Artificial Intelligence for Discovery

Artificial Intelligence for Discovery
Watson for Drug Discovery
Scott Spangler
July 20, 2018
Watson Health © IBM Corporation 2018

IBM is at the intersection of science and technology
Approximately
100 cognitive
health patents
filed in 2016
More than 7,000
employees
globally
We partner with industry
leaders to transform life
sciences.
IBM Watson Health Life-Sciences offerings
Drug development value chain
Market assessment
and molecule
development
(preclinical)
Clinical trial
protocol
development
Site selection
and patient
recruitment
Study conduct,
data collection
and operations
Study analysis,
clinical study
report and
submission
Postmarketing
and real-world evidence
studies (postmarket
requirements and
commitments)
Safety and
pharmaco-
vigilance
Research CommercializationDevelopment
Real World
Evidence
Watson for
Patient Safety
Watson™ Drug
Discovery
IBM Clinical
Development
Data assets:
Truven and Explorys®
software
From molecule …. to market…
More than 1500 Clinical
Trials conducted on our
technology
http://ibm.biz/wdd-tria

Watson Health © IBM Corporation 2018 3
Today, drug discovery is a manual, linear process driven by workflows
resulting in high costs and a high failure rate
Knowledge
Gathering
Hypotheses Experiments
Analysis &
Publications
Traditional R&D Process
Weeks to months Years
1DiMasia, J. et al. “Innovation in the pharmaceutical industry: New estimates of R&D costs.” Journal of Health Economics, Vol. 47 (2016), pp. 20-33.
• Internal information and insights generated are siloed
and not easily accessible between teams
• Difficult to stay up to date on the latest scientific
literature and assess the relevance of each publication
• Manual curation of connections across scientific
literature, no central storage
• Limited outside perspectives from other fields
Challenges
$2.5B*
The fully capitalized
cost of developing
and launching a
new drug in 2016.
100k+
Relevant articles
to consider

4
2010 2025
160 Zettabytes by 2025*
We are here
Computing power
Data growth Advances in neural networks,
machine learning and deep learning
Cloud
Data-centric
Systems
End-to-end
Security
Extreme
Scalability
*Source IDC. IBM projections based on analyst report
AI: Why now?
80% Unstructured

The Evolution of AI
We are here
Broad General
1980 2015 2050? Beyond
Narrow

“I feel so strongly that deep and simple is far more
essential than shallow and complex.”
-- Mr. Rogers
Guiding Principle:

Ten Corollaries to Mr. Rogers’ Law
1.Interpolation is preferable to Extrapolation
2.Confidence of “facts” must be accurately estimated
3.Cognitive Reasoning is useless without explanation
4.Structure implies behavior
5.The past is validation for the future
6.Unlikelihood is a measure of interestingness
7.Don’t look for an answer, look for a question
8.Use what you know as a map of the unknown
9.To know what you need to know, first know what you know.
10.Persistence is a virtue

Interpolation vs Extrapolation
Use what you know to fill in the gaps of what you do not know… far
more is implied than stated.
Implication rather than Prediction

Confidence Scoring – how to know what to believe
Two Factors Determine the Likelihood of a Fact being True
• Frequency of Occurrence (higher frequency -> more likely to be true)
• Consistency, measured by collaborative filtering (higher matrix factorization score ->
more likely to be true).
• At the limits:
• Infinite support = 100% confidence
• 1.0 MF = 100% confidence
• 0.0 MF and 1 support = lowest confidence
• P = aMFd
– b/Se
+ c
• Need to determine a, b, c, d and e experimentally.
• Process: Sample facts with different levels of Su and MF and determine the observed
P value.

Past is Validation for Future
Older Newer

Unlikelihood as a Measure of Interestingness

Don’t look for an Answer…
…. Look for a (better) Question
Don’t expect AI to be an oracle. Think of it as a collaborator. A really annoying one who
keeps saying, “What about…X?”

Use the Known to map the Unknown

To know what you need to know…
…you must first know what you know.
Training sets are hard work…get used to it.

Persistence is a virtue
Save your work!!
“Success is stumbling from failure to failure with no loss of enthusiasm.”
― Winston S. Churchill

Watson for Drug Discovery: accelerating discovery
Watson for Drug Discovery is acloud-based,end-to-end scalable platform that helps life science researchers
discover new disease pathways,new drug targets and additionaldrug indications
Visualization
Three core WDD components
Cognitive
Knowledge
External Internal
Public PrivateLicensed
• Big Data – structured & unstructured
• Scalable architecture
• Deep NLP & text mining
• Machine learning
• Domain adaptation
• Rich visualizations
• Researchers are able to interact with these data
points and knowledge maps through real-time
visualizations

Which new diseases might
a class of drugs or
compounds affect?
What other indications are
there for this known target?
What other biological
pathways does this
drug/compound/target
impact?
What are a set of targets
for a given disease area?
Which compounds are
good for which targets?
Which type of biological
pathways should we
consider?
What relationships might a
gene have with a set of
diseases and drugs?
What are some genes that
might be regulated by gene
X?
Are there new subtypes of
proteins involved in process Y
that occur in disease Z?
Watson for Drug Discovery helps researchers answer key questions to discover
new drug targets, predict gene function, and identify new drug indications
Target Identification
Gene Function /
Regulation Prediction
Drug Indications Commercial Applications
Can I assess the
effectiveness of my drug
(pharmacodynamics
biomarkers) in conjunction
with Real World Evidence
(RWE)?
Can I identify patient
segments (predictive
biomarkers) with RWE?

Watson for Drug Discovery looks broadly across
public, licensed and private data to unlock hidden
information and deliver insights
Knowledge Cognitive Visualization
*DISCLAIMER: Statistics are up to date as of 2/8/18
Clinical Trial
Data
Private
Ontologies
Electronic
Lab Notes
Proprietary
Compounds
Toxicology
Reports
Other Proprietary
Data
Private Data
(Client Confidential)
4.5M+
Patents
27,000+
Genes/proteins
12,000+
Distinct Drugs
1.7M+
Full-text Articles
27,000+
Distinct Conditions
28M+ Medline Abstracts
Clinicaltrial.gov
1M+ chemicals
Public Data Sources

Watson Health © IBM Corporation 2018 2121Watson Health © IBM Corporation 2017
Step 2: Organization
Domain Entities
Ontologies
(e.g. organism, disease, genes)
Step 3: Relationships
Step 4: Prediction
Known Pathways Predicted Links
Step 1: Entities
Unstructured
Entity Recognition &
Normalization
High Level Process for Accelerated Discovery

Diagram
Formula
Names
(149)
Chemical ID
Valium, Dizapam Alboral,
Aliseum,AlupramAmiprol, Asiolin,
Ansiolisina Apaurin, Apoepam, etc.
C16H13CIN2ORich dictionaries
enable Watson
to link all entity
representations
H C3
O
CI N
N
The process begins with recognizing and understanding the words
Annotating Biological Entities in Scientific Text
DB00829 (DrugBank)

…doxorubicin results in extracellular signal-regulated kinase (ERK)2 activation,
which in turn phosphorylates p53 on a previously uncharacterized site, Thr55…
Extracts Preposition
§ Recognizes preposition location on Thr55
Extracts Entities
§ ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid
Extracts Verb
§ Maps to domain of Post Translational Modification
§ Recognizes subject / object relationships
Extracts Entities
Extracts Entities
ERK2
phosphorylates
p53
on
Thr55
Annotating Biological Relationships in Scientific Text
Annotators allow Watson to read and extract meaning

24
WDD includes 2 complementary Predictive Analytics methods
CONSUMER ANALOG LIFE-SCIENCE EXAMPLE
Predictive Analytics
Semantic Similarity
Predictions
(“bag of words” + graph diffusion
methodology)
• Read 1000s of reviews of 20 film-
noir movies to understand the words
and phrases appearing therein
• Analyze 10,000s of other reviews of
1000s of other movies to identify
movies that are written about in a
similar fashion
• Read 1000s of abstracts that mention
23 kinases known
to phosphorylate p53
• Analyze 10,000s of other abstracts
mentioning 100s of other kinases to
identify kinases that are written
about in a similar fashion
Predict Relationships
Relationship-based
Predictions
(“matrix factorization”
methodology)
• Ingest survey data saying which
people like which movies
• Given a person in the list, predict
other movies that s/he might like
• Ingest data showing which Genes are
associated with which Diseases
• Given a Disease on the list, predict
other Genes that might be related
People
Movies
People
Movies
+ + +
+ +
+ +
+
+
+ + +
+? + + +?
+ +
+
+
Diseases
Genes
+ + +
+ +
+ +
+
+
+ + +
+? + + +?
+ +
+
+
Diseases
Genes
Watson Health © IBM Corporation 2017 IBM Confidential – For Registered WDD Users Only

Case Studies

WDD supported the identification of combination
therapies, new drug targets, and adverse events for
further investigation
"Applying the power of cognitive
computing to discovering new
medicines – is helping Pfizer to learn
how we can most efficiently discover
those immuno-oncology therapies
that have the best chance of
successful outcomes for patients.”
PFIZER
DRUG PRIORITIZATION: Prioritized 5 to 10 potential
IO drug combinations out of 140k possibilities for
further investigation
TOXICITY PREDICTION: Rank-ordered list of
potential adverse events for combination of targets,
drugs, and cell types
EFFICACY PREDICTION: Rank-ordered list of drug
targets by tumor type based on documented evidence
1
2
3
+
!
Laurie Olson, Executive Vice President,
Strategy, Portfolio, and Commercial
Operations, Pfizer
IO researchers are working to tailor drug combinations to tumor characteristics so
that more patients can eventually be treated
Watson for Drug Discovery is helping identify promising potential new target
combinations for immuno-oncology (IO) drugs

NEW DISCOVERY: Uncovered 5 never before linked
proteins altered in patients with ALS [1].
“By using Watson for Drug
Discovery we can make scientific
breakthroughs in a fraction of time
and cost, increasing our
knowledge of diseases faster than
ever before.”
Robert Bowser, PhD,
Chairman of Neurology,
Barrow Neurological Institute
BARROW NEUROLOGICAL INSTITUTE
RAPID EXPLORATION: Within weeks, using predictive
modeling, rank ordered ~1,500 RNA binding proteins for
their association to ALS. Validated through retrospective
analysis.
VALIDATED PREDICTIONS: Barrow examined Watson's
top evidence-based predictions & found 8 of the top 10
ranked proteins were linked to the disease.
ALS (Lou Gehrig’s Disease) is a devastating condition for which there is no cure
Watson for Drug Discovery helped identify 5 new proteins linked to amyotrophic
lateral sclerosis (ALS)
Syncrip, ranked #4 by Watson shows altered expression in patients withALS
[1] Bakkar N et al. Artificial intelligence in neurodegenerative disease research: use of
IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral
sclerosis. Acta Neuropathol 2017;124:339.

Naomi Visanji, PhD
Scientist,
University Health Network
UNIVERSITY HEALTH NETWORK
RAPID EXPLORATION: Rank ordered over 600 drug
candidates for likelihood of treating Parkinson’s disease
based on their ability to reduce the aggregation and/or
toxicity of the protein alpha-synuclein.
ACCELERATED RESEARCH: Enabled start of lab
validation efforts to evaluate drugs for repurposing for
L-Dopa-induced dyskinesia.
HYPOTHESES VALIDATION: Generated & evaluated
hypotheses by providing rationale for the links between
top drug candidates, alpha-synuclein, and Parkinson’s
disease [1].
?
Currently no disease-modifying compounds exist for Parkinson’s disease, and drug
repurposing offers the promise of finding a safe cure faster.
“We simply would not have and
could not have accomplished the
research we did without Watson.
The approach itself would not have
been feasible and the labor
intensiveness required would have
been too great”
[1] Kalia LV et al. (2017) In silico predictive analytics: accelerating identification of
potential disease-modifying compounds for Parkinson's disease. 13th International
Conference on Alzheimer's and Parkinson's Diseases, Vienna, Austria.
Watson for Drug Discovery equipped university of Toronto with cognitive tools to
accelerate drug repurposing for Parkinson’s disease

Text mining on
an annotated
corpus led to
identification of
cardiovascular
proteins
Methodology:
–– Using semantic similarity analysis and a training set of
known cardiovascular proteins, Watson evaluated 1,370
candidate proteins for their association with
cardiovascular phenotypes
Objective:
–– Accelerate classification and identification of proteins
associated with cardiovascular disease phenotypes
using and comparing with data from a clinical trial with
blood protein measurements
Business Problem:
–– Cardiovascular diseases continue to be the leading
cause of death in the US; about 1,400,000 people die
from cardiovascular diseases annually
–– Thousands of articles are published each year in this
field alone
Findings
–– In a predictive model, Watson ranked
cardiovascular proteins significantly higher than
non-cardiovascular proteins
–– Predictive ability is enhanced with increased
specificity of cardiovascular disease phenotype
(e.g. coronary atherosclerosis and heart failure
have high predictive value) [1]
Value of Watson:
–– New methodology could be applied to a variety
of diseases to prioritize what blood proteins to
measure as part of clinical trials
–– Long-term vision: Enabling design of cheaper
and more efficient clinical trial
[1] Ruff CT et al. (2017) Classification of Cardiovascular Proteins Involved in Coronary Atherosclerosis and Heart Failure Using Watson’s Cognitive Computing Technology.
Circulation. 2017;136:A16678.
Watson for Drug Discovery accelerated the classification of proteins involved in
cardiovascular diseases for a major academic research center

30Watson Health © IBM Corporation 2017
More about this Application
See recent program on the Discovery Channel
http://discovery.com/ThisIsAI

I - Publications
1. First article in collaboration with the Baylor College of Medicine, which goes into
detail into one of our predictive methodologies. Note that the "KnIT" platform is
now Watson for Drug Discovery.
http://dx.doi.org/10.1145/2623330.2623667
2. A follow up the original study with Baylor: "Predicting Future Scientific
Discoveries Based on a Networked Analysis of the Past Literature".
http://dx.doi.org/10.1145/2783258.2788609
3. A review article on Watson for Drug Discovery technologies.
http://dx.doi.org/10.1016/j.clinthera.2015.12.001
4. Artificial intelligence in neurodegenerative disease research: use of IBM Watson
to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis.
https://doi.org/10.1007/s00401-017-1785-8
5. Use of IBM Watson for Drug Discovery for predicting metabolites based on their
endocrine disruptive potential.
https://doi.org/10.1021/acs.analchem.7b02759
6. Classification of Cardiovascular Proteins Involved in Coronary Atherosclerosis
and Heart Failure Using Watson’s Cognitive Computing Technology.
http://circ.ahajournals.org/content/136/Suppl_1/A16678
II - Press
1. University Health Network -- Parkinson's disease:
https://www.thestar.com/news/insight/2017/04/15/can-watson-the-jeopardy-
champion-solve-parkinsons.html
https://www.ibm.com/thought-leadership/passion-projects/parkinsons-watson-
drug-discovery/
2. Pfizer -- Immuno-Oncology:
http://www.pfizer.com/news/press-release/press-release-
detail/ibm_and_pfizer_to_accelerate_immuno_oncology_research_with_watson_f
or_drug_discovery
3. Barrow -- ALS:
https://www.eurekalert.org/pub_releases/2017-11/sjha-brv113017.php
III - Inspirational Videos
1. University Health Network -- Parkinson's disease. Ted@IBM:
https://www.youtube.com/watch?v=l-1AC6CHSJM
2. Pfizer -- Immuno-Oncology:
https://youtu.be/_qqIwfnmVrU
3. Barrow -- ALS:
https://youtu.be/o8o-vM2Azlw
https://youtu.be/5E9Kda92_uU
Publications and Press

IBM'sstatements regarding its plans,
directions and intent are subject to change or
withdrawal withoutnoticeatIBM'ssole
discretion.
Information regarding potential future products is intended to outline our general
product direction and it should not be relied on in making a purchasing decision.
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about potential future products may not be incorporated into any contract. The
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our products remains at our sole discretion.

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Artificial Intelligence for Discovery

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