D4 Europe is packed with case studies, evidence and intelligence that you can take back to your organisation and impact your drug discovery and development programs immediately.
Here’s just a snapshot of what you’ll take away after attending the meeting:
- Dramatically improve your data-guided translational research by learning from AstraZeneca which data R&D data approaches are delivering results, and which are not. Improve your understanding of the basic economics of drug R&D, and get a detailed outline of the problems and approaches in translational research that pharma should be focusing on.
- Get a patient perspective on whether new data-driven approaches are improving the patient journey and quality of life. Learn which changes you can make to improve the harmony between drug R&D and the patient experience, and rediscover the role of patient groups in a fast-changing pharma ecosystem.
- Learn how Bayer free up their R&D data scientist’s time to focus on data analysis. Typically, over 80% of time is spent on cleaning, managing and preparing data for analysis, and 20% is spent on analysis. Understand how to deploy new platforms and approaches to change that situation and super-charge your R&D.
- Move beyond FAIR implementation by learning how to deploy a new Capability Maturity Model (CMM) for supporting transformation. Guide your FAIRification processes, measure outcomes and identify the foundational elements you need to build up your FAIR capabilities.
- Inspire and arm your workforce with data capabilities, by hearing from Ferring Pharmaceuticals on the practical implementation of data strategies by middle management.
Design educational and training processes to adapt your workforce, build an effective ‘data template’ and hear about the pros and cons of adapting a universal data vocabulary for your workforce.
- Get the latest best practice from Roche for integrating scale and the use of multi-omics data. Learn how to better structure and extract scientific context from data, ensuring your data has value and which platforms are the best fitfor-purpose.
- Hear from GSK how to achieve true value in drug discovery from big data. Overcome historic silos and old ways of ‘piecemeal’ data preparation and analysis, and understand how to use new data flows, data FAIRness and integration to enable powerful data mining, ML and AI along with efficiency improvements.
- Apply the lessons learned from Roche in implementing automated knowledge management methods. Learn how to use various ML methods to pass the necessary threshold of 75% accuracy, and understand the limitations and opportunities for the future in “semantic computing”.
- Leverage genetics and advanced analytics for target discovery and prioritization. Hear from GSK on how current generation and manipulation of diverse omics data at scale is driving forwards target identification and validation, including best practice for multi-omics data integration and analysis.
- Get the latest from a cutting-edge project to use largescale network analysis for drug discovery. Using a case study involving the determination of sequence space architectures of antibody repertories, get ideas on how to incorporate the principles of the project to your own drug discovery programs.
- Transform your use of ML and related approaches to optimize drug design, by hearing two incredible case studies from Novo Nordisk and Boehringer Ingelheim. Optimize ML-guided drug design through better model selection, use of training data and validation methods.
- Hear from Sanofi on how they’re building simulated clinical trials using synthetic control arms and virtual trials. Get the very latest on digital innovations making an impact, demonstrating ROI and best practices for the design and implementation of digital strategies.
- Learn from Bayer’s navigation guide for collaboration and formation of IT partnerships. Understand when and where in drug R&D collaboration is beneficial, discover available collaboration initiatives and build a picture of the ROI achieved from such partnerships.
- Dive deep into the world of artificial neural networks (ANNs). Leverage convoluted neural network modelling to empower your drug R&D, get an overview of how ANNs are being used to extract clinical insights and learn how to apply ANNs across late and early stage clinical programs.
- Understand from GSK how to apply narrow, deep and accurate imaging data to drug discovery at scale. Applying recent advances in AI to computer vision is expected to be a crucial step in increasing the rate of drug discover at GSK. Hear why and how they are aiming to provide access to imaging data at scale to R&D stakeholders.
- Hear how Novartis are making and executing better datainformed decisions for their drug pipeline. Hitting project kill-switches early and prioritizing drug leads properly has become essential in the drive towards more efficient and effective drug discovery and development. Discover how Novartis use advanced analytics to streamline their R&D strategy, terminate projects and validate insights.