Keynote presentation from Pharma AI/IOT conference, March 2020, London

1. How “AI” startups
can and will disrupt drug
discovery & development
Raminderpal Singh
Anduril Limited

2. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 2
Drug development is a cost and time-consuming process that can take more than 12 years and cost about
US$2.6 billion per approved compound.
In practice only about 14% of Phase I drugs reach approval. In oncology specifically, drug attrition rates are
even higher, with only a meager 3.4% of drugs being licensed, which represents a huge financial burden on a
development programme.
Discontinuation of drug development projects during clinical trial is often due to poor efficacy or safety, aspects
that may be tackled by more thorough characterisation earlier in the pipeline utilising appropriate biological
models and target validation approaches.
Although early sifting by proper target and hit validation can have a tremendous positive impact on downstream
success rates, candidates are sometimes fed into downstream development programmes without complete
knowledge of the underlying physiology and even improper assumptions on complex biology.
https://www.ddw-online.com/drug-discovery/p323012-fail-earlyfail-fast-a-
phenotypic-rescue-approach.html

3. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 3
Jackie Hunter
https://pharmaboardroom.com/articles/bridging-the-innovation-gap-with-ai/

4. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 4
AI =
• “dry lab”
• computational algorithms / models
• both “black box” and physical
• which learn through data
• and predict potential truths
+ Technology Innovations
• Compute power, storage costs, cloud
• Algorithmic breakthroughs, machine learning
• Genomics sequencing
+ Lots of VC Money
• Lots of exciting (and less exciting) startups

5. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 5
https://www.lifescienceleader.com/doc/how-biopharma-can-
compete-for-top-tech-skills-0001

6. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 6
https://clevis-research.de/en/the-potential-of-artificial-intelligence-in-
pharmaceutical-drug-development/

7. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 7
https://www.outsourcing-pharma.com/Article/2019/04/16/AI-for-drug-discovery-
will-be-driven-by-biopharma-and-the-rise-of-Asian-tigers-Insilico-CEO

8. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 8
https://www.outsourcing-
pharma.com/Article/2019/04/16/AI-for-drug-
discovery-will-be-driven-by-biopharma-and-the-
rise-of-Asian-tigers-Insilico-CEO

9. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 9
https://www.forbes.com/sites/yiannismouratidis/2018/11/30
/an-insight-of-ais-penetration-in-drug-development-market/

10. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 10

11. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 11

12. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 12

13. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 13
https://www.technologynetworks.com/drug-discovery/news/novel-drug-
candidate-designed-synthesized-and-validated-in-46-days-using-ai-323600

14. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 14

15. Dr Raminderpal Singh | raminderpal@anduril.uk | Copyright ©2020 Anduril Ltd 15
https://www.astrazeneca.com/media-centre/press-releases/2019/astrazeneca-starts-artificial-
intelligence-collaboration-to-accelerate-drug-discovery-30042019.html

16. Computational Antibody Design
Reduced development time and cost
Equipment/materials are costly
in vivo and in vitro, failures lead to wasted time and
money
In silico, failures are easy to bounce back from
Fast time scale simplifies repetition of failed
experiments
Performing large-scale experiments in parallel allows
for a greater probability of success
Easy to “debug”
Biological systems are complicated and messy
Specific experimental conditions are needed
Interference from surrounding molecules/environment
In silico, proteins can be isolated from surrounding
environment
Targeted
Designed with specific goals, not luck!
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17. Computational Antibody Design (con’t)
● Ability to target “undruggable” targets
○ Inaccessible epitopes, alternate states, autoimmune targets
○ In silico design: target can be isolated and designed without interference from the system
● Improved turnaround time for novel targets
○ Eliminate time-consuming lab work to generate candidate antibody sequences
○ Fast in-house scFv testing in an E. coli system
● No humanization necessary
○ Designs are fully human from the start!
17

18. Thank you
Raminderpal Singh
Anduril Limited