Watson for Drug Discovery is a cloud-based platform that aggregates diverse biomedical data sources to help researchers discover new disease pathways and drug targets. It uses natural language processing, predictive analytics, and dynamic visualizations. Case studies showed that Watson correctly predicted several known kinase interactions with the p53 protein and prioritized drug combinations for cancer immunotherapy. Researchers were able to generate novel hypotheses faster and more efficiently with Watson than traditional literature review methods.

1. Watson Health © IBM Corporation 2017 1
IBM Watson Health
Watson for Drug Discovery
August 2016

2. Watson Health © IBM Corporation 2017 22Watson Health © IBM Corporation 2017
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3. Watson Health © IBM Corporation 2017 3
Work With WatsonTM
Imagine a World Where…
access structured and
unstructured datafrom
disparatesourcesin
seconds
quicklyuncover novel
patterns and connections
across domains and
therapeutic areas
focus their time and
resourcesinvestigating
selected targets supported
byevidence
more efficient and
informed decision-making
effective drugs reaching
patients sooner
Researchers can:
Which can lead to…

4. Watson Health © IBM Corporation 2017 4
Work With WatsonTM
Imagine a World Where…
access structured and
unstructured datafrom
disparatesourcesin
seconds
quicklyuncover novel
patterns and connections
across domains and
therapeutic areas
focus their time and
resourcesinvestigating
selected targets supported
byevidence
more efficient and
informed decision-making
effective drugs reaching
patients sooner
Researchers can:
Which can lead to…
TheGoalofWatsonfor
DrugDiscovery istoMake
This aReality

5. Watson Health © IBM Corporation 2017 55Watson Health © IBM Corporation 2017
Watson for Drug Discovery

6. Watson Health © IBM Corporation 2017 6
Watson for Drug Discovery
Watson for Drug Discovery: Accelerating Discovery
Watson for Drug Discovery is a cloud-based, end-to-end scalable platform that helps life science
researchers discover new disease pathways, new drug targets and additional drug indications
Visualization Key Capabilities
• Aggregates diverse content
• Cognitive technology
• Scalability
• Domain understanding
• Agility / speed
Three Core WDD Components The Watson Advantage
Key Benefits
Helps:
• Accelerate insight generation
• Improve researcher productivity
Cognitive
Knowledge
External Internal
Public PrivateLicensed

7. Watson Health © IBM Corporation 2017 7
Watson for Drug Discovery
Watson for Drug Discovery looks broadly across public, licensed and
private data to unlock hidden information and deliver insights
Knowledge Cognitive Visualization
4M+
Patents
35,000+
Genes
10,000+
Distinct Drugs
150+ Medical
Textbooks
700,000+
Scientific Articles
90,000+
Distinct Conditions
25 Million+ Medline
Abstracts
200+ Scientific
Journals
Public and Licensed
Data Sources
“DISCLAIMER: Statistics are up to date as of 7/14/16”

8. Watson Health © IBM Corporation 2017 8
Watson for Drug Discovery
Watson for Drug Discovery looks broadly across public, licensed and
private data to unlock hidden information and deliver insights
Knowledge Cognitive Visualization
4M+
Patents
35,000+
Genes
10,000+
Distinct Drugs
150+ Medical
Textbooks
700,000+
Scientific Articles
90,000+
Distinct Conditions
Clinical Trial
Data
Private
Ontologies
Electronic
Lab Notes
Proprietary
Compounds
Toxicology
Reports
25 Million+ Medline
Abstracts
Other Proprietary
Data
200+ Scientific
Journals
Private Data
(Client Confidential)
Public and Licensed
Data Sources
“DISCLAIMER: Statistics are up to date as of 7/14/16”

9. Watson Health © IBM Corporation 2017 9
Watson for Drug Discovery
Watson for Drug Discovery looks broadly across public, licensed and
private data to unlock hidden information and deliver insights
Knowledge Cognitive Visualization
4M+
Patents
35,000+
Genes
10,000+
Distinct Drugs
150+ Medical
Textbooks
700,000+
Scientific Articles
90,000+
Distinct Conditions
Clinical Trial
Data
Private
Ontologies
Electronic
Lab Notes
Proprietary
Compounds
Toxicology
Reports
25 Million+ Medline
Abstracts
Other Proprietary
Data
200+ Scientific
Journals
Private Data
(Client Confidential)
Public and Licensed
Data Sources
“DISCLAIMER: Statistics are up to date as of 7/14/16”

10. Watson Health © IBM Corporation 2017 10
Watson for Drug Discovery
Your Data Remains Private
In a private instance, Watson
for Drug Discovery draws from
aggregated public and private
data, while maintaining your
data’s security and privacy.
Clients have two options
for accessing Watson for
Drug Discovery
–Public instance with shared Watson
base content corpus
–Dedicated private instance with client
proprietary content
Knowledge Cognitive Visualization
UI
Client Unique
Query Insights
Configuration of Private Instance
APIs
APIs
Client PrivateWatsonfor Drug
Discovery Instance
Annotators Annotators
(Including Client-unique Adaptations)
Public, Licensed
Content
Private,Client Provided Content (Client
Confidential)

11. Watson Health © IBM Corporation 2017 11
Watson for Drug Discovery
The Role of Cognitive in Watson in Drug Discovery
Cognitive Platform
Natural Language Processing
Watson Reads
Watson Understands
Watson Evaluates
Watson LearnsExternal Internal
LicensedPublic Private
–Trained with domain
specific dictionaries,
ontologies and subject
matter experts
–Capable of understanding
semantic and contextual
meanings
–Leans the language of
healthcare and life
sciences
–Can improve decision
making: reasoning
algorithms and predictive
models backed by
evidence
–Helps generate novel
hypotheses by predicting
potential relationships that
may not be known
Predictive Analytics

12. Watson Health © IBM Corporation 2017 12
Watson for Drug Discovery
Dynamic Visualizations to Look Broadly and Deeply
After Watson synthesizesthe dataand layers in cognitive technology to derive insights,researchers are
able to interact with these datapointsand knowledge maps through real-timevisualizations
With solutions like Watson for
Drug Discovery, Watson Health is
creating a new partnership
between people and technology
that can help enhance, scale and
accelerate human expertise
– Dynamic visualizations map detects connections between entities
– Rich visuals enable users to make sense of large volumes of data,detect the signalamong the noise and generate new insights

13. Watson Health © IBM Corporation 2017 1313Watson Health © IBM Corporation 2017
Case Studies

14. Watson Health © IBM Corporation 2017 14
Step 1
Step 2
Step 3
Watson for Drug Discovery
Watson for Drug Discovery Approach
Step 4 Look beyond to predict and
generate new hypotheses
Look holistically to understand
relationships and provide a
complete view
Loop deeply to organize and
understand the domain
Look broadly, aggregating rich and
diverse content and identifying
entities
Value of
Watson for
Drug
Discovery
Function
Form
Jak3
TFC7 TFC5 SER1
Jak1
Jak2
P53 ATM
Gene A Gene Bor
–
Known Pathways
---
Predicted Effects
Ontologies
(organization of
entities)
Unstructured Domain Entities
(genes/proteins, drugs, and diseases)

15. Watson Health © IBM Corporation 2017 15
Case Studies
Watson for Drug Discovery: Therapeutic Focus Areas
IBM Watson Health is currently working with clients and academic partners to deepen Watson for Drug
Discovery’s expertisein these prioritized therapeutic areas:
Sample
Engagements Oncology
–Accelerate discoveryof new kinases
phosphorylating p53
–Identifypromising new gene target
combinations for immuno-oncology drugs
–Identifypotential new biomarkersfor
colorectalcancer
Neurology
–Uncoverunknown subtypes of proteins
involved in motor neuron function in ALS
–Pinpointpromising new gene target
combinations for glioblastoma drugs that target
multiple disease pathways
–Investigateopportunities for alternative
indications for acompound in development
for MS
We are also working with
third parties to extend to
additional therapeutic
areas, including
immunology, diabetes,
infectious diseases and
cardiovascular disease

16. Watson Health © IBM Corporation 2017 16
Case Studies: Baylor College of Medicine
Watson helped accelerate cancer research to identify
protein kinase activity on p53 through the understanding of
scientific language and the use of predictive analytics
• In a retrospective analysis, correctly predicted 7 of 9
known p53 kinases; it took researchers worldwide 10
years to discover these 7 kinases while Watson
identified and prioritized them in a matter of weeks
• Prospectively identified an additional 6 candidates for
further research through the use of NLP and predictive
analytics

17. Watson Health © IBM Corporation 2017 17
Case Studies: Major Pharmaceutical Company
Watson helped identify promising potential new target
combinations for immuno-oncology drugs, utilizing
predictive analytics in a shortened timeframe
• Correctly ranked 4 of 5 evaluation combinations in the
top 2% of all combinations
• Retrospectively predicted IO gene candidates up to six
years in advance of their published discoveries
• Prioritized 5-10 potential IO drug combinations out of
140k possibilities for further investigation by evaluating
emerging patterns in the literature

18. Watson Health © IBM Corporation 2017 1818Watson Health © IBM Corporation 2017
Let’s get started
To get started, Watson Health will work with you to:
–– Identify a clear problem to solve
–– Understand the data sources you need
–– Be committed to training

19. Watson Health © IBM Corporation 2017 1919Watson Health © IBM Corporation 2017
Appendix

20. Watson Health © IBM Corporation 2017 20
Appendix
Powering Breakthrough and Tailored Solutions for R&D
Entity
Explorer
Look holistically at
genes, diseases and
drugs
Watson for Drug Discoveryhelps researchersgenerate new hypothesesand makeconnections that may
not have been previously considered,which canhelp lead to the discoveryof new insights,breakthroughs
and improvement in R&Dimpact
Chemical
Search
Search for similar
compounds by
name, composition
or structure
PTM
Summary
Explore post-
translational
modification events
for a protein
Co-occurrence
Table
Discover affinities
between genes,
drugs, diseases and
identify overlaps and
relationships
between entities of
interest
Biological
Entity Network
Visualize the specific
connections and
relationships
between genes,
drugs and diseases
Entity
Recognition
Generate new
hypotheses and
predict potential
relationships that
may not be known
Exploration Discovery

21. Watson Health © IBM Corporation 2017 21
Watson is helping researchers identify genes linked or
associated with specific viruses and serotypes
Value of Watson:
• Validation: Validated concepts and genes as
associated with viral infections and serotypes
• New Information: Identified new potential gene
differentiators and derived high-level biological
signatures that may point to previously unknown
differences between viral serotypes and between
pathogenesis of different flaviviruses.
• Novel Hypothesis: Helped researchers generate a new
hypothesis around a potential unique mechanistic
interplay differentiating the virus of interest from other
flaviviruses, and differentiating between serotypes by
uncovering patterns and connections in comparative
rankings and biological network exploration
Objective:
• Leverage Watson’s cognitive classification and machine learning
capabilities to drive innovative research related to pathogenesis and
protection to yield novel hypotheses
Business Problem:
Client had need to better understand why vaccine performance varies by serotype
• Data overload with thousands of potential factors to consider
• Limited ability to identify differentiating factors associated with viral serotypes,
pathogenesis, and protection
• Complex datasets that cause difficulty in extracting meaningful biological
signatures of processes
Preliminary Findings: Novel Hypothesis Generated
• Identified a potential differentiator for viral pathogenesis/protection which is
not directly described in the literature, making it unidentifiable through
classic literature mining tools
Identify
differentiating
factors associated
with viral serotypes,
pathogenesis, and
protection from
relevant literature
IBM Watson for
Drug Discovery
VACCINES
Discover Validate Apply
Appendix

22. Watson Health © IBM Corporation 2017 22
Canadian neuroscience leaders tap IBM Watson to speed time
for drug repurposing in Parkinson’s disease
Value of Watson:
• "We simply would not have and could not have
accomplished the research we did without WDD. The
approach itself would not have been feasible and the
labor intensiveness required would have been too great
." Dr. Naomi Visanji, Scientist, UHN
• In addition to prioritizing top-ranking candidates, Watson
also helped generate and evaluate these hypotheses
by providing molecular rationale for the links between
top drug candidates, alpha-synuclein, and Parkinson’s
disease.
Objective:
• Empower researchers with cognitive tools that will help speed drug repurposing
for Parkinson’s disease, and increase the likelihood of bringing effective
therapies to patients more rapidly.
Business Problem:
• Parkinson’s disease affects an estimated 10 million people globally, and
currently there is no cure. Furthermore, it can take up to 10 years and $2.6
billion to bring a drug to market.
• Due to the prohibitive cost and time for developing new therapies, repurposing
existing drugs is an attractive option; however, there are thousands of approved
drugs that could be investigated and researchers are overwhelmed by volume
and pace of emerging data.
Preliminary Findings:
• Watson rank ordered a list of over 600 drug candidates for likelihood of treating
Parkinson’s disease based on their ability to reduce the aggregation and/or
toxicity of a alpha-synuclein, a key protein in the Parkinson’s disease
neurodegenerative process
• Watson provided supporting evidence for the top ranking candidates through
relevant articles and biological relationship extracted from text.
• Top candidates will be validated for effects on alpha-synuclein aggregation
and toxicity in the lab as well as in epidemiologic studies assessing incidence
and outcomes in PD.
Prioritizing drugs to
repurpose for
Parkinson’s Disease
IBM Watson for
Drug Discovery
Appendix

23. Watson Health © IBM Corporation 2017 23
Select higher quality biomarker candidates to accelerate the
discovery process
Value of Watson:
• Provided a comprehensive view of potential biomarkers
by analyzing the scientific literature and helped to
identify emerging trends/new information, such as
clinicalrelevance and response/non-response
• Quicklyexplored and ranked thousands of potential
candidates beyond what can be assessed using
traditional methods
• Provided an objective,repeatable machine-based
algorithm that minimizes subject matter experts’ bias
Objective:
• Identify potential new biomarkers for colorectal cancer
Business Problem:
• Thousands of potential candidates to consider
• Limited abilityto identify biomarkers with sufficient specificityand sensitivity
• Difficult to extract meaningful molecular signatures of biological
processes from complex datasets
• Nocombineddata-driven or knowledge-based methods are currently
applied for biomarker discovery
Preliminary Findings:
• Provided a ranked list of candidates, of which the top10-25 could be
researched for further validations
Hypothesizing novel
biomarker
candidates for
colorectal cancer
from relevant
literature
IBM Watson for
Drug Discovery
ONCOLOGY
Discover Validate Apply
Appendix

24. Watson Health © IBM Corporation 2017 24
Watson is helping to form and validate new hypotheses
related to glioblastoma
Value of Watson:
• Watson’s top candidate tumor gene already has
support from lab experiments, demonstrating the
value of an objective and innovative Watson-based
approach
• Watson provided a methodology to accelerate
research and reduced cost, time and manual effort
Objective:
• Identify the genes that regulate cells that can become tumorous
• Uncover promising new gene target combinations for glioblastoma
drugs that target multiple disease pathways
Business Problem:
• No effective drugs available and all clinical trials have failed
• Too many hypotheses that would take years to test; there is a need for
methodologies to prioritize these hypotheses
Predicting novel
drug combinations
to treat
glioblastoma
NEUROLOGY
IBM Watson for
Drug Discovery
Research results
expected in mid-2017
Preliminary Findings:
• Predicted genes to be involved in the transformation of normal
brain cells into tumor cells
• Identified seven potential drug combinations that could target
multiple disease pathways implicated in glioblastoma
Appendix

25. Watson Health © IBM Corporation 2017 25
Watson is identifying drug repurposing candidates to treat malaria
Value of Watson:
• Based on analytics and modeling of knowledge of
malaria, related genes, proteins, targets, and
metabolic pathways, new potential malaria
treatments were identified from drugs approved for
other purposes
• It took the client more than a year to generate a list
of 12 drug candidates; it took Watson four weeks to
generate a list of 11, half of which matched those
in the client’s list
Business Problem:
• Time-intensive process to identify new and potentially more effective
treatment options
Understanding
Watson potential
value and
capabilities in life
sciences
IBM Watson for
Drug Discovery
Preliminary Findings:
• IBM provided a ranked list with predictions of 11 potential drugs
for further investigation; 6 matched those of the client list, 5 were
novel suggestions
Objective:
• Accelerate the identification of existing drugs that can be
repurposed to treat malaria
Appendix

26. Watson Health © IBM Corporation 2017 26
Watson is accelerating academic research to help doctors develop
novel treatments for cardiovascular diseases
Value of Watson:
• Demonstrated predictive capabilities; Watson
successfully ranked “blinded” positive proteins (not in
the training set) higher than others in the list of
candidate proteins
• Watson proposed novel protein biomarkers associated
with cardiovascular outcomes using semantic similarity
analysis on known cardiovascular biomarkers
• This analysis will help more targeted treatments for
cardiovascular disease
Objective:
• Accelerate identification of proteins associated with cardiovascular
disease and disease outcomes
• Identify genes associated with certain cardiovascular disease subtypes
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
• Challenging to analyze the volumes of data in the context of all that is known
about cardiovascular diseases
• Cardiovascular diseases treatments will require approaches adapted for each
individual
Hypothesizing
novel targets for
cardiovascular
disease from
relevant literature
NEUROLOGY
IBM Watson for
Drug Discovery
Research results
expected in mid-2017
Preliminary Findings:
• Evaluated over 1,000 candidate proteins to arrive at a ranked
set of 1,200 proteins for further study as biomarkers for
cardiovascular disease
Appendix

27. Watson Health © IBM Corporation 2017 27
Watson is helping Barrow Neurological Institute form and validate
new hypotheses related to amyotrophic lateral sclerosis (ALS)
Value of Watson:
• Watson predicted proteins that are likely to bind RNA
and form aggregates by quickly analyzing and ranking
100s of potential protein candidates
• Validated initial predictions against “blinded” data,
demonstrating Watson’s predictive capabilities
• Prioritized top protein candidates using Watson’s
relationship extraction capabilities as well as client data
Objective:
Key questions to investigate:
• Are there unknown subtypes of proteins involved in ALS?
• Can these proteins form aggregates that cause motor neurons death?
Business Problem:
• Medication can slow ALS and reduce discomfort, but there is no cure and
the disease is fatal
• Death of motor neurons leave ALS patients physically disabled, and
researchers are still unclear as to which proteins are involved in these
processes
Preliminary Findings:
• From nearly 1,500 candidate proteins, Watson helped predict which are
most likely to bind RNA and have the capacity to form aggregates
• Initial analysis using public experimental data and new lab experiments
lends support to Watson’s predictions
• 90% of the top ranked targets were proved to be linked to ALS
• 5 new proteins identified in months rather than years by analyzing large
amounts of disparate data more quickly than traditional methods
Predicting novel
proteins that could
be potential drug
targets for ALS
IBM Watson for
Drug Discovery
Full research results expected in
2017. Scientific abstract to be
presented in two major neuroscience
conferences in Nov and Dec 2016.
Appendix

28. Watson Health © IBM Corporation 2017 28
Watson is enabling researchers to uncover insights around gene
regulation
Value of Watson:
• Create accurate, meaningful predictive models
• Aid in the discovery of gene regulatory pathways
affected by drug of interest
Objective:
• Identify the genes most impacted by the loss of function of
Gene X in cancers
• Detect how Gene Y mutation affects expression levels of genes
involved in cancer cell metabolism, cell cycle, and/or growth and
whether these genes are likely to be up- or down-regulated
Business Problem:
• Lack of understanding around the genes regulated by Drug X target,
leading to difficulties in discovering biomarkers that indicate patient
responder status and in designing alternative drugs
Driving innovative
research in the field
of oncology and
yielding novel
findings
IBM Watson for
Drug Discovery
Research results will be
available mid-2017
Preliminary Findings:
• Watson ranked many known positive genes regulated by Gene X
within the top 10% of candidate genes; Watson’s other top
candidates are being studied in the lab
• When assessing the impact of Gene Y mutation, Watson’s
predictions showed good agreement with available assay
Appendix

29. Watson Health © IBM Corporation 2017 2929Watson Health © IBM Corporation 2017
Thank you