should not be the focus of companies
today.
Short term aim
On specific tasks that seem to require intelligence:
Develop systems that achieve a level of cognitive performance
similar/comparable/better than that of humans. (Narrow AI)
 Achieved for many tasks already, can speed up your business today. Field
is moving really quickly.

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AI Definition
To achieve flight, humans did not
have to imitate birds exactly.
• The principles of flight were
extracted ( lift surface +
velocity)
• The EFFECT of flight was
achieved.
At the very least in the context of the
short term aim of AI:
• we do not want to imitate
human intelligence.
• Reproduce the EFFECT of
intelligence

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The field of data science (non-exhaustive)
Data science
AI
Classical AI
techniques
Search / Planning
Optimization
Logic: induction and
deduction
Knowledge
representation
Expert systems
ML
Supervised machine
learning
Bayesian networks
Decision trees, SVM’s, …
Neural networks
Unsupervised
machine learning
Clustering
Reinforcement learning
Learning distributions:
autoencoders, GANS, …
PCA
…
Data
analytics
Statistics
Machine learning
Clustering
...
Mathematics
The field of AI draws
from many fields of
study, in this tree a non-
exhaustive overview is
given in an attempt to
provide context. The
relations are explained
further in the slides
below.

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The field of data science
Data science is a multi-disciplinary field that uses scientific methods,
processes, algorithms and systems to extract knowledge and insights
from structured and unstructured data. The data science field entails
data analytics and A.I. among others. A useful distinction can be made
by observing:
• Data analytics is a field where a data scientist stays in the driver seat
to extract information, draw conclusions and answer questions.
• A.I. systems generally require an expert for development but
afterwards they can perform the required tasks in an automated way.
However, data analysts use AI/ML techniques (clustering, classifying)
and visa versa.

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Classical subfields of AI
The “classical field” of AI entails
• Search/planning/scheduling
• Optimization
• Logic induction and deduction
• Knowledge representation, expert systems, …
Although these fields are less novel, they are still highly relevant to solve
contemporary problems because:
• Progress is still being made on the scientific front
• Through the availability of data and computational power, problems that were
previously not solvable have now become candidates to apply these
techniques
• Advances in machine learning allow for unstructured data such as speech,
text, images etc. to be interpreted and translated to structured data. This
structured data can be handled by these classical subfield. This creates
tremendous synergy.

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Machine learning
“Machine learning is a branch of artificial intelligence that uses
sophisticated algorithms to give computers the ability to learn from
the data and make predictions.”
The biggest leaps in AI of the last decade have been in the
machine learning space. These advances have been made
possible by 3 main factors in order of decreasing importance:
• Computing power ( parallel computing made
Available/affordable through the gaming world)
• Advances in the ML techniques.
• Data availability
The momentum created by the interest of the general public, large
companies and governments has greatly contributed to funds
and efforts being directed towards A.I. This has created the
perfect storm and the avalanche of innovation we currently
observe.

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Machine learning steps
Machine learning comprises 2 steps:
• During development – Training. Data is fed to the ML algorithm, the
algorithm learns patterns from the given examples.
• During operation – Inference. The model is deployed and in operation,
new data is fed to the model and the learned patterns can be applied
to new input data to provide the desired output.

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Machine learning FAQ
Frequently asked question :
“ But don’t machine learners learn continuously ? “
Answer:
In most applications, the two steps of machine learning are clearly
separated in time. Training is performed and a fully trained network is
deployed. But,
• These steps are often repeated iteratively and
alternately on new batches/instances of data. This called iterative
learning and allows for incremental improving and releasing of new
models.
• Models that learn with every new incoming data point exist as well.
The inference step and training step happen simultaneously. This is
called continuous learning. These techniques are not widespread in
engineering applications with high reliability requirements yet, because
they are harder to test, verify and validate before release, since they are
ever-changing.

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Supervised vs. Unsupervised learning
Supervised learning algorithms require annotated training data
containing both:
• Example input data
• Associated desired output data.
The models then learns to extract the desired output form the input data.

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Supervised vs. Unsupervised learning
Unsupervised learning algorithms require training data containing
only:
• Example input data
The models extract patterns from the input data and apply these
to new data to provide insight.

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Machine learning FAQ
Frequently asked question :
“ Does this mean that unsupervised machine learning algorithms
are smarter and better than supervised machine learning
algorithms ? Since they have no need for annotated data ? “
Answer:
No, these types of models are used for different tasks and have
different characteristics. Unsupervised models are not able to
perform many of the tasks supervised machine learning
algorithms do very well and visa-versa.

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Machine learning overview
There are many machine learning
paradigms and algorithms. These are
some of the more important families of
models: Bayesian (belief) networks,
Support vector machines, Decision
trees/forests, Artificial neural networks
and many more…
All these families have their specific
characteristics.
• Amount of data required
• Data noise sensitivity
• Computational effort required for
training and inference.
• Human interpretability of the learned
patterns: Black box vs. White box
• Performance
• Supervised vs. supervised.
Choosing the right ML for the job
should be based on requirements.

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Machine learning model selection FAQ
QUESTION: “ All the material I read about AI talks about neural networks, are they the best overall
models out there right now ? ”
Yes, and no. The main driver behind the new wave of AI technologies has been neural networks. The
main reason is that these networks turn out to be remarkably versatile in several regards:
• The types of tasks they can solve ( estimation, language modeling, speech-to-text, prediction,
computer vision, … )
• The complexity of relations they can learn. (simple to highly complex)
• The amount of data they can handle and learn from. (from small data to big data)
• Their robustness to noise in the data.
Because of this flexibility and these models have taken the AI world by storm. However, A good
selection should match the AI task requirements and model characteristics. Although neural networks
have achieved exceptional results and have facilitated the revival of AI, they have some drawbacks
regarding interpretability and computational cost. In specific cases these drawbacks might lead the
developer to favor other ML techniques. These limitations should be well understood. That being said,
NN have revolutionized the AI world and the rate of innovation is increasing in speed partly because of
them.

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AI = Big data FAQ
Question: “ Is A.I. inseparably tied to BIG data or does A.I. for small data exist ? “
Answer: No it is not, however it is often desired. Let’s elaborate,
1. Firstly, not all A.I. techniques are data driven. A lot of search, planning and
optimization methods just require a good description of the problem.
2. Secondly, even some classes of machine learning methods can perform well on
limited amount of data, given a limited complexity of the task.
3. Thirdly, many of the very high performance ML techniques for complex tasks do
require big data. i.e. large, deep neural networks require a lot of data. However,
for common tasks such object recognition we can reuse networks that were
trained on other dataset and only have be fine-tuned on reduced dataset that is
specific to our problem. This technique is called transfer learning.
To conclude, AI requires big data for complex problems of uncommon tasks that are
very specific to your use case. Complexity is dependent on the amount of input and
output variables and the complexity of the relations that needs to be learned.

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What AI is good at…
• Classification
• Predictions
• Recognize patterns
• Recognize deviation from patterns
• Structuring the unstructured
• Estimating from proxy information
• Agency
• Model complex systems
• Optimization, search

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Classification

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Classifiers learn to classify samples based on their features.
• The input data can take any data format: images, videos, text,
molecule representations, …
• The output is a finite set of classes that we want to recognize.
ML based classification models can achieve fully automated
above human performance in many cases.
Classification

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Classification – example use cases
• Machine Learning Based Toxicity
Prediction. From Chemical
Structural Description to toxicity
classification.
• Computer-Aided drug design.
743,336 compounds,
approximately 13 million
chemical features, and 5069
drug targets were used to train
the ML algorithm. The model
provides classification of
properties, structures and
functions.

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Finding patterns

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Find patterns
ML algorithms are good at finding
patterns in data of any type. The power
of these algorithms becomes apparent
when the data is to large for humans to
sift true.
Examples of every day pattern finding
powered by ML:
• Spam filters find patterns in spam
mails to later classify and exclude
them
• Recommender engines find patterns
in consumers profiles and products to
match sales and allow targeted
advertising.
• Time series patterns allow to predict
stock prices.
• Clustering algorithms to find similar
compounds to a target chemical
compound.

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Find patterns – example use cases
• Deep Reinforcement Learning Approaches
for Process Control . Finding patterns in
plant behavior for process control.
• Sales Lead Scoring decision support -
Pattern finding ML systems allow to learn
from CRM historical data to find companies
with a high chance of closing. These tools
extract patterns of previously successful
sales pipelines and search for companies
with similar patterns.
• Pattern finding ML systems allow to learn
from historical crm data to find customer
with high up-sell or cross-sell potential.
These tools extract patterns of previously
successful sales pipelines and search for
companies with similar patterns.

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Recognize deviation from patterns

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Recognize deviation from patterns
Oftentimes we want to recognize deviations
from ‘normal operation’ of a system. These
deviations might be very rare or no data is
available of them at all.
Pattern recognition systems rely on many
similar examples of these patterns being
available to learn from. This approach won’t
work for detecting deviations from patterns.
In general these cases are solved by using ML
techniques characterizing normal behavior
and detecting when the system deviates from
this behavior.
Using ML for this purpose allows to
characterize highly complex systems
behavior and predicting never seen before
anomalies.

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Recognize deviation from patterns – example use cases
• On-line reactor monitoring with neural networks. On-line
condition monitoring and signal validation has become a
significant issue to ensure stable operation and deviations
from normal operations produce alerts.
• Deep learning for pyrolysis reactor monitoring. From thermal
imaging toward smart monitoring system to detect faults
using neural networks.
• Historical example: This example from Suewatanakul [1993]
demonstrates the use of a feedforward ANN to detect faults in
a heat exchanger.

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Prediction

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Predicting
ML algorithms learn how to predict
desired output parameters from new,
never seen before samples. The ML
learns from record data or historical data.
Predictions can predict quantities real
time or predict into the future.
The input data can contain multiple
variables taking into account many
context variables other methods would
not be able to handle.

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Predicting – Example use cases (1)
• Sales forecasting - Sales managers face the daunting
challenge of trying to predict where their team’s total sales
numbers will fall each quarter. Using an AI algorithm,
managers are now able to predict with a high degree of
accuracy next quarter’s revenue.
• Quantum chemistry - Machine learning algorithm to
predict the atomization energies of organic molecules.
(von Lilienfeld)
• Computational Material Design – ML ( deep learning )
applications to predict and design material properties in
silico.
• Thermal reactor control - High-speed and high-accuracy
thermal control of a continuous-flow chemical reactor with
computer vision and a predictive Artificial Neural Network.

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Predicting – Example use cases (2)
• Chemical reaction prediction - Chemists at Princeton
University and Spencer Dreher of Merck Research
Laboratories harness artificial intelligence to predict the
future of chemical reactions. They predict yields
accurately while varying up to four reaction components
by applying machine learning.
• Chemical reaction prediction - treating chemical reactions
as a translation problem ( think google translate) . In using
such an approach, researchers were able to feed chemical
components into a neural network trained on a dataset of
395,496 reactions. The neural network then used what it
had learned about prior reactions to make predictions
about what would occur under new conditions.
• Predictive maintenance - Predictive maintenance is the
practice of using anomaly detection, pattern recognition
and other AI techniques to predict when machinery needs
maintanence. This is being applied in factories, fleet
management, process control today.

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Structuring the unstructured

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Structuring the unstructured
For the longest time, computers could only perform operations on structured data like excel sheets, databases etc.
Advances in neural networks have revolutionized computing by allowing unstructured data to be interpreted and
structured in meaningful ways. This has allowed unstructured formats such as natural language ( written and spoken),
images, videos, speech and others to be converted to structured data by means of extracting higher level meaning and
features from those documents. These advances have added perception to computers resulting in an explosion of
applications that used to be off-limits for computers.
Examples from daily life:
• Adding perception systems to cars enabling autonomous cars.
• Computer vision: object detection, face recognition and others for identification.
• Speech to text and natural language understanding allowing for voice interfaces to computers
• …
Semantic segmentation =
automatically assigning a meaningful
label to each pixel.

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Structuring the unstructured – example use cases (1)
• Computer vision enabled
techniques for organic synthesis.
• Lab tools – Computer vision and
speech recognition technologies for
experiment tracking, monitoring
and logging.
• Semantic segmentation on
molecules - multi-scale structural
analysis of proteins by deep
semantic segmentation
• Deep learning to yield a powerful
tool for both protein design and
structure prediction.

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Structuring the unstructured – example use cases (2)
• Speech to text and language
understanding technologies
allow interfacing with devices in
new, hands-free ways.
• Production line intelligence -
Rockwell automation created a
‘data scientist in a box ‘ called
Shelby. Including a production
line chatbot with text based
conversational interface,
chatbot and a voice interface.
Based on Microsoft Cortana.

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Estimating from proxy information

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Estimate from proxy information
Estimators to estimate the unmeasured quantities indirectly by using proxy-
parameters of measured quantities. The machine learning algorithms then learns the
relation between the measured parameters and the desired unmeasured parameters.
This is often useful because some quantity can not be measured directly, so it needs
to be estimated from related parameters that can be measures.
Example:
• Extracting the letters you intended to type on your smartphone keyboard from the
letters you actually typed ( autocorrect )
• Predictive maintenance by measuring vibrations of an accelerometer on a machine
to detect mechanical failure.
• Estimating core temperature from multiple external temperatures
• Estimating process quantity in a reactor vessel that is too hot for direct
measurement but has some surrounding parameters that are linked to the
condition of interest.
• …

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Estimate from proxy information – Example use cases
• Machine learning can be used to estimate hard to measure
parameters easier to measure parameters as an alternative to the
conventional observers and hardware sensors. This is especially
valuable for cases in which the environment doesn’t allow for direct
measurement. These estimators, also known as software sensors
have been successfully applied in many chemical process systems
such as reactors, distillation columns, and heat exchanger due to
their robustness, simple formulation, adaptation capabilities and
minimum modelling requirements for the design.
• These systems can predict unmeasured states such as
concentration, temperature, heat flux, molecular weight and
impurities from context parameters. An overview can be found in
the paper: “Artificial Intelligence techniques applied as estimator in
chemical process systems – A literature survey Jarinah Mohd Ali”

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Agency

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Agency
AI systems can have agency, meaning they
can act as an agent and learn directly from
their environment. These types of systems
are very good at learning to play games
because they are continuously improving
whilst playing. However, they have also
found their way in robotics and some end-
to-end autonomous vehicle applications
amongst others. It should be noted that
these systems are not often encountered
when reliability and safety are required.
They are hard to test because they learn
continuously and the design can’t be
frozen.
Reinforcement learning is the most popular
technique in this space.

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AI, automation and robotics
At this point in time, for most robotics and automation
applications, these end-to-end reinforcement learning models are
not used in critical systems. When Robotics utilize ML techniques
these are most often ML perception systems combined with
some planning or optimization methods. These systems can be
thoroughly tested and released in a controlled way.

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Model complex systems

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Model complex systems
Machine learning algorithms can
learn complex relations between a
large number of variables. This
allows for the modelling and
characterization of complex
systems with many variables.
The model is learned on
measurement data of the
process.

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Model complex systems – use case examples
• Stirred Tank modeling with Reinforcement
Learning- ML algorithms were used to model
the dynamics based on measurement data.
• Chemical reaction modeling learned from
specimen data.
• Modeling plant operation, learned from data.

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Optimization, search, planning

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Search / planning / optimization
Search planning and optimization
are all about finding solutions in a
large solution space.
Examples:
• Automatic scheduling and
planning
• Stock optimization
• Production parameter
optimization
• Vehicle routing
• Information retrieval
…

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Search / planning / optimization – use case examples (1)
• Price Optimization - Today, an AI algorithm
could tell you what the ideal discount rate
should be for a proposal to ensure that you’re
most likely to win the deal by looking at
specific features of each past deal that was
won or lost.
• Process optimization - i.e. Machine learning to
optimize process of continuous flow
chemistry.
• Process Optimization – optimization of
manufacturing process parameters using
deep neural networks as surrogate models

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Supply chain management can benefit
greatly from AI techniques for optimization:
Improving forecast accuracy, optimizing
transportation performance, improving
product tracking & traceability and
analyzing product returns.

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Artificial Intelligence for Inventory Management - Amazon examples
1. Demand Prediction for Inventory Management
2. Reinforcement Learning systems for full-inventory management.
3. Robot automation
You may be using SAP, Xero or any other myriad of software for your
inventory management. These can be integrated with Ai.

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Information retrieval

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Information retrieval
Information retrieval is found everywhere, in the
search bar on your phone, email and your search
engine. Information retrieval can be used to search
through multimedia databases, documentation,
scientific literature and other databases.
Recent advances in AI like neural network
encodings allow for faster and more intelligent
search that goes beyond text matching. These
technologies make previously unsearchable
formats, searchable:
• Searching similar images based on query
images.
• Searching similar molecules based on their
chemical structure.
• Search videos
• Searching audio recordings
• …

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• Dealing with the long tail of distribution
• Learning outside the data
• Explaining itself
• Deciding - what probability is acceptable?
• Reasoning – induction vs deduction
• Designing itself

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Dealing with the long tail of distribution & Learning outside the data
Although modern machine learning algorithms are surprisingly good at predicting outside of sample
cases correctly, most techniques require a representative dataset during training of the full input space.
This means that ML won’t be able to learn a lot about samples that are very far from anything ever seen
before. ( although the anomaly detection systems have ways to deal with this (see = “ recognizing
deviations from patterns ).
Explaining itself
Different methods have different levels of interpretability for humans. However at this point high
performance methods like neural networks can learn very complex relations and patterns but have no
way of explaining or providing insights into its learned relations.
Deciding - what probability is acceptable?
Many machine learning based decision tools will provide some type of probability output. For example it
can output the probability that a chemical process is overheating. In this case, humans still have to
decide what the threshold for action is and what that action would be. However, this is not always the
case, one can let a ML algorithm learn optimal actions and thresholds in some cases.
Reasoning
Humans are very good at linear reasoning and reasoning by analogy with very limited information. There
is a lot of research on this topic but AI systems are not yet at that point.
Designing itself
At this point AI algorithms still require a creator or designer. The A.I. expert is tasked with defining a
good size and architecture of the AI model so that it can learn or perform the task at hand. There are a
lots of research and first applications being created that try to automate this process but at this point
A.I. experts are still needed in most cases.

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AI approach

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“Let’s collect as much data as possible and apply A.I. later”
“We have a bunch of data laying around … “
“A.I. as a solution to everything…”
What is the right approach to AI in your organization ?

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Assess your organization readiness for AI
Strategy
Readiness level 1 = initial
Readiness level 2 = repeatable
Readiness level 3 = defined
Readiness level 4 = managed
Readiness level 5 = optimizing
Adapted from th
AI awareness
(in organization)
Legal Data
People
1
2
3
4
5
AI readiness requires a
company wide
commitment. Fill in this
canvas to asses your
readiness.
A legend of the axis is
provided on the next
slide.

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Level 1 Level 2 Level 3 Level 4 Level 5
Initial Repeatable Defined Managed Optimizing
Strategy No corporate initiatives.
Isolated.
Integration & cooperation
in multiple business
units.
Penetration of AI in all
business units.
Evidence based process
metrics regarding AI
usage.
Continuous improvement
& AI as a well-known and
common strategy.
Data Scattered & unmapped
data sources & tools.
Some centralized
sources, tools or data
lakes available.
Centralized data
warehouses with mapped
data quality & potential.
Corporate standard tools.
Data management &
value potential is known
& reported on
consistently. Enterprise
AI architecture defined.
Active steps are taken to
optimize monetization.
People Training & people is ad
hoc & individual.
People development &
regular courses.
Data competency &
development
frameworks.
Organizational structure,
culture of innovation.
Collaboration according
to competencies.
Data literacy is a
cornerstone of talent
management with
mandatory & continuous
development for all
relevant employees.
AI awareness
(in
organization)
Product owner Marketing R&D Higher management
Legal Scattered or unclear
responsibility.
Awareness training.
Defined & communicated
responsibilities.
Clear responsibility with
centralized oversight,
enforcement & training.
Internal audits,
mandatory reporting &
penalization.
Legal compliance as an
asset & unique selling
point.
Assess your organization readiness for AI
Adapted from the Faktion framework

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Integrated approach to AI
AI algorithms don’t live in a vacuum. They interact through IT
structure, with the physical world and humans. A good AI
solution is a global optimum at all these levels to achieve the
desired value proposition. Often the AI model is expected to
solve the problem downstream of the sensors, IT and user
components. This can lead to suboptimal solutions.

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When developing AI applications there are several levels of customization one can take.
As a rule of thumb it is best to start with existing services ( top of the diagram ) and
customize only when needed. Lower levels in the pyramid require more specialized
personnel but allow more freedom to build custom applications.
Tools and frameworks
Integrated
software
components
SAP Leonardo
Salesforce Einstein
…
AI services
IBM Watson …
Google Cloud services …
Amazon AI services: Amazon forecast
Amazon lex (chatbots)
Amazon recognition (computer vision)
Amazon translate
….
Custom
development
tools
Tensorflow (neural networks)
Pytorch (neural networks)
Google OR (search and optimization)
…

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Integrated software components
SAP – Leonardo
• SAP Conversational AI
• SAP Data Intelligence
• SAP Cash Application
• AP Service Ticket Intelligence
• SAP Customer Retention
• SAP Predictive Analytics
• …

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Integrated software components
Salesforce Einstein

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Ai canvas
The AI canvas allow to analyze your AI opportunity. The goal is to
bring all stakeholders together and fill in the 4 main blocks as best
as possible:
1. The goal of the system
2. The predict step.
3. The learn step.
4. The evaluation step.

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Predict Goal EvaluateLearn
Impact on decisions
How are predictions used to make decisions that provide the proposed
value to the end-user?
AI canvas
Machine Learning / inference tasks
Input, output to predict & type of problem.
Making predictions
When to we make predictions on new inputs?
Offline evaluation
Methods & metrics to evaluate the system before deployment.
Value propositions
What are we trying to do for the end-
user(s) of the predictive system?
What objectives are we serving?
Live evaluation &
monitoring
Methods & metrics to evaluate the
system after deployment & to
quantify value creation.
Data sources
Which raw data sources can we use (internal & external)?
Features
Input representations extracted from data sources..
Collecting data
How do we get new data to learn from (inputs & outputs)?
Building models
When do we create/update models with new training data?
Adapted from Louis Dorard’s Machine Learning Canvas v.04
Input
Desired output
Problem type
Prediction schedule
Prediction policy
Methods
Metrics
What
Why
Who
Methods
Metrics
Statistical features
Expert features
Collection strategy
Model building strategy
Model building schedule
Workflow integration
Collection policy

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Predict Goal EvaluateLearn
Impact on decisions
How are predictions used to make decisions that provide the proposed
value to the end-user?
• Selection of molecules for further investigation.
• Omission of molecules for further investigation.
AI canvas – QSAR* toxicity use-case
Machine Learning / inference tasks
Input, output to predict & type of problem.
Making predictions
When to we make predictions on new inputs?
Offline evaluation
Methods & metrics to evaluate the system before deployment.
Input Molecular descriptors
Physico-chemical properties
Desired output Toxicity classification: Toxic/ non-toxic
Problem type Classification problem
Prediction schedule User (lab professional) initiated
Prediction policy Check data quality
Assure authorized user?
Methods - Lab validation tests for out of sample
predictions
- Expert evaluation
Metrics - Statistical accuracy error.
- Receiver operator curve (ROC)
Value propositions
What are we trying to do for the end-
user(s) of the predictive system?
What objectives are we serving?
What
Classify the toxic response of
chemical agents based on a
formal description of the
molecule in silico. (QSAR for
toxicity )
The in silico model is learned on
a database of molecule
descriptors and their toxicity.Why
• Increases efficiency of
toxicity screening. (
cheaper and faster )
• In silico testing is safer than
lab tests.
• Reduce suffering for lab
animals.
Who
• R&D team.
• Lab personnel
Live evaluation &
monitoring
Methods & metrics to evaluate the
system after deployment & to
quantify value creation.
Methods
Random sample tests on
predictions verified by lab tests
Metrics
% correctly identified:
Confusion matrix
Data sources
Which raw data sources can we use (internal & external)?
Existing databases: PubChem, ChEMBL, …
Features
Input representations extracted from data sources..
Collecting data
How do we get new data to learn from (inputs & outputs)?
Model Strategy
When do we create/update models with new training data?
• Experimental : molar refractivity, dipole moment, …
• Theoretical molecular descriptors: 1D,2D,3D,4D
• Fingerprints
• Graph invariants
• WHIM
• ….
Collection strategy Lab testing champagne for out of sample
products
Collection policy Good representation of different toxicity types.
Good distribution over chemicals
….
Model building strategy
Arteficial neural networks for classification
Model building schedule
First cycle: Establish feasibility. ~200 datapoints
Second cycle: Increase + diversify dataset. Retrain & refine model
Adapted from Louis Dorard’s Machine Learning Canvas v.04
Workflow integration
R&D staff assessment of:
1 – Ease of use
2 – Clarity of result
*Quantitative structure-activity relationship

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CONFIDENTIAL
AI opportunities in chemistry today

CONFIDENTIAL
Applications of AI in chemical industry
• Manufacturing: ML is primarily used for monitoring equipment and controlling applications in manufacturing. ML
algorithms predict failures in equipment and also predict the necessary maintenance of equipment. This results in
reduced down-time which in turn optimizes production.
• Drug Design: Traditionally, drug designing is a long drawn complex process. But with ML tools such as self-organizing
maps, multilayer perceptron, bayesian neural networks, and counter-propagation neural networks drug design has
become less challenging process.
• Compound classification: Chemists spend hundreds of hours in compound classification when it is done manually.
Furthermore, it is prone to human error and thereby not cost-effective. ML applications are being used for classifying
compounds.
• Toxicity prediction: ML methods such as support vector machines (SVM) and artificial neural network (ANN) are
widely popular to conduct R&D activities such as determining in vivo toxicity. Based on available in vitro bioassay data,
ML applications can be designed to predict the toxicity of chemicals.
ML applications are also used by chemical companies in couple other areas supportive to manufacturing:
• Demand prediction: For many chemical industries, the demand for products fluctuates throughout the year. For
instance, the demand for oil keeps changing every month. To further complicate, demand planning processes can be
inaccurate and hence, too expensive for a company. But demand planning is a critical process that is required in
manufacturing. ML algorithms, developed by data science experts, are accurate and are well-suited for conducting
demand prediction.
• Workforce management: In chemical industry, skilled workforce is in great demand. Hence, hiring and retaining skilled
employees is a challenge. Many companies focus on training managers, and employees to keep employee churn at a
minimal level. ML applications are useful to predict workloads, identify departments with greater churn, predict
employees who may leave.
• Business decision support
• Inventory optimization

CONFIDENTIAL
Application of AI in Chemical Engineering & manufacturing
• AI in chemical process modelling.
• AI in optimization of chemical processes.
• AI chemical process control.
• AI chemical process monitoring.
• AI techniques in fault detection and diagnosis
of chemical engineering.
• Predictive maintenance and fleet based
management for machinery.
• Quality control through automated systems (
computer vision )
• Automated Plant monitoring
• Waste minimization
• Manufacturing planning and configuration.
• Automation

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Application of AI in Chemical R&D
• Medicinal Chemistry and Pharmaceutical Research
• Drug/chemical Design
• Target validation, small-molecule design and optimization, predictive biomarkers, identification of
prognostic biomarkers and analysis of digital pathology data in clinical trials.
• Virtual screening (VS) for rational drug/chemical development.
• Prediction in biological affinity, pharmacokinetic and toxicological studies, as well as quantitative
structure-activity relationship (QSAR) models.
• Theoretical and Computational Chemistry
• i.e. Prediction of ionization potential, lipophilicity of chemicals,
chemical/physical/mechanical properties of polymer employing topological indices and
relative permittivity and oxygen diffusion of ceramic materials.
• Analytical Chemistry
• i.e. Neural network techniques with the aim to obtain multivariate calibration and
analysis of spectroscopy data, HPLC retention behavior and reaction kinetics.
• Biochemistry
• Neural networks are being employed in biochemistry and correlated research fields such
as protein, DNA/RNA and molecular biology sciences.
• I.e. e reverse-phase liquid chromatography retention time of peptides enzymatically
digested from proteomes, prediction the stability of human lysozyme.

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Resource management
• Inventory optimization/management
• Optimize throughput, energy consumption and profit.
• Supply chain management
• AI for increasing productivity and reducing costs
• Advanced analytics planning systems
• Order/demand forecasting
• (Human/material) resource forecasting

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Application of AI in business
“”The five largest Customer Relationship Management (CRM) vendors by
market share in 2015 were Salesforce, Oracle, SAP, Adobe Systems, and
Microsoft. These five companies make up almost half of the entire CRM
market. All of them have been investing in their internal development of
machine learning and AI, while also buying AI startups.” AI capabilities
are currently available for each of the five CRM giants
• AI-powered personalized marketing/experience – personalizing the
content each customer receives.
• Predictive recommendations – using a customer’s data to recommend
products they would be most interested it.
• Optimizing the selling process for representatives – opportunity
analysis of clients to create guidance to help close deals.
• Help direct sales offer by finding patterns in crm that have high chance
of yielding new business.
• New product introduction forecasting.
• Chatbots for better customer service

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Patent landscape
AI, ML …

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CONFIDENTIAL
The “AI” related patents are seldom
application/industry specific. There is no
significant patenting activity in industry
4.0 for the chemical industry specifics.
For the fields of machine learning and data management
processes, only 87 and 73 patents, respectively, have been filed
during the last five years within the chemical industry.

CONFIDENTIAL
KEY TAKEAWAYS FROM PATENT SEARCHES
• Machine Learning related patents cannot be spotted in relation to the
chemical industry.
• Software patenting is by tradition very limited. By looking at patents related
to machine learning and data management systems in the chemical
industry for the last five years, we found 87 and 73 patents respectively.
• The key technological development values are related to threshold,
accuracy, efficiency, robustness, overfitting, noise. These concepts sit
around quantifying, measuring, delivering, processing and demonstrate the
willingness of the players to continuously improve and automate their
processes.
• The leading category into which machine learning and data management
are used for innovation within the chemical sector is the medical field, where
it is used for gene editing, antimicrobial agent preparation, data processing
of samples or automatic diagnostic process.
• On a global level, US is the main player 1/3 of the activity. The leading
European player is Germany with 1/8 of the global activity.
2614 patents
Timeframe: 2013 – 2018
Total factory control &
predictive maintenance
[Cross-industry level]
POOL 1
We have analyzed 3 different patent pools
(see the results in the following pages)
87 patents
POOL 2
73 patents
POOL 3
Machine learning
Chemical industry
Programming tools
or database systems
Chemical industry

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The openness pledge in AI (1)
In the A.I. researcher community an open source
mentality has traditionally been applied. Even big tech
companies like Apple, Facebook, Amazon,
and Microsoft have all, like Google, released software
their own engineers use for machine learning as open
source. They have pledged commitment to openness
in artificial intelligence. The big tech companies seem
to have it both ways though:
“At the same time, these proponents of AI openness
are also working to claim ownership of AI techniques
and applications. Patent claims related to AI, and in
particular machine learning, have accelerated sharply
in recent years. So far, tech companies haven’t
converted those patents into lawsuits and legal threats
to thwart rivals. Google itself exemplifies the trend. In
2010, only one Google filing mentioned machine
learning or neural networks in its abstract or title,
according to a search of the USPTO database. In 2016,
there were 99 such filings from Google and other
Alphabet companies. Facebook filed for 55 patents
related to machine learning or neural networks in 2016,
up from zero in 2010. IBM, which has been granted
more US patents than any other company for the past
25 years running, boasts that in 2017 it won 1,400 AI-
related patents, more than ever before ”
U.S. patent filings in machine learning

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The openness pledge in AI (2)
The AI community is has a strong “ maker mentality “ of sharing code via platforms like github. Most
big advances in machine learning algorithms and techniques are shared by universities and big tech
companies. There are several reasons why this doesn’t necessarily amount to bad business rational:
• Tech companies often compete for market share, the tools they provide are an opportunity to bring
makers to their environments, use their infrastructure (cloud platform) and to gather information on
what is happening in the field.
• The competitive edge is often not in the machine learning algorithm but in the data used to train a
specific instance of that algorithm. The data and trained models are not always shared, the
techniques for training are.
Among the technologies that major tech companies have opened recently are:
• Amazon’s Alexa, the voice-command response system inhabiting the company’s Echo device,
opened in June 2015;
• Google’s TensorFlow, the heart of its image search technology, open-sourced in November 2015;
• The custom hardware designs that run Facebook’s M personal assistant, open-sourced
in December 2015; and
• Microsoft’s answer to these machine-learning systems, the prosaically named Computation
Network Tool Kit, made public last month, the latest addition to the public’s library of options for AI
systems.
Initiatives like Elon Musk’s OpenAI are aimed at facilitating and boosting the open culture in AI.

CONFIDENTIAL
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Our services are marketed through 5 brands each
addressing specific missions in product development.
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