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 30-31 January, 2018
London, UK

Day One
Tuesday 30th January 2018

Day Two
Wednesday 31st January 2018

08.50
Chairman’s Opening Remarks

  • James Weatherall Executive Director & Head of Advanced Analytics Centre, AstraZeneca

Synopsis

Key Takeaways & Lessons Learned from Day One

The Rise of Artificial Intelligence & Machine Learning in Pharma – Optimising the Future

09.00
Improving In-Silico Drug Target Selection

  • Philippe Sanseau Head of Computational Biology & Stats & GSK Senior Fellow, GlaxoSmithKline

Synopsis

  • Overviewing the use of advanced analytics for discovery
  • Using AI in target selection and validation
  • Discussing the data challenges in advanced analytics

09.30
Disrupting Drug Discovery with AI

Synopsis

  • Overviewing the fact that the process of biomedical discovery has not changed for 50 years, and that only a small fraction of globally generated scientific information can form ‘useable’ knowledge
  • Understanding how AI can offer a solution to this problem and how machine learning technology is changing the way new medicines are discovered and developed
  • Analysing how AI, as an augmentation tool to human intelligence, is essential in providing experienced scientists with the analytical tools they need to design better compounds faster

10.00
Morning Refreshments & Networking

10.30
Deep Learning for Drug Discovery: Applying Deep Adversarial Autoencoders for New Molecule Development

Synopsis

  • Introducing deep learning technologies
  • Outlining two strategies for AI drug development
  • Detailing on generative modelling for drug discovery

11.00
Exploring New External Sources with an Open Innovation Interface to Pharma Research

Synopsis

  • Adapting our traditional business models in the pharmaceutical industry – an overview of change pressure in an exponentially changing world
  • Presenting a concrete case example of implementing open innovation, including benefits and ‘costs’ of implementing in pharma research
  • Exploring plug’n play interfaces for new external parties and technologies focusing on explorations and value creation exemplified with AI in drug research

11.30
Leveraging Human Biology to Identify New Targets Backed by a Therapeutic Hypothesis – Building a Recommendation Engine for Targets

  • Eliseo Papa Senior Data Scientist & Open Targets Liaison, Digital Health Tech & Data Sciences, Biogen

Synopsis

  • Reaching fast proof of concept with high probability of success, while looking for new drug targets
  • Using deep learning NLP to quickly detect signals in the literature
  • Leveraging integrated genetics and omics data to screen and rank promising hits
  • Understanding the role of academic/private partnerships such as Open Targets, and how to drive change at the company level

12.00
Lunch & Networking

13.00
Mastermind Session: Supporting the Successful & Efficient Development of Better Drugs with Advanced Analytics

Synopsis

This session facilitates in-depth discussions between participants in an informal environment.

After splitting into small groups, participants will discuss key issues and challenges regarding data science applications in the biopharmaceutical space.

14.00
Taming Large Datasets for Translational Research

  • Paul-Michael Agapow Group Leader – Translational Bioinformatics, Data Science Institute, Imperial College London

Synopsis

  • The genome is not enough: Assessing how translational research requires large data and integrated analysis
  • Understanding that large amounts of data require at least assisted discovery and preferably auto-discovery
  • Analysing how contextual information is required to usefully prioritise result
  • Evaluating deep learning and knowledge mining as promising leads in this direction

14.30
Afternoon Refreshments & Networking

15.00
Predictions from Data Science: Drug repurposing, Combinations & Detection of Patient Populations

Synopsis

  • Leveraging data analytics (high scale and big data) for research and clinical data – text and semantic mining
  • Tackling drug repositioning with enhanced analytical capabilities
  • Covering unmet needs in medicine and the pharmacological potential of drugs
  • Increasing the value of assets by developing new indications and/or new drug combinations
  • Analysing how translational bioinformatics and data science can support drug development to establish the molecular and scientific rational

15.30
Panel Discussion: Transforming Pharma with Machine Learning & AI

Synopsis

  • What really is AI? – Analysing how artificial intelligence is changing biopharmaceutical research
  • Buzz word or valuable in strategic decision-making in the long run? – How AI and machine learning can optimise and accelerate drug development
  • Are regulators ready for AI in pharma?
  • Addressing the future promise of these advanced deep learning methodologies to support the bringing to market of more efficient, safe therapies

16.15
Chairman’s Closing Remarks

  • James Weatherall Executive Director & Head of Advanced Analytics Centre, AstraZeneca