Computational approaches using AI are being used to speed up drug discovery and clinical trials in the following ways: (1) AI is being applied to large datasets to help identify new biomarkers and repurpose existing drugs, with the global AI healthcare market expected to reach $36.1 billion by 2025. (2) Major pharmaceutical companies are collaborating and sharing data using AI to accelerate target identification and automate molecule design. (3) Startups are generating huge image datasets from high-throughput drug screening experiments to help identify new drug candidates in areas like oncology. (4) AI can help improve clinical trials by identifying best patient populations, enabling dynamic trial design adjustments, and improving patient access and

1. How to use computational approaches to
find new biomarkers and repurposing drug
candidates?
By Dr. Sukant Khurana
beatcovidcampaign@gmail.com
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2. My passions
• Infectious diseases
• Parkinson’s and epilepsy
• Supplements
• Nutraceuticals
• AI for drug discovery
• Genomics
• AI, blockchain, and IOT
• Holistic education for 4th
industrial revolution
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www.towardsthe4th.com
www.ioncurerx.com

3. How AI is taking over the industry?
Global AI in healthcare market is expected to reach a value of $36.1 billion by 2025, compared
to just $2.1 billion in 2018.
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9. Current trends
• Speed to market. Current cost ~ US$2.6-billion.
• Top 10 pharmaceutical companies are using AI to share their data as part of
the Machine Learning Ledger Orchestration for Drug Discovery (Melloddy
project) Collaboration for data sharing.
• Eli Lilly’s partnership with Atomwise is speeding up target identification.
• The Machine Learning for Pharmaceutical Discovery and Synthesis
Consortium at MIT, is a data sharing program that includes companies such
as GlaxoSmithKline (GSK), AstraZeneca and Eli Lilly for automating molecule
design.
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10. Current trends
• Pfizer is using IBM Watson for immuno-oncology drugs.
• Sanofi is collaborating with UK start-up Exscientia’s artificial-
intelligence (AI) platform to hunt for metabolic-disease therapies.
• Roche subsidiary Genentech is using an AI system from GNS
Healthcare in Cambridge, Massachusetts, for cancer treatments.
• 12 June 2007, a robot called Adam, identified the function of a yeast
gene. By searching public databases, Adam generated hypotheses
about which genes code for key enzymes that catalyse reactions and
used robotics to physically test its predictions in a lab.
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11. Current trends
• In January, Eve, had discovered that triclosan, a common ingredient in
toothpaste, could potentially treat drug-resistant malaria parasites. The
researchers developed strains of yeast in which genes essential for growth
had been replaced with their equivalents either from malaria parasites or
from humans. This identified triclosan as affecting malaria-parasite growth
by inhibiting the DHFR enzyme — also the target of the antimalarial drug
pyrimethamine. However, resistance to pyrimethamine is common. The
researchers showed that triclosan could act on DHFR even in
pyrimethamine-resistant parasites.
• Berg has turned cancer related drug discovery upside-down.
• BenevolentBio’s knowledge-graphs and ALS story.
• Biggest cache of antibacterials.
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12. Current trends
• Deep Knowledge Analytics, in the landmark Nature Biotechnology
publication of its portfolio company, Insilico Medicine, demonstrated
the design, synthesis and preclinical validation of a novel drug
candidate in just 46 days
• More than 150 well-funded startups providing AI services/products
for drug discovery.
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13. Deepmind’s esoteric but profound story
• DeepMind beat seasoned biologists at predicting the shapes of
proteins. A tool that can accurately model protein structures could
speed up the development of new drugs. There are more possible
protein shapes than there are atoms in the universe. DeepMind did
more than what 50 top labs from around the world could accomplish.
• DeepMind’s simulation doesn’t yet produce the kind of atomic-level
resolution that is important for drug discovery.
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14. More image data than all Hollywood!
• At Recursion, each week robots apply thousands of
potential drugs to various types of diseased cells, in
400,000 to 500,000 miniature experiments that generate
5 to 10 million cellular images. Machine-learning
algorithms then scan the images, searching for
compounds that disrupt disease without harming healthy
cells.
• With Takeda Pharmaceutical Co. Ltd. and Sanofi,
generated more than 2.5 petabytes of data in the past
few years.
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15. AI can boost Clinical trials by:
• Measuring biomarkers
• Identifying and characterizing patient subpopulations best suited. Less than
a third of all phase II compounds advance to phase III, and one in three
phase III trials fail-not because the drug is ineffective or dangerous, but
because the trial lacks enough patients or the right kinds of patients
• Dynamic design or mid path correction.
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16. AI can boost Clinical trials by:
• Measuring biomarkers
• Identifying and characterizing patient subpopulations best suited. Less than
a third of all phase II compounds advance to phase III, and one in three
phase III trials fail-not because the drug is ineffective or dangerous, but
because the trial lacks enough patients or the right kinds of patients
• Dynamic design or mid path correction.
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17. Make clinical trials better for patients by:
• More access to and control over their personal data.
• Coaching via AI-based apps could occur before and during trials.
• AI could monitor individual patients' adherence to protocols continuously
in real time.
• AI techniques could help guide patients to trials of which they may not
have been aware, including efficiently and accurately diagnose, treat and
manage neurological diseases.
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18. Challenges
• Whose IP?
• More need of experimental validation of AI prediction
• Data Privacy.
• Getting the right and large enough data sets, adopting the right AI,
training it in the right way, ensuring there is no bias included or
amplified in the process.
• Global companies dealing with local laws.
• Lack of skills and training.
• Hype. Algorithmic challenges and garbage data.
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19. Future and suggestions
• Collaboration – cross border, dual IP. Utilize strengths of different regions.
• Investment with patience. Investment without knowledge. Silos of experts.
• Government should shift funding to private entities from non-performing
institutes to private sector, especially for valley of death.
• Region specific problems.
• Phyto and oceanic libraries.
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20. Future and suggestions
• The future is hybrid.
• AI part of ecosystem of 4th industrial revolution. Break silos within AI
and also silo of AI.
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Thank you!