Day One
Wednesday July 26, 2017

Day Two

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Thursday July 27, 2017

Breakfast & Networking

Chair’s Opening Remarks

Beyond the Hypothetical: Real World Impact of AI Implementation in R&D

Discover: Massive Parallelization of Rare Disease Drug Discovery

  • Ron Alfa Vice President, Discovery & Product , Recursion Pharmaceuticals


  • Leveraging deep learning on images to augment high content screening
  • AI-enabled unbiased phenotypic screens on loss-of-function disease genes
  • Case studies of discoveries made using these methods

Discover: AI Opportunities in R&D

  • Giancarlo Crocetti Associate Director, IT-RDM Translational Medicine, Boehringer Ingelheim


  • Understanding current data mining efforts in R&D
  • Promising AI approach that will shorten or improve drug develop
  • Creating new opportunities in the identification of new therapies

Discover: Showcasing the Utility of AI in Early Stages of R&D to Prove This Disruptive Technology’s Validity


  • Developing validated methods to showcase applicability of AI platforms
  • Building a “disease-profile” and harnessing multi-object optimization tools to design best candidates
  • Matching AI performance to human decision making to demonstrate the utility of AI platforms

Morning Refreshments

Harnessing Differential Data-Centric & Knowledge Driven Approaches for R&D Innovation

Discover: Making Big Data Useful: Practical Approaches

  • Hal Stern Executive Director, Applied Technology, Merck


  • Just how big is “big data” and how do we define its dimensions of utility?
  • How do we combine machine learning, visualization and non-traditional solvers?
  • What are the new risks and threat surfaces created?

Discover: Cognitive Computing Technology Pilot

  • Jun Dong Director, Global Regulatory Affairs, Development , & Information Technology Services, Sanofi


  • Today, developing regulatory intelligence from internal and external available regulatory information is very time-consuming and resource-demanding
  • The pilot is to assess current cognitive computing (machine intelligence) technology feasibility and maturity to replace (even partially) the manual process to provide RI answers to ad-hoc requests
  • Discussing and evaluating lessons learned

Discover: The Low Hanging Fruit that No One Seems to See – Yet: Using AI to Dig Through Your Haystacks of Safety Data

  • Shaun Comfort Associate Director, Senior Safety Science Leader, Genentech


  • Collecting and uniting siloed safety data
  • Harnessing machine learning to unlock valuable insight into drug candidate safety data

Networking Lunch

Discover: Harnessing AI to Make Sense of Large, Complex Datasets and Re-Invigorate R&D Innovation

  • Mark Gerstein Professor, Biomedical Informatics, Molecular Biophysics, , Biochemistry & Computer Science, Yale University


  • Discussing how we can utilize AI to reduce cost, reduce timelines and improve the value of “big data” mining
  • Understanding machine learning techniques to help manage “big data”
  • Improving data integrity and security in a cloud based, AI strategy

Discuss: Marrying Machine Learning Platforms with R&D Data to Streamline the Application of AI Technology

  • Jun Dong Director, Global Regulatory Affairs, Development , & Information Technology Services, Sanofi
  • Shahar Keinan CSO, Cloud Pharmaceuticals


  • Selecting the right data: what data for which applications?
  • Discussing data sharing and ownership
  • Investigating liability concerns around data sharing and the competitiveness of information derived from AI algorithmic approaches
  • How can we work towards a unified solution that can generalize for multiple types of data?
  • What does the perfect work flow and technology stack look like for pharma-centric AI application?
  • Understanding the computing resources needed to effectively action AI for big data sets
  • Discussing blockchain technology, it’s value, implementation and intersection with AI and data

Analyzing Collaboration, Commerciality & Future Directions to Advance the Integration of AI in Pharma

Discuss: What’s the Future for AI Application and Collaboration and What Can we do Now to Maximize the Chance of Success?

  • Shaun Comfort Associate Director, Senior Safety Science Leader, Genentech
  • Richard Barker Founding Director , Centre for the Advancement of Sustainable Medical Innovation (CASMI)
  • Ed Addison CEO, Cloud Pharmaceuticals


  • What does the partnering paradigm look like for AI and pharma companies?
  • What does long term value and ROI look like to all stakeholders involved?
  • What are the requirements for each stakeholder entering an R&D agreement?
  • What are the future questions we want to be asking now of our data and where must we improve AI technology to be able to answer them?
  • Replacing humans with machines: What does the future application of AI technology look like?
  • An eye on the end goal: Discussing the impact of AI on patient healthcare

Debate: Developing the Perfect Pharma-Tech Collaboration to Create Fertile Soils for Future Growth of the Industry


  • The do’s and don’ts of future pharma-tech collaboration
  • Debating how AI can facilitate a wider discussion around cross-industry collaboration to improve global R&D efforts
  • Developing trust and transparency in the pharma industry to advance the collaborative application of AI

Chair’s Closing Remarks

Close of Day Two & End of Inaugural AI Pharma Innovation Summit 2017


Afternoon refreshments will be available for any final networking opportunities