D4 USA - Data-Driven Drug Development

D4 USA - Data-Driven Drug Development is organized by Front Line Genomics and will be held from Oct 15 - 17, 2019 at Kimpton Marlowe Hotel, Cambridge, Massachusetts, United States of America.

Description:
D4 USA is ram packed full of case studies – opportunities to get into the weeds, learn and be inspired. Broken down into two sections (‘Enabling Science’ and ‘Doing Science’), every session has been carefully screened to ensure that data and evidence is presented to back up the incredible work that’s being pioneered within pharma companies. This is a deep-learning environment.

The format is simple. 150 senior technical and scientific leaders in the same room. This is designed to ensure that you leave the event not just armed with new knowledge, but with a renewed list of contacts who you’ve actually had the time to spend time getting to know. Don’t commit to a 1000 person meeting where you learn nothing and get face-time with no-one for longer than two minutes. D4 USA is a harbour from the storm, where you can spend two highly; productive days in a friendly, welcoming environment where you will meet the right people and develop the right relationships to move your biodata capabilities forward.

Key Benefits of Attending:
• Learn and get inspired from listening to 30 case studies presenting data, evidence, lessons learned, mistakes and insights that will drive your own biodata capabilities forward
• Understand and leverage the fast emerging role of the healthcare data ecosystem in transforming drug discovery and development
• Build your biodata capabilities from the bottom up, from both a technical and scientific standpoint
• Get straight past the hype and vision and into the detail on AI/ML as a tool to build, manage and examine knowledge and vast data sets
• Meet and spend time with other senior-level pharma and biotech leaders from pioneering organizations

This is the most impressive pharma-focused speaker line-up ever assembled for an event on this topic, on a diverse array of subjects tackling THE critical problems in drug development.

D4 Features Data/Evidence Driven Case Studies in the Following Areas:
Learn from use-cases of AI, ML and NLP in drug R&D, and evaluate if the promise has been met by expectations
• Grasp AI & ML’s application throughout the drug R&D cycle, from early drug discovery, preclinical work, clinical phases and drug repurposing
• Hear of the latest developments in AI & ML along with both success and failures in their application, experienced by key players
• See if current NLP technologies have met expectations, where are the successes and challenges
• Discover bleeding-edge semantic technology developments and their impact on advanced analytics for data-driven approaches

Grasp the design and execution of data-driven drug R&D strategies, with practical lessons from major pharma
• Discover the step-by-step process in planning and delivering a business case towards data-driven drug development
• Thoroughly understand the ROI of implementing advanced analytics and IT architectures to your drug R&D, and learn when to apply these IT tools
• Hear of the significance of producing IT solutions and architects that meet end-users’ needs and how to achieve this
• Grasp how to secure budget and management approval to launch your data-driven strategy 

Hear and evaluate the true definition and value of knowledge graphs, and hear about the successes and challenges experienced by healthcare and pharma
• Understand the definition of a knowledge graph and what value this knowledge solution brings 
• Grasp the mechanism to creating knowledge graphs, what kinds of data do you need, how to access them and the processes involved for data visualization
• Discover knowledge graphs comprised of healthcare setting pharmacy data for the contrast of drugs’ effectiveness, and what promise does this hold for pharma?
• See the killer use case of FAIR data for knowledge graphs 

Integrating biodata, from data silos of public and internal domains, to multi-omics data, along with RWD and clinical data
• Hear from all the different aspects of major pharma, solution providers, biotech and healthcare, on how biodata are being integrated and how is value being translated
• Discover the best practices for structured data and data lakes; see from case studies on which approach suits which drug R&D projects
• How to generate higher dimension, multi-omics data and evaluate their ROI
• Hear on the uses of RWD and its complementation to clinical data to optimize drug pipelines and clinical trial population selection

Pre-Conference Day | October 15th
WORKSHOP 1: A crash course in AI for leadership teams in pharma
WORKSHOP 2: An executable roadmap to data-driven drug target identification and drug repurposing that will align with your business strategy