8:55 am Chair’s Opening Remarks

Transforming Translational Research with Predictive Analytics and Augmented Intelligence

9:00 am Case Study: Biomarker Identification

  • Liling Warren Head, Director of Translational Statistics, Teva Pharmaceuticals


• Consider the benefits of medical imaging at the preclinical stage as an alternative to traditional biomarkers
• Push biomarker identification to preclinical stages and utilize the data produced to refine them for the

9:30 am Case Study: In Vivo and In Vitro Model Selection


• Predict the best characteristics for in vivo and in vitro models through analysis of previous reactions to similar compounds

10:00 am A Scalable Platform for Generative Lead Optimization of De Novo Molecules


• Discovery of Potent, Selective Aurora Kinase Inhibitors with Favorable Secondary Pharmacology
• Generative networks for optimization of chemical structure in high performance compute workflows

10:30 am Q&A Panel

  • Andrew Weber Research Director, ATOM Consortium
  • Liling Warren Head, Director of Translational Statistics, Teva Pharmaceuticals

11:00 am Morning Networking Break

11:30 am Wrap Up Roundtables: 10 tables, 10 problems, 10 solutions. Ready, set, resolve!


• Design a plan to source and identify new helpful data sets
• Create a data management system
• Design a business plan for implementation of AI/ML technologies across the pharma value chain
• Create a model for the identification of novel targets
• Create a model for the accurate and efficient identification of targets
• Design a process to identify biomarkers at the preclinical stage for use in clinical trials
• Augment the selection of in-vitro and in-vivo models
• Design a ML system for phase I clinical trials
• Translate current ML systems to application in phase II and III clinical trials
• Translate current ML systems to applications using RWE available

12:30 pm Lunch Break

Journey to the Future: From Faster Processes to Personalized and Predictive Medicine

1:30 pm Panel: Know the Present, Predict the Future


• Augment processes with machine learning utilizing the research, preclinical and clinical data available
• Consider the potential of personalized medicine and the technology required to make early predictions of the
“ideal” patient for both clinical trial and treatment

2:10 pm Case Study: AI and Machine Learning Approaches in Clinical Trials


• Utilize AI and machine learning approaches to mine and find clinically significant insights in increasing clinical trial data
• Tackle the “big data” issues faced by pharma – find and implement the best solutions for clinical trial data

2:40 pm Case Study: Machine Learning Model to Identify Undiagnosed Individuals with a Specific Disease or Condition


• Accelerate early diagnosis and timely intervention for individuals with familial hypercholesterolaemia by applying machine learning to large health-care encounter datasets
• Increase early diagnosis and intervention for “silent” disease

3:10 pm Q&A Panel

3:30 pm Chair’s Closing Remarks

3:40 pm Networking Coffee to Stay or Go

4:00 pm End of Summit