Tuesday 27th February, 2018
08.00 Coffee & Registration
09.00 Chair’s Opening Remarks
Applied AI in Drug Discovery: A Look at the Leading Case Studies; Where AI Has Had a Transformative Impact to Date?
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.
09.40 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
- Brandon Allgood CTO, Numerate
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
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
- Sandor Szalma Global Head of Computational Biology, Takeda
12.30 Networking Lunch
12.30 Reinventing Drug Discovery with AI
- David Wild Founder & President,, Data2Discovery
14.00 Implementation of Deep Learning Based ADME Predication in Small Molecule Drug Discovery Pipeline
- Lei Jia Senior Scientist, Amgen
14.30 Machine Learning Descriptors for Epitope Mapping and Affinity Prediction: A New Frontier in Structural Analysis
- Rafeal Depetris Principal Scientist, Kadmon Corporation
15.00 The Practical Impact of AI in Drug Discovery So Far
- Friedrich Rippmann Director, Computational Chemistry & Biology, Global Research & Development | Discovery Technologies, Merck
15.30 Afternoon Refreshments
16.00 AI Breakout Roundtables
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.