February 27-28, 2019
San Francisco

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Day One
Wednesday 27th February, 2019

Day Two
Thursday 28th February, 2019

08.00
Coffee & Registration

08.30
Chair’s Opening Remarks

08.40
A.I. and Precision Medicine: Furthering Scientific Understanding of Complex Disease

Synopsis

  • Novel feature selection strategies for enhancing causal statistical machine learning in the biomedical sciences.
  • Ensemble Computational Intelligence
  • Deep Learning
  • Probabilistic Programming

Applied AI in Drug Discovery: A Look at the Leading Case Studies; Where AI Has Had a Transformative Impact to Date?

Synopsis

The need for a paradigm shift in the drug discovery process isn’t a well-kept secret and is clearly evidenced by the time taken for a drug to reach final approval and the otherwise unsustainably high failure rates. What is also well known, is the potential that AI has to step up to that challenge and provide the transformative power required to streamline the process, discover new targets, generate new drugs and much more. This session will take a look at the progress that AI has made so far in pharma drug discovery in practical case studies, and will set the tone for where the successes have been so far, and the case studies that inform the areas where improvement is necessary. Importantly, this session will highlight what is still needed to realize the next wave of applications of AI and machine learning in drug discovery.

09.10
Projects Colossus and Enigma: The Use of AI Methods in Early Drug Discovery and Late Drug Development

Synopsis

  • The historical repository of clinical trial data represents some of the costliest
    and important data in pharmaceutical development. Clinical trial data is often
    analyzed by function, bio marker, safety, pharmacokinetic etc
  • Hear about the development of the Colossus project at Genentech; an extensible
    framework integrating clinical, pathology, imaging, RNA and genomic data. Using
    this data, we are able to build models to predict responders and non responders,
    clinical trajectory and adverse event likelihood
  • Using this we were able to identify a new responder subpopulation, which was
    replicated across trials and indications
  • The enigma project uses deep learning methods in the discovery and formulation
    of novel small molecules
  • Deep learning and generative methods have produced models with greater
    predictive power and robust characteristics than previous methods

09.40
Integrating Machine Learning into the Drug Discovery Workflow

  • Pat Walters Computation & Informatics group, Relay Therapeutics

10.10
Harnessing the Power of AI to Improve Translation: From Biology to Discovery & Discovery to Drug Product Development

10.40
Speed Networking

11.00
Morning Refreshments

Clarity in Place of Complexity: How to Best Structure Datasets to Optimize the Insights That Will Be Garnered from AI & Machine Learning Technologies

Synopsis

The necessity to restructure existing drug discovery data, as well as order the freshly generated data is paramount. To have abundant and structured data is the prerequisite to the insights gained from powerful algorithms. This session will look at some of these issues around breaking down the traditional data silos in pharma drug discovery. It will focus on strategies to best structure datasets to ensure that algorithms can gain the best possible insights and make the best possible predictions.

11.30
Starting from Scratch: Data Driven AI in Target Discovery

  • Slava Akmaev Senior Vice President, Chief Analytics Officer, Berg

12.00
Insights from Machine Learning in Support of Reverse Translation

12.30
Networking Lunch

12.30
Reinventing Drug Discovery with AI

14.00
Implementation of Deep Learning Based ADME Predication in Small Molecule Drug Discovery Pipeline

14.30
AI Empowered Drug Discovery

14.45
The Practical Impact of AI in Drug Discovery So Far

  • Friedrich Rippmann Director, Computational Chemistry & Biology, Global Research & Development | Discovery Technologies, Merck

15.15
Afternoon Refreshments

16.00
AI Breakout Roundtables

Synopsis

Whilst the value in adopting AI technologies throughout drug discovery is now well known and accepted in the pharmaceutical industry, there is still a lot to be analyzed and discussed. from where the technology can be beneficially applied in drug discovery, to how people departments, and data can be ordered and structured to allow for the utmost value to be attained in the shortest time possible. With a lot to be learned and common challenges to be shared, these interactive and discussion based sessions are where potential solutions and ideas can be shared.

17.00
Chair’s Closing Remarks

17.10
Close of Day 1