The term Artificial Intelligence was first coined in 1956 and since then the technology has progressed, disappointed, and re-emerged. Now the prediction is that AI will add $16 trillion to the global economy by 2030. AI is becoming as fundamental as electricity, the internet, and mobile as they were born into the mainstream. Not having an AI strategy in 2020 will be like not having a mobile strategy in 2010, or an Internet strategy in 2000. As a result of technology advancements, AI-related patent applications have surged over the recent years. The patent searchers, information professionals and bioinformatics researchers who have been involved in collecting data, organizing the data and analysing the data are starting to move up in the ladder with AI. Of course, AI can help you, your business, your employees, and your customers, but you need a prescriptive approach to harness its power and put AI to work. This presentation will take a glimpse under the hood of AI and look into some recent trends in data and analytics that are relevant to the information professionals.

1. AI is Not Magic:
It’s Time to Demystify and Apply
VINGYANI
AI-SDV 2020, Nice, France
05-06 October 2020
Srinivasan Parthiban

2. The Lineman’s Handbook of 1928
2010s
AI is the new electricity
- Andrew Ng
Today, too many people view artificial intelligence (AI) as another magical technology
that’s being put to work with little understanding of how it works.

3. Artificial in the sense that the
intelligence is not displayed by a biological organism
but rather by a machine!
Intelligence in the sense that it is defined as the
“ability to accomplish complex goals.”

4. Waves
of AI
DLML

5. Are We Heading
For Another AI
Winter Soon?

6. Data Tools
The AI Opportunity
Complex and Diverse
Applications

8. AI is transforming creative industries
cutting-edge content offers in a fast-changing landscape
With content accessible anytime and anywhere and
the emergence of both mainstream and niche platforms,
the impacts of new business models and new market players on a diversifying ecosystem,
including the ever more prominent role of technology from content creation to distribution.

9. Huge Surge in AI Patents
Top 4 categories (account for 42% of all patents):
Transportation (15%)
Telecommunication (15%)
Life and medical sciences (12%)
Personal devices, computing and
human-computer interaction (11%)
Patent families for top functional applications by earliest priority year
Computer vision grew by an average of 23% annually between 2011 and 2016

10. 2017 Transformer
2018 BERT, GPT
2019 GPT2
2020 GPT3
Transformers and Natural Language Processing

11. Publications
involving AI
methods
(e.g. deep learning,
NLP, computer vision,
RL) in biology are
growing
>50% year-on-year
since 2017. Papers
published since 2019
account for 25% of all
output since 2000.
Search done on
02 October 2020
Biology is experiencing its
“AI moment”:
We can expect over 20,000 papers in 2020
Number of articles

12. It all starts with FAIR …
The best machine-learning algorithms and advanced analytics
are meaningless if our data is not in order.

13. Drug Discovery and Development: A Long, Risky Road
This is
ultimately a
problem of
prediction!
2000’s
$2.6B
1970’s
$100M’s

14. Collaborations and consortiums:
Improving access to robust and reliable data: Data sharing is the new competitive advantage
The Accelerating Therapeutics
for Opportunities in Medicine
(ATOM) consortium is US-based
collaborative initiative for the
development of state-of-the-art,
AI-enabled drug discovery
processes.
It aims to significantly reduce
the preclinical drug discovery
timeframe for the patients’
benefit.
Established in 2017
The Machine Learning for
Pharmaceutical Discovery
and Synthesis (MLPDS)
consortium is a collaboration
started by the Massachusetts
Institute of Technology
involving 13 major
biopharma companies.
It aims to facilitate the
design of useful algorithms
for the automation of small
molecule discovery
Established in 2018
The Machine Learning Ledger
Orchestration for Drug Discovery
MELLODDY project is a consortium of
17 partners created to enable
effective sharing of the chemical
libraries of ten biopharma
companies, specifically for AI drug
discovery applications.
The collaboration is underpinned by
the use of blockchain technologies
aimed at improving the accuracy of
predictions for identifying better drug
candidates.
Established in 2019

15. Pharma Companies Join Forces to Train AI for
Drug Discovery Using Blockchain
The project aims at developing a
state-of-the-art platform for
collaboration, based on blockchain
architecture technology, which would
allow collective training of artificial
intelligence (AI) algorithms using data
from multiple direct pharmaceutical
competitors, without exposing their
internal know-hows and
compromising their intellectual
property -- for the collective benefit
of everyone involved.

17. Evolution of Pharmaceutical Research
In vivo In vitro In silico
Time
In machino
AI

18. AI vs Covid - 19

19. Number of Covid-19 publications in 2020
Covid-19

20. SARS-CoV-2 - The Coronavirus
Coronavirus Structural Task Force
Main Protease

21. Drugs interact
with proteins like a
lock and key

22. The challenge posed to each
participating team was to screen a
billion molecules on their
interaction with Covid19 (‘affinity’)
Billion Molecules
against Covid-19

23. Drug discovery goes open source
to tackle COVID-19.
This is a rare example of where AI is being
actively used on a clearly-defined problem
that’s part of the COVID-19 response.
An international team of scientists are
working pro-bono, with no IP claims, to
crowdsource a COVID antiviral.
Crowdsource Scientists
PostEra - AI prediction
CRO
Academic labs
Crowdfunding

24. Covid-19 SAR Data from ChEMBL

25. CAS COVID-19 Antiviral Candidate Compounds Dataset

26. Parthi and his team won Covid Hackathon

27. Generative Models

28. An unexpected year for Data Science
May 2020
January 2020
2020: A very busy year for Data Science

29. AI isn’t magic,
and there’s a lot more to it than just bunch of math
The 7 Steps of Machine Learning
• Ask the right questions
• Get the right data
• Use algorithms to make recipes from patterns in the data
• Check that the recipes work on new data
• Build a production-ready system
• Make sure that launching is a good idea
• Keep your production ML system reliable over time

30. sc
Vingyani, a DeepTech company, combines cutting edge science and advanced engineering with the objective of making a profound impact on humanity.
Artificial Intelligence for Pharmaceutical Research
Data Science Consulting and Advanced Analytics
AI-SDV, France
October 05-06, 2020Thank you very much!
We offer custom solutions and
development in data strategy,
python programming, machine learning,
NLP, computer vision, and predictive analytics.
parthi@vingyani.com