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8:00 am Registration and Welcome Coffee

9:00 am Chair’s Opening Remarks

A Reflection of the Previous Year: Implementation and Utilization of Machine Learning in R&D Processes

9:10 am Panel: The Good, the Bad and the Ugly

  • Birgit Schoeberl Global Head of Modeling, Simulation & Pharmacokinetics Sciences, Novartis
  • Tudor Oprea Professor - Medicine & Chief of Translational Informatics Division & Internal Medicine, University of New Mexico
  • Lina Nilsson Senior Director - Data Science Product, Recursion Pharmaceuticals
  • Brandon Allgood Chief Technology Officer, Numerate
  • Alex Aronov Senior Director & Head of Data Science, Data Strategy & Solutions, Vertex Pharmaceuticals

9:50 am Panel: Big Data, Big Deal?

  • Deirdre Olynick ATOM, Director - Business Development & Operations, University of Califorina, San Francisco
  • Sándor Szalma Senior Director - Biomedical Informatics & Global Head of Computational Biology Biomedical Informatics, Takeda
  • Shruthi Bharadwaj Scientist, Novartis
  • Marc Bailly Associate Principal Scientist, Merck

10:30 am Speed Networking – Establish meaningful business connections at a rapid rate

11:00 am Morning Networking Break

11:30 am Speed Learning: Roundtable discussions like you’ve never seen them before

Synopsis

Uncover the story behind four technological advancements from target discovery to phase II clinical trials in one quick-fire session. Each table will be hosted by leading specialist who will share the secrets of their most high-impact strategy; you then get the opportunity to question the host before moving on to your next table.

12:30 pm Lunch Networking Break

Augmentation of Early Stage R&D for Increased Efficiency and Accuracy

1:30 pm Are we there yet? The Use of AI in Target and Drug Discovery

  • Tudor Oprea Professor - Medicine & Chief of Translational Informatics Division & Internal Medicine, University of New Mexico

Synopsis

• Addressing the hype cycle, and current expectations related to deployment of AI/ ML in early drug discovery
• How knowledge-based classification of proteins, combined with exhaustive annotations of drug-target-disease associations can inform and contextualize the decision-making process with respect to target selection
• The importance of in-depth domain expertise with respect to the development, interpretation and deployment of AI/ML models (XGBoost, MetaPath and 17 different types of “big” data) in target discovery for Type 2 Diabetes, Alzheimer’s disease and Autophagy

2:00 pm Discovery Without Data: Novel Target Selection

  • Gerald Higgins M.D. P.hD. Research Professor of Computational Medicine and Bioinformatics, University of Michigan

Synopsis

• Deep learning in pharmacogenomics for novel drug discovery and patient stratification
• How to overcome the data gap and provide the ML programming with the knowledge necessary to make new discoveries

2:30 pm Themed Networking Afternoon Break

Synopsis

Finding the right solution for your specific challenge can be like finding a needle in a haystack – especially when there are a wealth of options available. AI-ML 2020 is the forum to facilitate these conversations and marry problems with solutions. Our themed networking meetings will allow industry and solution providers to find
matches made in heaven.

2:30 pm Poster Session

Synopsis

Join us in the exhibition hall for a browse of our poster submissions – ranging from early stage target discovery to clinical trial design. Don’t forget to grab a coffee and discuss with your colleagues!

3:30 pm Case Study: Cell Line Development and Bioreactor Prediction

  • Huanyu Zhou Senior Director and Head Translational and Non-clinical Statistics , Teva Pharmaceuticals

Synopsis

• Establish which vectors impact anti-bodies and predict how they will affect the final product
• Generate the right biologics at an early stage to scale up to the right protein or antibody for specific patients

4:00 pm Protein Prediction for Small Molecules and Assays

  • Marcin Grotthuss Computational Biologist, The Broad Institute of MIT & Harvard

Synopsis

• Identify the right medication from and the reasons why that medication is the ideal candidate based on genome and pathway analysis
• How to teach your program so the machine can find possible connections with your knowledge as a foundation

4:10 pm Q&A Panel

  • Huanyu Zhou Senior Director and Head Translational and Non-clinical Statistics , Teva Pharmaceuticals
  • Marcin Grotthuss Computational Biologist, The Broad Institute of MIT & Harvard
  • Gerald Higgins M.D. P.hD. Research Professor of Computational Medicine and Bioinformatics, University of Michigan
  • Tudor Oprea Professor - Medicine & Chief of Translational Informatics Division & Internal Medicine, University of New Mexico

5:00 pm Poster Session and Drinks Reception