Brandon Allgood discusses how AI can be implemented in key areas of failure along the pharmaceutical R&D pipeline. He notes that while AI shows promise in areas like target identification, hit identification, lead optimization, and clinical trial design, there are also challenges to address like limited labeled data, complex biological processes, and ensuring AI models are properly validated. Allgood concludes that life scientists and computer scientists need to work together to develop domain-specific AI approaches, and that the culture and use of AI in drug discovery cannot mirror existing cheminformatics tools if its potential is to be fully realized.

1. Brandon Allgood, PhD
Chief Technology Officer
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How AI can be implemented in
Key Areas of Failure along the
R&D Pipeline

2. Why now?
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Source: ANDREESSEN HOROWITZ

3. AI in the hype cycle
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We tend to overestimate the effect of a technology in the short run
and underestimate the effect in the long run – Amara’s Law

4. What is AI?
ML systems are function (model) generators
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AI
General AI Narrow AI
Machine
Learning
Unsupervised
Learning
Clustering
Anomaly
detection
PCA
Supervised
Learning
Linear
regression
SVM
Random
Forest
Deep
Learning
Reasoning
Systems
…
…
…
Expert
Systems

5. Areas of largest impact
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6. Common characteristics
Data is cheap (there is a lot of labeled data)
Training sets look a lot like space being predicted
Training set features are obvious
Very constrained environment
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7. Data in pharma
Data is expensive (small amounts of labeled data)
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8. Data in pharma
Data is expensive (small amounts of labeled data)
Data is imbalanced (very few actives)
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Active
Inactive
Healthy
Sick

9. Data in pharma
Data is expensive (small amounts of labeled data)
Data is imbalanced (very few actives)
Models are often used to extrapolate, not interpolate
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10. Data in pharma
Data is expensive (small amounts of labeled data)
Data is imbalanced (very few actives)
Models are often used to extrapolate, not interpolate
Biological processes are not fully understood
Right features are unclear
Avoid assumptions about the biology
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11. Data in pharma
Data is expensive (small amounts of labeled data)
Data is imbalanced (very few actives)
Models are often used to extrapolate, not interpolate
Biological processes are not fully understood
Right features are unclear
Avoid assumptions about the biology
Processes and phenomena span orders of magnitude
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12. Data in pharma
Data is expensive (small amounts of labeled data)
Data is imbalanced (very few actives)
Models are often used to extrapolate, not interpolate
Biological processes are not fully understood
Right features are unclear
Avoid assumptions about the biology
Processes and phenomena span orders of magnitude
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Need to develop domain specific approaches

13. Enabling data-­driven research
Availability and discoverability over organization
Proper structure is hard to anticipate
Organization will arise, if the data is accessible
Storage is cheap, keep many copies
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14. Enabling data-­driven research
Availability and discoverability over organization
Proper structure is hard to anticipate
Organization will arise, if the data is accessible
Storage is cheap, keep many copies
Co-­locate the data with the compute
Data gravity – Moving data is now often the bottle neck
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15. Enabling data-­driven research
Availability and discoverability over organization
Proper structure is hard to anticipate
Organization will arise, if the data is accessible
Storage is cheap, keep many copies
Co-­locate the data with the compute
Data gravity – Moving data is now often the bottle neck
Compute should ideally be unlimited and elastic
Use the public cloud!
Security posture doesn’t need to be compromised, a new skill
set is required
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16. Data sharing
To assume the AI revolution in pharma will happen in
your organization is not realistic
Data needs to reach those that can make the next
breakthroughs in algorithm design
It is time to start sharing data through data partnerships
ATOM collaboration (GSK, UCSF, NCI, LLNL, others)
To be announced: European partnership including J&J
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Data’s value lies in the insights extract from it

17. Not your grandma’s CADD
CADD tools help scientists build mental models
AI can ingest more data & build more complex models
AI models are however narrowly focused and require a
human/AI partnership to be successful
Med-­chemists can now focus on creating enriched spaces
Model predictions need to be double checked
Must remain vigilant to avoid imparting bias
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Deploying AI systems in the same way as CADD
will highly limit their effectiveness

18. Strengths of AI
Ingest all available data & build more complex models
Models can be deployed at scale to evaluate orders of
magnitude more options
Models can be continually retrained as new data
becomes available
AI systems don’t sleep
AI is naïve (of chemistry, biology, psychology, etc)
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19. Weaknesses of AI
Still need experts to build AI models
Need some data to start
Still narrow, so training & output need to be scrutinized
AI models can be black boxes
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These are all addressable in the long term

20. Impact on pharma R&D
Omics analysis
Literature connections
Example: Open Targets
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Target ID Discovery Pre-­clinical Clinical Beyond
Source: Enrico Ferrero, PhD, Associate GSK Fellow

21. Impact on pharma R&D
Hit ID
Numerate platform
AI models built with as few as 8 active compounds
Identify compounds from libraries in novel scaffolds (30%-­70% hit rate)
Screen 10M purchasables < 1 hour or 128M synthesizables < 1 day
Including active learning à predicts most informative compounds
Lead ID & optimization
Numerate platform
Includes all available data from all available source
Search and rank ~10B novel compound libraries in ~2 weeks
Drive efficacy in fewer design cycles
Include any number of off-­target or ADME models of concern
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Target ID Discovery Pre-­clinical Clinical Beyond

22. Impact on pharma R&D
ADME / toxicity
Numerate platform
ADME suite – Includes models for all standard preclinical in vitro
assays, including, stability, permeability, and protein binding
ToxTool – Includes over 6,600 MoA models and 4,000 GO based meta-­
models (including pathway context)
Other work: DL applied to the Merck Kaggle challenge
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Target ID Discovery Pre-­clinical Clinical Beyond

23. Impact on pharma R&D
Trial design
Biomarker development
Cohort selection
Patient recruitment
Patient compliance
Smart data collection
Anomaly detection
Basic analytics
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Target ID Discovery Pre-­clinical Clinical Beyond

24. Impact on pharma R&D
Repurposing/Repositioning
Drug-­Omics analysis
Literature analysis
Patients
Preventative care
Personalized medicine
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Target ID Discovery Pre-­clinical Clinical Beyond

25. Strategic Value of AI in pharma R&D
Faster drug discovery
Increased safety
Lower failure rates
Faster regulatory approval
Speed to market
Cost efficiency
Improved patient engagement
Better treatment outcomes
New growth opportunities
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Target ID Discovery Pre-­clinical Clinical Beyond

26. Conclusions
Believe the hype, just don’t get ahead of yourself
Data needs to be made more available
Life scientists and computer scientists need to come
together to develop domain specific methods
The culture and approach to using AI cannot be the
same as to CADD tools
Need to be robust, beyond the hype
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27. Fourth industrial revolution?
Time to begin retraining
The ability to work with AI and understand its capability &
potential needs to be pervasive
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