Data, Evidence and Case Studies from the World’s Leading Minds in Pharma

The concept of D4 is simple.

Deliver 30+ pharma-led case studies outlining data and evidence that will allow attendees to gather intelligence and make strides forward in their data-driven discovery and development approaches. D4 Pharma is a ‘best-in-class’ event, with an unrivalled quality of speakers, attendees and discussions.

Put together by pharma, for pharma. This event is the result of 8 Special Interest Groups, designed by people from every big pharma company in the top 20, and others, to identify priorities, challenges and solutions, and construct a conference that moves beyond ‘what if’ and straight to ‘how’ and ‘now’.

Bring together enablers and doers. D4 brings together IT/data professionals (those building and improving systems/technology – ENABLERS) together with scientists using data to deliver better results (DOERS). The needs and priorities of both groups are different, so D4 has separate tracks for each. However, they also need to understand each other better, so D4 brings the whole group back together at critical points in the meeting.

Deliver near term results from AI/ML. Low productivity has become the norm, and development success is dropping. We need changes and results now. With dozens of conferences offering to exhibit the potential and hope of AI and ML, D4 will move beyond air-headed and vacuous discussions – showcasing the evidence, data and case studies of pioneering technologies and approaches.

A fabulous opportunity to understand what other pharma/biotech companies are doing to accelerate drug development and connect with like-minded colleagues from a well-balanced range of industries.
— Bino John, Associate Director, AstraZeneca

Pioneering Speakers Include

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Nuray Yurt
Executive Director, Enterprise Analytics & Data Science

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John Quackenbush
Chair of the Biostatistics Department
Harvard T.H. Chan School of Public Health

Frank Lee
Global Healthcare and Life Sciences Industry Leader
IBM Systems

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Linda Moineau
IT Director
The Broad Institute of MIT and Harvard

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Elizabeth George
Director, Digital Clinical Trials

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Jennifer Hall
Chief, Institute for Precision Cardiovascular Medicine
American Heart Association

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Peter V Henstock
Machine Learning & AI Technical Lead: Combine AI, Software Engineering, Statistics & Visualization

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Tom Plasterer
Director of Bioinformatics, Data Science & AI, Bio Pharmaceuticals R&D

Bino John
Associate Director

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Philip C Ross
Director, Head of Translational Bioinformatics Data Science
Bristol-Myers Squibb

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Deborah McGuinness
Tetherless World Constellation Chair, Professor of Computer and Cognitive Science, Chair
Rensselaer Polytechnic Institute
and University Researcher

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Sridevi Ponduru
Associated Medical Director

I made more new contacts at the D4 conference than at any conference over the past couple of years.
— Peter Henstock, Senior Data Scientist, Software Engineering, Statistics and Visualization, Pfizer

Why Attend


Learn and Gather Data

30+ case studies presented by leading pharma, biotech and academics. Understand the evidence, lessons learned and challenges being faced today in drug development.


Shape Your R&D Capabilities

Listen and participate in thought-provoking and evidence-based presentations, discussion panels and innovation showcase sessions.


Get Straight to the Information Most Relevant to You

Through two tracks aimed at 1) IT/data leaders in pharma and 2) Scientific leaders adopting these technologies.


Go Beyond the Hype and Promises of AI and ML

Get into the detail of how to build, manage and examine vast data sets.

Meet and Spend Time

Get face-time with other senior-level pharma and biotech leaders from pioneering/leading organizations.

’A great opportunity to get inside the minds of leading thinkers in leveraging data science and IT to create new drugs.
— Steve Hoang, Head of Computational Biology, Hemoshear Therapeutics

D4 Features Data/Evidence Driven Case Studies in the Following Areas:

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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

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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

A great blend of topics around data-driven drug discovery bringing people from different domains close to each other.
— Martin Romacker, Principal Scientist, Roche