This presentation will show the introduction of AI and its possible implementation in Pharmaceutical Industry such as drug discovery, personalized medicine, molecular target prediction, site selection, patient recruitment, process automation, process optimization and more.

1. Artificial
Intelligence in
Pharmaceutical
Industry
Kevin Lee, Director of Data Science

2. The Agenda
➢ The Data Trend
➢ Introduction of Artificial
Intelligence (AI)
➢ AI implementation
➢ Future of AI
➢ Questions and Discussion

3. Q1 : World’s Largest Transportation Company?
•Available in 83 countries and
over 674 cities worldwide
•> 77% of US ride-hailing market
•Total number of rides by 2016 – 2
billion
•Number of Uber drivers
worldwide – 1.5 million
•Uber values: $3.9 b in 2013 to
$68 billion in 2017

4. • Inc’s 2014 Company of the Year
• Serving 3 million listings in 65,000 cities and 200
countries.
• Airbnb is valued at $30 billion.
Q2: World’s largest accommodation
provider?

5. Common Characteristics of
Exponential Organization
➢Data (Big)
➢Algorithm
Exponential &
Scalable Growth

6. How will Data (Big) and Algorithm
derive the innovation?
Better
Algorithms
Better
Products
More
Users
More Data

7. The Economist - The world’s most valuable
resource is no longer oil, but DATA.

8. Big Data
Artificial
Intelligence

9. Intelligence exhibited
by Machine
• Learning
• Problem Solving
• Prediction

10. Why AI is so popular now?
• General purpose technologies that
impacts all the business and daily lives.
• Ex. Electricity, oil, internet, iPhone

12. Voice Recognition
• Siri
• Alexa
• Google Assistant
Voice Recognition is 3
times faster than typing on
cell phone

14. Recommendation
• Amazon
• Netflix
• Spotify

15. Customer
Service
• Chatbot
• Call

16. AlphaGO

18. Other AI applications
• Smart Cars
• Google self-driving cars
• Telsa’s autopilot
• Purchase Prediction
• Fraud Detection
• Security Surveillance

19. What is
Machine
Learning?
An application of
artificial intelligence
(AI) that provides
systems the ability to
automatically learn
and improve from
experience without
being explicitly
programmed.

20. How does Human Learn?
- Experience

21. How does Machine learn?
Algorithm
Input
Data

22. Machine finds best model
using input data
X
Y
hθ(x) = x + 30
hθ(x) = 2x + 30

23. Best model can provide best
predicted value
X
Y
Xi
Yi

24. More data,
Better model,
Better prediction
X
Y
X
Y
Different data,
Different model,
Different prediction

25. X
Y
Garbage
in
Garbage
out
Bad data, Bad model, Bad prediction

26. Artificial Neural Network (ANN)
• Most powerful ML
algorithm
• Game Changer
• Works very similar to
human brain – Neural
Network
• Types – DNN, CNN, RNN
• Architecture
• Artificial Neurons
• Input Layers
• Hidden Layers
• Output Layers

27. Human Neural Network vs Artificial
Neural Network (ANN)
➢ Human
Neurons :
Neural
Network – 100
billions
➢ Artificial
Neurons :
Artificial Neural
Networks

28. So, why is it a big deal?
• We know more than we can tell. We
can’t explain exactly how we are doing
a lot of things. Now, using ML, we can.
Ex. Face-recognition
• ML systems are often great learners –
superhuman performance.
Ex. Go, Jeopardy

29. Why is AI(ML) so popular now?
• Cost effective
• Automate a lot of works
• Can replace human labors
• “Pretty much anything that a normal person can do in
<1 sec, we can now automate with AI” Andrew Ng
• Accurate
• Better than humans
• Can solve a lot of complex business problems

30. Healthcare AI market
• US - 320 million in 2016
• Europe – 270 million in 2016
• 40% annual rate
• 10 billion in 2024
• AI Start up driven growth

31. AI will be the primary drug
discovery tool by 2027

32. AI application in Pharma R&D
• Drug discovery
• Drug candidate selection
• Clinical system optimization
• Medical image recognition
• Medical diagnosis
• Optimum site selection / recruitment
• Data anomality

33. AI application in Pharma R&D
• Molecular target prediction
• Protein structures to predict drug
interactions and drug bioactivity
• Treatment of patients
• Recommendation
• Prediction treatment outcomes
• Guide of new therapy developments

34. Why Pharmaceutical companies look for
AI? – Discovery and Development

35. Why
Pharmaceutical
companies look
for AI? –
Personalized
Medicine

36. Adoption of AI in
Pharma/Healthcare
• Slow
• Regulatory
• Big investment in Healthcare and a lot of
AI Start up aiming Pharma

37. Future AI adoption
• Insight of current
business model
• Process optimization
• Process automation
• Data driven new
business

38. “We didn’t do
anything wrong,
but somehow, we
lost”

39. Thanks!!!
Please contact
kevin.kyosun.lee@gmail.com
https://www.linkedin.com/in/
HelloKevinLee/