Everything you want to know about role of artificial intelligence in drug discovery. Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python, deep neural network, GANs AI in drug discovery and development AI in clinical trials

1. Artificial intelligence (AI)
in drug discovery
Available on Slideshare
from May 12, 2017 09.30 PM
Amit ka PPT
(Amit’s PPT)
Dr. Amit Ratn Gangwal Jain
(MPharm., PhD.)

2. The photographs are the properties and
talent of original creator and/or right
holders. All the images are taken from
net. Thanks

3. Drug discovery is not
everyone’s cup of tea/coffee.
• Developing a new drug costs an
average of nearly $2.6 billion and may
take as long as 1.5 decades.
• Artificial intelligence will play crucial
role in curtailing this sum and time
frame.

4. What AI can do?
Artificial intelligence is showing the
potential to be a faster, more efficient
way to find and develop new drugs. A
growing number of organizations &
universities are focusing to minimize the
complexities involved in classical way of
drug discovery by using AI computing to
envisage which drug candidates are
most likely to be effective treatments

5. What if
• A software generates new molecular
structures by combining properties of
existing drugs.
• Automatic chemical design helps drug
discovery team jump to conclusion in a
faster & accurate manner.
• A treatment method from the ground up
using a deep learning neural network.

6. Continue….
• A system to use historical, biological & chemical data
to imagine novel molecules with the potential to fight
major diseases.

7. Leading pharma companies already
started working on AI enabled drug
discovery models or collaborating
with tech/IT organizations.
Established tech/IT companies have
also started diverting/expanding
their portfolio towards drug
discovery.

8. AI techniques in use for drug discovery
• Deep learning technique known as a generative adversarial
network (GAN) by Baltimore based company, Insilico
Medicine.
• GPU (graphics processing unit)-accelerated deep
learning to target cancer and age-related illnesses by
above organization.
• BenevolentBio’s deep learning software, powered by
the NVIDIA DGX-1 AI supercomputer (it ingests & analyzes
the information to find connections and propose drug
candidates).
• BenevolentBio aims to reinvent drug discovery by using
deep learning and natural language processing to
understand and analyze large pool of data originated from
patents, genomic data and the more than 10,000
publications uploaded daily across all biomedical journals
and databases.

9. https://www.slideshare.net/Docker/using-docker-for-
gpuaccelerated-applications-by-felix-abecassis-and-
jonathan-calmeis (source for below slide)

11. https://www.slideshare.net/JayanthiRepalli/ai-applications-in-life-sciences-drug-
development?qid=05e847a3-7478-4928-8e7c-
cb0700c059fe&v=&b=&from_search=3 (source for below slide)

14. You may see this original blog from for latest and solid
updates on application of AI in healthcare.
(https://blogs.nvidia.com/?s=healthcare)

15. Jobless: What experts have
to say?
Tom Reuner, SVP of intelligent automation and IT
services at IT consulting firm HfS, believes that
employees have to incessantly re-invent themselves
as the journey toward digital transformation
necessitates new skill sets and continuous learning.
"Many employees will struggle to make that journey.
But equally, many service providers will struggle to
adapt to these new realities. Skills will become more
important than just scale," he said. Ray Wang, CEO
of Constellation Research, said cloud, artificial
intelligence, and software platforms will lead to 20%-
30% reduction of staffing by 2020.
http://timesofindia.indiatimes.com/business/india-business/technology-giants-prepare-for-
layoffs/articleshow/58585806.cms

16. Job or jobless

17. http://epaper.bhaskar.com/indore/129/12052017/mpcg/12/

18. Summary
• Artificial intelligence enabled techniques
(AIET) assist drug discovery team in
finding perfect or most optimum lead
among great number of tentative drug
candidates.
• AIET can generate large pool of data
based on input and customized solution
you need.
• AIET can wrangle tens of crores of data
to present you with the most optimum
set of data, by filtering unwanted one.

19. Continue
• AIET’s most important job/tool is to
extract only those relevant data (from
huge data pool, from patent data base,
journal data base and other data base
source) which is most consistent,
significant and result oriented from drug
discovery point of view for targeted
disease. No doubt here input giving
rights will be resting with humans only.

20. Continue
• But once after feeding raw intelligence
to machines, AIET will not be in human
control for that particular shot. Later
you may again change codes as per
your expected outcome.

21. Benefits of AI
1. Get rid of data mining by humans.
2. Saving of time.
3. After initial set up, expenses will be
less vs classical way to discover drug
(at least for a particular research
direction)

22. Good news is that
Artificial intelligence will never
eradicate the jobs of scientists unlike it
may do for other industries where
danger of losing jobs is looming like
anything. This is so because, here
health aspect of people is associated
and therefore even an iota of AI enabled
stuff has to be ensured and validated
by scientists.

23. For more information, you may go through my
editorials in PharmaTutor.

24. Thanks