AI in Pharma
December 4-5, 2018 | Download Brochure
Investment and the application of AI in the pharmaceutical industry is rapidly gaining momentum. We bring together CEOs, CIOs, CTOs and Global AI, IT and Informatics Experts from leading pharmaceutical and technology companies to give strategic and visionary
talks based on use cases throughout the drug development pipeline.
Peter Henstock, PhD, AI & Machine Learning Lead, Pfizer
1:15 ProtaBank: AI and Machine Learning for Protein Engineering. Where's the Data?
Barry Olafson, PhD, CEO, Protabit LLC
1:40 The Potential for Advanced Analytics in the Pharmaceutical Industry
Michael Ringel, PhD, Senior Partner and Managing Director, Boston Consulting Group (BCG)
Advanced analytics, including artificial intelligence, have the potential to improve any decision. In pharma, biopharma and healthcare more broadly, this offers the promise of a major improvement in cost and effectiveness, given that an enormous portion of current spend in both domains ends up as waste. In this overview presentation, we set up the opportunities, challenges and reasons for optimism.
2:00 CO-PRESENTATION: Artificial Intelligence Accelerants in Oncology Informatics
Tom Plasterer, PhD, US Cross-Science Director, R&D Information, AstraZeneca
Jonathan Dry, PhD, Director of Bioinformatics, Oncology, AstraZeneca
Data and information flows in oncology research provide an ideal opportunity to evaluate emerging applications in Artificial Intelligence. While some bioinformatics and next-generation sequencing workflows are becoming standardized and can provide a well-described input dataset for AI applications, other require extensive data preparation and stewardship prior to exploitation. Applications for both well-structured, well-stewarded and poorly-structured data will be described.
2:40 Why Is the Pharmaceutical Industry Finding AI so Difficult?
Peter Henstock, AI & Machine Learning Lead, Pfizer
The rise of AI has dramatically changed the course of many industries as they have been able to adopt this range of new technologies. Although pharmaceutical companies regularly incorporate technology innovations, the transformation has arguably been much slower. This presentation will outline some of the challenges, highlight some successful examples, and describe some of the strategies we are using to drive AI across the organization.
2:40 Data Store: Making all Data Machine Learnable at NIBR
John R. Walker, Head of Core Data and Analytics, NIBR Informatics, Novartis
NIBR invests billions each year in generating scientific data. The Data Store is the central platform to find, store and process these data assets. This presentation will describe the challenges, strategy and implementation details for this new platform at Novartis.
3:00 Networking Break in the Exhibit Hall with Poster Viewing
3:45 Integrating AI Workflows in Drug Discovery
John Baldoni, Senior Vice President, In silico Drug Discovery, GlaxoSmithKline
GSK formed an In-Silico Drug Discovery Unit to explore how AI/ML/DL methods can affect the drug discovery process in multiple dimensions. The unit takes advantage of existing internal capability and integrates subject area expertise from external pharma-focused companies and non-pharma sectors to improve the effectiveness of drug discovery. The goal of the unit is to flexibly use and integrate these platform capabilities in its work to discovery a drug. The talk will describe the enabling activities that have emerged outside of the company and those within the company to enable this approach, the business model that is forming and successes and failures to date.
4:05 The Big Data Revolution: Can AI Shape Drug Discovery?
Litao Zhang, PhD, Vice President, Leads Discovery & Optimization, Disease Sciences & Biologics, BMS
This talk will present artificial intelligence paradigm shift due to the explosion of data: “big data”. Is Artificial Intelligence maturing to solve our challenges in drug discovery? How can the big data make the meaningful impact on drug discovery? What are the hurdles to transfer big data into knowledge to guide our innovation? We will use lessons learned to engage our dialogs with the audience.
4:25 INTERACTIVE PANEL DISCUSSION: The Use of AI to Disrupt Drug Discovery: How to Reduce Time and Costs and Increase Throughput
- Where can Deep Learning show benefits in R&D?
- Algorithms: Data distribution vs. outcome prediction
- Critical need in DL for transparency and solution interpretability
- Deep learning works great on image data. What about molecular data which is sparse, dirty, inconsistent? Drug Discovery must be accurate on outliers and deep learning is not so great here. What do we do?
- Application of machine learning to toxicity and Pk
- Application of machine learning to reduce computational time of computational chemistry
- Augmented intelligence
- Target discovery and lead discovery require different methods
- How can ML/AI approaches be used to speed up the drug discovery process: Target ID, lead optimization and clinical development
- Do we have access to the right data, suitable for state of the art ML/AI approaches?
- How can we recruit the best given the high level of competition across multiple sectors?
Alex Zhavoronkov, Founder, Insilico Medicine
Mark Davies, Vice President Biomedical Informatics, Benevolent AI
Jim Brase, Chief Technical Officer & Interim Co-Lead, ATOM and Deputy Associate Director for Computation, Lawrence Livermore National Laboratory
Slava Akmaev, PhD, Senior VP & Chief Analytics Officer at BERG
Eric Neumann, CEO & Founder, Aidaka
Ed Addison, CEO, Cloud Pharmaceuticals
Dany De Grave, Senior Director, Innovation Programs and External Networks, Sanofi
2:20 Pharma Platinum Sponsor Presentation (Opportunity Available)
2:25 Strategy and Application of AI in Closing the Loop from Clinical Trials to Discovery Biology
Carolyn Cho, PhD, Director, Immunology Therapeutic Area Pharmacometrics Lead, Merck
Systems immunology is a powerful approach to understanding complex pathophysiology in immunological disorders and in vaccine response. However, the identification of testable causal hypotheses has been widely recognized as one of the most significant challenges of this approach. Causal modelling of data from a previous “bioage” clinical study (Fourati et al., Nat Comm 7:10369, 2016) is being applied to identify hypothesized mechanisms of vaccine hyporesponse in the elderly.
2:45 Generating a Business Case for Innovation: How Can AI Enable the Future of Clinical Trials?
Basker Gummadi, Digital Innovation Lead, Bayer
How can driving innovation enable and achieve business objectives? This talk will highlight business cases within clinical trials that can be achieved by AI, Robotic Process Automation and Blockchain.
3:05 Networking Break in the Exhibit Hall with Poster Viewing
3:40 INTERACTIVE PANEL DISCUSSION: Can AI & ML Make Clinical Trials Faster, More Effective and Patient Centric?
- AI: The tangible value beyond the hype
- Applying AI frameworks to improve trial outcomes
- Leveraging data for machine learning projects
- Keeping the focus on the patients
- Knowledge engineering (AI, ML, and DL) in pharma R&D
- New ethical considerations for the life sciences industry
- What is the future for consent and permissions? How granular should this be when patients control their data?
- How we feed the machine is as important as the machine itself
- How is the role of the health tech ethicist different? How can companies build that capacity?
Craig Lipset, Head of Clinical Innovation, Pfizer
Ted Slater, Global Head, Scientific AI & Analytics, Cray
Balazs Flink, MD, Clinical Trial Analytics Lead, R&D Business Insights and Analytics, Bristol-Myers Squibb
Chris Bouton, PhD, CEO, Vyasa
Sara Holoubek, CEO, Luminary Labs
4:20 Exploring New Ways of Working in Regulatory: Sanofi’s Proof of Concept Pilot
Dany De Grave, Senior Director, Innovation Programs and External Networks, Sanofi
At Sanofi we defined AI for the Regulatory Space as “The ability of an intelligent system to quickly integrate vast amounts of diverse, complex and unstructured regulatory-pertinent information transforming it into actionable insights for strategy planning and decision making throughout the product lifecycle. It allows iterations at an unprecedented scale in human-like terms and provides “logic” for insights provided.” We will share lessons learned from a Proof of Concept pilot.
DIGITAL USE CASES
4:40 Implementing Digital and AI Strategies Across Organizations within Sanofi
Natalija Jovanovic, PhD, Head Digital Catalyst, Sanofi
5:00 Close of AI World 2018